#how to update python in pyspark

[mysticpandakid]

PySpark SQL: Introduction & Basic Queries 

Introduction 

In today’s data-driven world, the volume and variety of data have exploded. Traditional tools often struggle to process and analyze massive datasets efficiently. That’s where Apache Spark comes into the picture — a lightning-fast, unified analytics engine for big data processing. 

For Python developers, PySpark — the Python API for Apache Spark — offers an intuitive way to work with Spark. Among its powerful modules, PySpark SQL stands out. It enables you to query structured data using SQL syntax or DataFrame operations. This hybrid capability makes it easy to blend the power of Spark with the familiarity of SQL. 

In this blog, we'll explore what PySpark SQL is, why it’s so useful, how to set it up, and cover the most essential SQL queries with examples — perfect for beginners diving into big data with Python. 

Agenda 

Here's what we'll cover: 

What is PySpark SQL? 

Why should you use PySpark SQL? 

Installing and setting up PySpark 

Basic SQL queries in PySpark 

Best practices for working efficiently 

Final thoughts 

What is PySpark SQL? 

PySpark SQL is a module of Apache Spark that enables querying structured data using SQL commands or a more programmatic DataFrame API. It offers: 

Support for SQL-style queries on large datasets. 

A seamless bridge between relational logic and Python. 

Optimizations using the Catalyst query optimizer and Tungsten execution engine for efficient computation. 

In simple terms, PySpark SQL lets you use SQL to analyze big data at scale — without needing traditional database systems. 

Why Use PySpark SQL? 

Here are a few compelling reasons to use PySpark SQL: 

Scalability: It can handle terabytes of data spread across clusters. 

Ease of use: Combines the simplicity of SQL with the flexibility of Python. 

Performance: Optimized query execution ensures fast performance. 

Interoperability: Works with various data sources — including Hive, JSON, Parquet, and CSV. 

Integration: Supports seamless integration with DataFrames and MLlib for machine learning. 

Whether you're building dashboards, ETL pipelines, or machine learning workflows — PySpark SQL is a reliable choice. 

Setting Up PySpark 

Let’s quickly set up a local PySpark environment. 

1. Install PySpark: 

pip install pyspark    

2. Start a Spark session: 

from pyspark.sql import SparkSession    spark = SparkSession.builder \      .appName("PySparkSQLExample") \      .getOrCreate()    

3. Create a DataFrame: 

data = [("Alice", 25), ("Bob", 30), ("Clara", 35)]  columns = ["Name", "Age"]  df = spark.createDataFrame(data, columns)  df.show()    

4. Create a temporary view to run SQL queries: 

df.createOrReplaceTempView("people")    

Now you're ready to run SQL queries directly! 

Basic PySpark SQL Queries 

Let’s look at the most commonly used SQL queries in PySpark. 

1. SELECT Query 

spark.sql("SELECT * FROM people").show()    

Returns all rows from the people table. 

2. WHERE Clause (Filtering Rows) 

spark.sql("SELECT * FROM people WHERE Age > 30").show()    

Filters rows where Age is greater than 30. 

3. Adding a Derived Column 

spark.sql("SELECT Name, Age, Age + 5 AS AgeInFiveYears FROM people").show()    

Adds a new column AgeInFiveYears by adding 5 to the current age. 

4. GROUP BY and Aggregation 

Let’s update the data with multiple entries for each name: 

data2 = [("Alice", 25), ("Bob", 30), ("Alice", 28), ("Bob", 35), ("Clara", 35)]  df2 = spark.createDataFrame(data2, columns)  df2.createOrReplaceTempView("people")    

Now apply aggregation: 

spark.sql("""      SELECT Name, COUNT(*) AS Count, AVG(Age) AS AvgAge      FROM people      GROUP BY Name  """).show()    

This groups records by Name and calculates the number of records and average age. 

5. JOIN Between Two Tables 

Let’s create another table: 

jobs_data = [("Alice", "Engineer"), ("Bob", "Designer"), ("Clara", "Manager")]  df_jobs = spark.createDataFrame(jobs_data, ["Name", "Job"])  df_jobs.createOrReplaceTempView("jobs")    

Now perform an inner join: 

spark.sql("""      SELECT p.Name, p.Age, j.Job      FROM people p      JOIN jobs j      ON p.Name = j.Name  """).show()    

This joins the people and jobs tables on the Name column. 

Tips for Working Efficiently with PySpark SQL 

Use LIMIT for testing: Avoid loading millions of rows in development. 

Cache wisely: Use .cache() when a DataFrame is reused multiple times. 

Check performance: Use .explain() to view the query execution plan. 

Mix APIs: Combine SQL queries and DataFrame methods for flexibility. 

Conclusion 

PySpark SQL makes big data analysis in Python much more accessible. By combining the readability of SQL with the power of Spark, it allows developers and analysts to process massive datasets using simple, familiar syntax. 

This blog covered the foundational aspects: setting up PySpark, writing basic SQL queries, performing joins and aggregations, and a few best practices to optimize your workflow. 

If you're just starting out, keep experimenting with different queries, and try loading real-world datasets in formats like CSV or JSON. Mastering PySpark SQL can unlock a whole new level of data engineering and analysis at scale. 

PySpark Training by AccentFuture 

At AccentFuture, we offer customizable online training programs designed to help you gain practical, job-ready skills in the most in-demand technologies. Our PySpark Online Training will teach you everything you need to know, with hands-on training and real-world projects to help you excel in your career. 

What we offer: 

Hands-on training with real-world projects and 100+ use cases 

Live sessions led by industry professionals 

Certification preparation and career guidance 

🚀 Enroll Now: https://www.accentfuture.com/enquiry-form/ 

📞 Call Us: +91–9640001789 

📧 Email Us: contact@accentfuture.com 

🌐 Visit Us: AccentFuture 

[pandeypankaj]

How should I start learning Python?

Good Choice! Python is a fabulous language for Data Science, since it is very readable, versatile, and features a great many libraries.

1. Mastering the Basics of Python

First of all, learn the basics: one needs to study Variables, Data Types — numbers, strings, lists, dictionaries, Operators, Control Flow — if-else, loops, functions

Practice consistently: Learning to code is like learning a language. One has to keep practicing.

Online Resources: One can study through online platforms like Codecademy, Coursera, Lejhro, or watch YouTube Tutorials to learn in a structured format.

2. Dive into Data Structures and Algorithms

Master data structures: Know in detail about lists, tuples, sets, and dictionaries.

Understand algorithms: Know about sorting, searching, and other basic algorithms.

Problem solving: Practice problems or coding challenges on LeetCode or HackerRank.

3. Explore Data Analysis Libraries

NumPy: Introduce yourself to array manipulation, mathematical operations on arrays, and random number generation.

Pandas: Learn data manipulation, cleaning, analysis of DataFrames.

Matplotlib: Visualize your data elegantly with a variety of plot types.

Seaborn: Beautiful visualizations with a high-level interface.

4. Dive into Machine Learning

Scikit-learn: The study of supervised and unsupervised learning algorithms.

How to evaluate a model: metrics, cross-validation, hyperparameter tuning.

Practice on datasets: Solve real-world problems and build up your portfolio.

5. Deep Dive into Data Science

Statistics: probability theory, distributions, hypothesis testing, regression

Big data tools: Be familiar with PySpark for large datasets.

Data Engineering: Data pipelines, ETL processes, cloud platforms

Additional Tips

Join online communities: Participate in forums, discussions, and projects to learn from others.

Build projects: Apply the skill by making a data science project of your own.

Keep learning: The field is very dynamic; hence, keep updating your skills.

Remember

Start small: Break down complex topics into smaller, manageable chunks. 

Practice consistently: To get good at coding, one needs to code regularly. 

Don't be afraid to experiment: Try different approaches, learn from failures.

Look into leveraging some of the free and paid-for online resources that are available.

[technical-shorts-datatools]

PySpark - How to Add Or Update Multiple Columns in Dataframe

Data integrity refers to the quality, consistency, and reliability of data throughout its life cycle. Data engineering pipelines are methods and structures that collect, transform, store, and analyse data from many sources. #bigdata #datascience #database #digitalanalytics #analytics #python #PySpark #Programming #learing #AI #ml #dataengineering

[sql-datatools]

PySpark - How to Add Or Update Multiple Columns in Dataframe

Data integrity refers to the quality, consistency, and reliability of data throughout its life cycle. Data engineering pipelines are methods and structures that collect, transform, store, and analyse data from many sources. #bigdata #datascience #database #digitalanalytics #analytics #python #PySpark #Programming #learing #AI #ml #dataengineering

[datavalleyai]

The Ultimate Guide to Becoming an Azure Data Engineer

The Azure Data Engineer plays a critical role in today's data-driven business environment, where the amount of data produced is constantly increasing. These professionals are responsible for creating, managing, and optimizing the complex data infrastructure that organizations rely on. To embark on this career path successfully, you'll need to acquire a diverse set of skills. In this comprehensive guide, we'll provide you with an extensive roadmap to becoming an Azure Data Engineer.

1. Cloud Computing

Understanding cloud computing concepts is the first step on your journey to becoming an Azure Data Engineer. Start by exploring the definition of cloud computing, its advantages, and disadvantages. Delve into Azure's cloud computing services and grasp the importance of securing data in the cloud.

2. Programming Skills

To build efficient data processing pipelines and handle large datasets, you must acquire programming skills. While Python is highly recommended, you can also consider languages like Scala or Java. Here's what you should focus on:

Basic Python Skills: Begin with the basics, including Python's syntax, data types, loops, conditionals, and functions.

NumPy and Pandas: Explore NumPy for numerical computing and Pandas for data manipulation and analysis with tabular data.

Python Libraries for ETL and Data Analysis: Understand tools like Apache Airflow, PySpark, and SQLAlchemy for ETL pipelines and data analysis tasks.

3. Data Warehousing

Data warehousing is a cornerstone of data engineering. You should have a strong grasp of concepts like star and snowflake schemas, data loading into warehouses, partition management, and query optimization.

4. Data Modeling

Data modeling is the process of designing logical and physical data models for systems. To excel in this area:

Conceptual Modeling: Learn about entity-relationship diagrams and data dictionaries.

Logical Modeling: Explore concepts like normalization, denormalization, and object-oriented data modeling.

Physical Modeling: Understand how to implement data models in database management systems, including indexing and partitioning.

5. SQL Mastery

As an Azure Data Engineer, you'll work extensively with large datasets, necessitating a deep understanding of SQL.

SQL Basics: Start with an introduction to SQL, its uses, basic syntax, creating tables, and inserting and updating data.

Advanced SQL Concepts: Dive into advanced topics like joins, subqueries, aggregate functions, and indexing for query optimization.

SQL and Data Modeling: Comprehend data modeling principles, including normalization, indexing, and referential integrity.

6. Big Data Technologies

Familiarity with Big Data technologies is a must for handling and processing massive datasets.

Introduction to Big Data: Understand the definition and characteristics of big data.

Hadoop and Spark: Explore the architectures, components, and features of Hadoop and Spark. Master concepts like HDFS, MapReduce, RDDs, Spark SQL, and Spark Streaming.

Apache Hive: Learn about Hive, its HiveQL language for querying data, and the Hive Metastore.

Data Serialization and Deserialization: Grasp the concept of serialization and deserialization (SerDe) for working with data in Hive.

7. ETL (Extract, Transform, Load)

ETL is at the core of data engineering. You'll need to work with ETL tools like Azure Data Factory and write custom code for data extraction and transformation.

8. Azure Services

Azure offers a multitude of services crucial for Azure Data Engineers.

Azure Data Factory: Create data pipelines and master scheduling and monitoring.

Azure Synapse Analytics: Build data warehouses and marts, and use Synapse Studio for data exploration and analysis.

Azure Databricks: Create Spark clusters for data processing and machine learning, and utilize notebooks for data exploration.

Azure Analysis Services: Develop and deploy analytical models, integrating them with other Azure services.

Azure Stream Analytics: Process real-time data streams effectively.

Azure Data Lake Storage: Learn how to work with data lakes in Azure.

9. Data Analytics and Visualization Tools

Experience with data analytics and visualization tools like Power BI or Tableau is essential for creating engaging dashboards and reports that help stakeholders make data-driven decisions.

10. Interpersonal Skills

Interpersonal skills, including communication, problem-solving, and project management, are equally critical for success as an Azure Data Engineer. Collaboration with stakeholders and effective project management will be central to your role.

Conclusion

In conclusion, becoming an Azure Data Engineer requires a robust foundation in a wide range of skills, including SQL, data modeling, data warehousing, ETL, Azure services, programming, Big Data technologies, and communication skills. By mastering these areas, you'll be well-equipped to navigate the evolving data engineering landscape and contribute significantly to your organization's data-driven success.

Ready to Begin Your Journey as a Data Engineer?

If you're eager to dive into the world of data engineering and become a proficient Azure Data Engineer, there's no better time to start than now. To accelerate your learning and gain hands-on experience with the latest tools and technologies, we recommend enrolling in courses at Datavalley.

Why choose Datavalley?

At Datavalley, we are committed to equipping aspiring data engineers with the skills and knowledge needed to excel in this dynamic field. Our courses are designed by industry experts and instructors who bring real-world experience to the classroom. Here's what you can expect when you choose Datavalley:

Comprehensive Curriculum: Our courses cover everything from Python, SQL fundamentals to Snowflake advanced data engineering, cloud computing, Azure cloud services, ETL, Big Data foundations, Azure Services for DevOps, and DevOps tools.

Hands-On Learning: Our courses include practical exercises, projects, and labs that allow you to apply what you've learned in a real-world context.

Multiple Experts for Each Course: Modules are taught by multiple experts to provide you with a diverse understanding of the subject matter as well as the insights and industrial experiences that they have gained.

Flexible Learning Options: We provide flexible learning options to learn courses online to accommodate your schedule and preferences.

Project-Ready, Not Just Job-Ready: Our program prepares you to start working and carry out projects with confidence.

Certification: Upon completing our courses, you'll receive a certification that validates your skills and can boost your career prospects.

On-call Project Assistance After Landing Your Dream Job: Our experts will help you excel in your new role with up to 3 months of on-call project support.

The world of data engineering is waiting for talented individuals like you to make an impact. Whether you're looking to kickstart your career or advance in your current role, Datavalley's Data Engineer Masters Program can help you achieve your goals.

[amalgjose]

How to change the python version in PySpark ?

How to change the python version in PySpark ?

To switch the python version in pyspark, set the following environment variables. I was working in an environment with Python2 and Python3. I had to use Python3 in pyspark where the spark was using Python 2 by default.

Python 2 was pointing to –> /usr/bin/python

Python 3 was pointing to –> /usr/bin/python3

To configure pyspark to use python 3, set the following environment variables.

[abubakrjajja]

List Of Free Courses To Do In 2021

ASSLAMOALAIKUM !!

As I promised you guys for free courses in my last post and I noticed so many people want to learn something but they can’t afford expensive courses or they don’t know where to start. There shouldn’t be any compromise on getting yourself educated. So, here is the list of free courses for your Self Learning.

Disclaimer : These courses are for educational purpose only. It is illegal to sell someone’s courses or content without there permission. I’m not the owner of any of these courses. I’m only willing to help you and I don’t earn from this blog or any links.

All courses are in English Language.

How to Download

Download & Install uTorrent app in your Laptop or Mobile

Choose your course from the list below

Click the course title & it will download a (.torrent) file

Launch (.torrent) file and click OK

Now download will start & it’ll take time depending on your internet speed

Islam

Basics of Islamic Finance [download] [info]

Arabic of the Quran from Beginner to Advanced [download] [info]

How to read Quran in Tajweed, Quranic Arabic Course [download] [info]

Draw Islamic Geometric Patterns With A Compass And Ruler [download] [info]

Digital Marketing

The Complete Digital Marketing Course — 12 Courses in 1 [download] [info]

Ultimate Google Ads Training 2020: Profit with Pay Per Click [download] [info]

Digital Marketing Masterclass — 23 Courses in 1 [download] [info]

Mega Digital Marketing Course A-Z: 12 Courses in 1 + Updates [download] [info]

Digital Marketing Strategies Top Ad Agencies Use For Clients [download] [info]

Social Media Marketing + Agency

Social Media Marketing MASTERY | Learn Ads on 10+ Platforms [download] [info]

Social Media Marketing Agency : Digital Marketing + Business [download] [info]

Facebook Ads & Facebook Marketing MASTERY 2021 [download] [info]

Social Media Management — The Complete 2019 Manager Bootcamp [download] [info]

Instagram Marketing 2021: Complete Guide To Instagram Growth [download] [info]

How Retargeting Works–The Complete Guide To Retargeting Ads! [download] [info]

YouTube Marketing & YouTube SEO To Get 1,000,000+ Views [download] [info]

YouTube Masterclass — Your Complete Guide to YouTube [download] [info]

Video Editing + Animation

Premiere Pro CC for Beginners: Video Editing in Premiere [download] [info]

Video Editing complete course | Adobe Premiere Pro CC 2020 [download] [info]

Learn Video Editing with Premiere Pro CC for beginners [download] [info]

2D Animation With No Drawing Skills in AE [download] [info]

Maya for Beginners: Complete Guide to 3D Animation in Maya [download] [info]

After Effects — Motion Graphics & Data Visualization [download] [info]

After Effects CC 2020: Complete Course from Novice to Expert [download] [info]

Graphic Designing

Adobe Photoshop CC — Essentials Training Course [download] [info]

Photoshop CC Retouching and Effects Masterclass [download] [info]

Graphic Design Masterclass — Learn GREAT Design [download] [info]

Graphic Design Bootcamp: Photoshop, Illustrator, InDesign [download] [info]

Canva 2019 Master Course | Use Canva to Grow your Business [download] [info]

CorelDRAW for Beginners: Graphic Design in Corel Draw [download] [info]

Learn Corel DRAW |Vector Graphic Design From Scratch | 2020 [download] [info]

Digital Painting: From Sketch to Finished Product [download] [info]

The Ultimate Digital Painting Course — Beginner to Advanced [download] [info]

Graphic Design Masterclass Intermediate: The NEXT Level [download] [info]

Amazon & Dropshipping

How to Start an Amazon FBA Store on a Tight Budget [download] [info]

The Last Amazon FBA Course — [ 2020 ] Private Label Guide [download] [info]

Amazon Affiliate Marketing Using Authority Site (Beginners) [download] [info]

Amazon Affiliates Mastermind: Build Authority Sites [download] [info]

Amazon FBA Course — How to Sell on Amazon MASTERY Course [download] [info]

The Complete Shopify Aliexpress Dropship course [download] [info]

Virtual Assistant

New Virtual Assistant Business — Your Blueprint to Launch [download] [info]

Must-Have Tools for Virtual Assistants [download] [info]

Learn How To Hire and Manage Your Virtual Assistants [download] [info]

Common Virtual Assistant Interview Questions (and Answers) [download] [info]

WordPress

Wordpress for Beginners — Master Wordpress Quickly [download] [info]

Become a WordPress Developer: Unlocking Power With Code [download] [info]

How To Make a Wordpress Website -Elementor Page Builder [download] [info]

The Complete WordPress Website & SEO Training Masterclass [download] [info]

Complete WordPress Theme & Plugin Development Course [2020] [download] [info]

How to build an ecommerce store with wordpress & woocommerce [download] [info]

Website Development for Beginners in Wordpress [download] [info]

Web Design with WordPress: Design and Build Great Websites [download] [info]

Web Development + SEO

The Complete Web Developer Course 2.0 [download] [info]

Build Websites from Scratch with HTML & CSS [download] [info]

Django 3 — Full Stack Websites with Python Web Development [download] [info]

Web Development: Make A Website That Will Sell For Thousands [download] [info]

Set up a localhost Web Server for Faster Website Development [download] [info]

Website Design With HTML, CSS And JavaScript For Beginners [download] [info]

Adobe Muse CC Course — Design and Launch Websites [download] [info]

SEO 2020: Complete SEO Training + SEO for WordPress Websites [download] [info]

Complete SEO Training With Top SEO Expert Peter Kent! [download] [info]

SEO AUDIT MASTERCLASS: How to do a Manual SEO Audit in 2020 [download] [info]

Freelancing

Seth Godin’s Freelancer Course [download] [info]

Fiverr Freelancing 2021: Sell Fiverr Gigs Like The Top 1% [download] [info]

Complete Web Design: from Figma to Webflow to Freelancing [download] [info]

Freelance Bootcamp — The Comprehensive Guide to Freelancing [download] [info]

Learn Photoshop, Web Design & Profitable Freelancing [download] [info]

Start a Freelance Business: Take Back Your Freedom Now! [download] [info]

How to Dominate Freelancing on Upwork [download] [info]

Copywriting — Become a Freelance Copywriter, your own boss [download] [info]

The Freelance Masterclass: For Creatives [download] [info]

Freelance Article Writing: Start a Freelance Writing Career! [download] [info]

Copywriting: Master Copywriting A — Z | Content Writing[download] [info]

Computer Science

Computer Science 101: Master the Theory Behind Programming [download] [info]

SQL — MySQL for Data Analytics and Business Intelligence [download] [info]

Spark and Python for Big Data with PySpark [download] [info]

Learn SAP ABAP Objects — Online Training Course [download] [info]

Build Responsive Real World Websites with HTML5 and CSS3 [download] [info]

Modern HTML & CSS From The Beginning (Including Sass) [download] [info]

Java Programming Masterclass for Software Developers [download] [info]

Java In-Depth: Become a Complete Java Engineer! [download] [info]

MongoDB — The Complete Developer’s Guide 2020 [download] [info]

Complete Whiteboard Animation in VideoScribe — 5 Animations [download] [info]

The Complete React Native + Hooks Course [2020 Edition] [download] [info]

Flutter & Dart — The Complete Guide [2021 Edition] [download] [info]

Ultimate AWS Certified Solutions Architect Associate 2021 [download] [info]

Cisco CCNA 200–301 — The Complete Guide to Getting Certified [download] [info]

App Development

Mobile App Development with PhoneGap [download] [info]

Desktop Application Development Windows Forms C# [download] [info]

Python Desktop Application Development with PyQt [download] [info]

GUI Development with Python and Tkinter [download] [info]

Cross-platform Desktop App Development for Windows Mac Linux [download] [info]

The Complete Android Oreo Developer Course — Build 23 Apps! [download] [info]

The Complete Android App Development [download] [info]

Complete VB.Net Course,Beginners to Visual Basic Apps-7 in 1 [download] [info]

Learning Visual Basic .NET — A Guide To VB.NET Programming [download] [info]

Game Development

Lua Programming and Game Development with LÖVE [download] [info]

Unreal Engine C++ Developer: Learn C++ and Make Video Games [download] [info]

Complete C# Unity Game Developer 2D [download] [info]

Complete C# Unity Game Developer 3D [download] [info]

Python Bootcamp 2020 Build 15 working Applications and Games [download] [info]

RPG Core Combat Creator: Learn Intermediate Unity C# Coding [download] [info]

Make a fighting game in Unity [download] [info]

Coding

Ultimate Rust Crash Course [download] [info]

C Programming For Beginners — Master the C Language [download] [info]

Mastering Data Structures & Algorithms using C and C++ [download] [info]

C++: From Beginner to Expert [download] [info]

Lua Scripting: Master complete Lua Programming from scratch [download] [info]

PHP for Beginners — Become a PHP Master — CMS Project [download] [info]

Learn Object Oriented PHP By Building a Complete Website [download] [info]

PHP with Laravel for beginners — Become a Master in Laravel [download] [info]

Learn Python Programming Masterclass [download] [info]

Python Beyond the Basics — Object-Oriented Programming [download] [info]

Node.js, Express, MongoDB & More: The Complete Bootcamp 2021 [download] [info]

Node.js API Masterclass With Express & MongoDB [download] [info]

Engineering & Technology

Arduino Step by Step: Getting Started [download] [info]

Arduino Programming and Hardware Fundamentals with Hackster [download] [info]

Arduino Step by Step Getting Serious [download] [info]

Complete Guide to Build IOT Things from Scratch to Market [download] [info]

Introduction to Internet of Things(IoT) using Raspberry Pi 2 [download] [info]

Internet of Things (IoT) — The Mega Course [download] [info]

Automobile Engineering: Vehicle dynamics for Beginners [download] [info]

Automotive 101: A Beginners Guide To Automotive Repair [download] [info]

Mechanical Engineering and Electrical Engineering Explained [download] [info]

Basics Of PLC Programming From Zero Using LogixPro Simulator [download] [info]

Internal Combustion Engine Basics (Mechanical Engineering) [download] [info]

Deep Learning A-Z: Hands-On Artificial Neural Networks [download] [info]

Artificial Intelligence A-Z™: Learn How To Build An AI [download] [info]

Tensorflow 2.0: Deep Learning and Artificial Intelligence [download] [info]

Business & Management

Business Continuity Management System. ISO 22301 [download] [info]

The Data Science Course 2020: Complete Data Science Bootcamp [download] [info]

An Entire MBA in 1 Course:Award Winning Business School Prof [download] [info]

Brand Management: Build Successful Long Lasting Brands [download] [info]

IT Help Desk Professional [download] [info]

Ethics and Attitude in the Office [download] [info]

The Ultimate Microsoft Office 2016 Training Bundle [download] [info]

How to Sell Anything to Anyone [download] [info]

The Complete Communication Skills Master Class for Life [download] [info]

Business Ethics: How to Create an Ethical Organization [download] [info]

Others Mixed

Blogging Masterclass: How To Build A Successful Blog In 2021 [download] [info]

Blogging for a Living — Perfect Small Budget Project [download] [info]

The Complete JavaScript Course 2021: From Zero to Expert! [download] [info]

The Complete Foundation Stock Trading Course [download] [info]

Lead Generation MASTERY with Facebook Lead & Messenger Ads [download] [info]

Data Entry Course for Beginners [download] [info]

SAP WM Course on RF/Mobile Data Entry [download] [info]

The complete AutoCAD 2018–21 course [download] [info]

Complete course in AutoCAD 2020 : 2D and 3D [download] [info]

The Complete Foundation FOREX Trading Course [download] [info]

Complete Fitness Trainer Certification: Beginner To Advanced [download] [info]

Health Coaching Certification Holistic Wellness Health Coach [download] [info]

Chinese language for beginners : Mandarin Chinese [download] [info]

Learn Italian Language: Complete Italian Course — Beginners [download] [info]

Emotional Intelligence: Master Anxiety, Fear, & Emotions [download] [info]

Accounting & Financial Statement Analysis: Complete Training [download] [info]

Accounting in 60 Minutes — A Brief Introduction [download] [info]

The Complete Cyber Security Course : Hackers Exposed! [download] [info]

How To Be Successful in Network Marketing [download] [info]

Create and Sell Online Courses in Website with WordPress CMS [download] [info]

Teacher Training — How to Teach Online — Remote Teaching 1Hr [download] [info]

Sell Your Art Masterclass [download] [info]

The Ultimate Guide To Food Photography [download] [info]

Fundamentals of Analyzing Real Estate Investments [download] [info]

[kajalkumari1]

Python Career Opportunities – Which one will you choose?

The Next Big Thing to look up onto is Python and there is no doubt about that. Questions related to its worth, career opportunities, or available jobs are not to be worried about. As Python is rapidly ceasing the popularity amongst developers and various other fields, its contribution to the advancement of your career is immense.

There are reasons why Python is “the one”. It is easily scripted language that can be learned quickly. Hence reducing the overall development time of the project code. It has a set of different libraries and APIs that support data analysis, data visualization, and data manipulation.

Python Career Opportunities

Number of Python Jobs

While there’s a high demand for Python developers in India, the supply is really, really low. To testify this, we’ll take account of an HR professional statement. The professional was expected to recruit 10 programmers each for both Java and Python. About a hundred good resumes flooded in for Java, but they received only 8 good ones for Python. So, while they had to go through a long process to filter out good candidates, with Python, they had no choice but to take those 8 candidates.

What does this tell you about the situation? Even though Python has easy syntax, we really need more people in India to upskill themselves. This is what makes it a great opportunity for Indians to get skilled in python. When we talk about the number of jobs, there may not be too many for Python in India. But we have an excellent number of jobs per Python programmer.

Job boards like Indeed and Naukri offer around 20,000 to 50,000 job listings for Python and this shows that Python career opportunities in India are High. Choosing Online Python Classes in Lucknow to pursue your career is a good choice. The below stats shows the total job postings of the major programming languages.

Types of Python Jobs

So what types of jobs can you land with Python?

Well, for one, Python scope is intensive in data science and analysis. Clients often want hidden patterns extracted from their data pools. It is also preferred in machine learning and artificial intelligence. Data scientists love Python. Also, in our article on applications of Python, we read about how Python is used everywhere in web development, desktop applications, data science, and network programming.

Python Job Profiles

With Python on your resume, you may end up with one of the following positions in a reputed company:

1. Software Engineer

·      Analyze user requirements

·      Write and test code

·      Write operational documentation

·      Consult clients and work closely with other staff

·      Develop existing programs

2. Senior Software Engineer

·      Develop high-quality software architecture

·      Automate tasks via scripting and other tools

·      Review and debug code

·      Perform validation and verification testing

·      Implement version control and design patterns

3. DevOps Engineer

·      Deploy updates and fixes

·      Analyze and resolve technical issues

·      Design procedures for maintenance and troubleshooting

·      Develop scripts to automate visualization

·      Deliver Level 2 technical support

4. Data Scientist

·      Identify data sources and automate the collection

·      Preprocess data & analyze it to discover trends

·      Design predictive models and ML algorithms

·      Perform data visualization

·      Propose solutions to business challenges

5. Senior Data Scientist

·      Supervise junior data analysts

·      Build analytical tools to generate insight, discover patterns, and predict behavior

·      Implement ML and statistics-based algorithms

·      Propose ideas for leveraging possessed data

·      Communicate findings to business partners

Python Future

While many top companies are stuck with Java, Python is one of the old yet trending technologies. The future of Python is bright with :

1. Artificial Intelligence

Artificial Intelligence is the intelligence displayed by machines. This is in contrast to the natural intelligence displayed by humans and other animals. It is one of the new technologies taking over the world. When it’s about AI, Python is one of the first choices; in fact, it is one of the most-suited languages for it.

For this purpose, we have different frameworks, libraries, and tools dedicated to letting AI replace human efforts. Not only does it help with that, but it also raises efficiency and accuracy. AI gives us speech recognition systems, autonomous cars, etc.

The following tools and libraries ship for these branches of AI:

·      Machine Learning – PyML, PyBrain, scikit-learn, MDP Toolkit, GraphLab Create, MIPy

·      General AI – pyDatalog, AIMA, EasyAI, SimpleAI

·      Neural Networks – PyAnn, pyrenn, ffnet, neuro lab

·      Natural Language and Text Processing – Quepy, NLTK, genism

2. Big Data

Big Data is the term for data sets so voluminous and complex that traditional data-processing application software is inadequate in dealing with them.

Python has helped Big Data grow, its libraries allow us to analyze and work with a large amount of data across clusters:

·      Pandas

·      scikit-learn

·      NumPy

·      SciPy

·      GraphLab Create

·      IPython

·      Bokeh

·      Agate

·      PySpark

·      Dask

3. Networking

Python also lets us configure routers and switches, and perform other network-automation tasks cost-effectively. For this, we have the following Python libraries:

·      Ansible

·      Netmiko

·      NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support)

·      Pyeapi

·      Junos PyEZ

·      PySNMP

·      Paramiko SSH

All these technologies rely on Python today and tomorrow.

Top Organizations Using Python

 With its extreme popularity and powerfulness, Python is preferred by unicorns too:

1. NASA & ISRO

NASA and ISRO use Workflow Automation System (WAS), an application written and developed in Python. It was developed by NASA’s shuttle-support contractor USA (United Space Alliance).

NASA also uses Python for APOD (Astronomy Picture Of the Day), API, PyTransit, PyMDP Toolbox, EVEREST.

2. Google

Who, on this Earth, lives and doesn’t know Google? We use it for everything – sometimes, even to find answers to life’s deepest questions. Google uses Python for its internal systems, and its APIs for report-generation, log analysis, A/Q and testing, and writing core search-algorithms.

3. Nokia

This one reminds me of Nokia 3310, the pocket phone that could break a tile. Nokia makes use of PyS60 (Python for S60). It also uses PyMaemo (Python for Maemo) for its S60 (Symbian), and Maemo (Linux) software platforms.

4. IBM

An American multinational technology company headquartered in New York, IBM uses Python for its factory tool control applications.

5. Yahoo! Maps

Maps is an online mapping portal by Yahoo! It uses Python in many of its mapping lookup services and addresses.

6. Walt Disney Feature Animation

WDFA uses Python as a scripting language for animation. All the magic that happens in Disneyland has a bit of Python behind it.

Why Python?

So, after all this Python career opportunities talk, why should you take Online Python Classes in Lucknow? What has it to offer to you? What’s the scope of Python? Let’s see.

 ·      Its simplicity and conciseness make it perfect for beginners.

·      It has a large community that continuously contributes to its development.

·      Because of the highly demand-supply ratio, it provides excellent career opportunities, especially in India.

·      We have a number of frameworks to make web development easy as pie.

·      Python is the preferred language for Artificial Intelligence and Machine Learning.

·      Raspberry Pi, a microcomputer, lets us make our own DIYs with Python, at prices that do not blast holes in your pockets.

·      Both startups and corporates, make extensive use of Python, thanks to its powerfulness and simplicity.

·      Python has been consecutively topping the most loved programming language on the StackOverflow developers survey report.

·      StackOverflow survey reports showed us that Python is the fastest growing language in high-income countries. IBM used the STL model to predict the future growth of major languages in 2020 and it seems Python is going to leave everyone behind.

  Why is Python in demand?

According to expert research, there is a huge gap between demand and supply of python developers/experts across countries like India, the USA, and more. As a result, the available python developers are paid thrice of that of actual salaries to fill the scarcity. This is an important lesson for all those who are doubting the career opportunities with python and also lacking a good hold in python. Expertise in python by gaining experience or even through online python certification training. It adds value to your resume and all-in-all to your overall career goal.

  Python Skills

After knowing all the opportunities that Python holds, its good to know all the ins and out to it. Focus is always on skill first so that you stand out amongst others. They can be broken down as follows:

·      Core Python (Basic knowledge between Python 2 and Python 3 is sufficient, complete knowledge of all modules is not required)

·      Web Frameworks (Learn common Python frameworks such as Django or Pandas)

·      Object-relational mappers (Ability to connect to the database with the help of ORM rather than SQL )

·      Understand Multiprocess Architecture (Ability to write and manage threads for high-performance)

·      RESTful APIs (understand how to use them and able to integrate components with them)

·      Building Python Applications (One should know how to package up a code and deployment and release)

·      Good communication and designing skills (Able to communicate well with members as well as implement servers that are scalable, secure and highly available)

This was all in the Python career opportunities article that provides you benefits by taking Online Python Classes in Lucknow.

[howlongisthedata]

Apache Spark Deep Learning with BigDL:  How to Get Started

photo credit:  https://github.com/intel-analytics/BigDL

What is BigDL?

If you’re reading this article, you probably already know what BigDL is, but in either case, here is the definition from the men and women @ Intel:

BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters. To make it easy to build Spark and BigDL applications, a high level Analytics Zoo is provided for end-to-end analytics + AI pipelines. 

For a bit more detail, BigDL is a distributed deep learning framework built for Apache Spark, and created by the great people @ Intel.  BigDL is optimized to run on Intel CPUs, and more specifically on Apache Spark clusters.

In this article we’ll review why you might use it, and how you can get started with it!

Why use BigDL?

Again, if you’re reading this article you probably already have an idea as to why you want this, but in either case, here is what the creators of BigDL have to say on the subject:

You may want to write your deep learning programs using BigDL if:

You want to analyze a large amount of data on the same Big Data (Hadoop/Spark) cluster where the data are stored (in, say, HDFS, HBase, Hive, etc.).

You want to add deep learning functionalities (either training or prediction) to your Big Data (Spark) programs and/or workflow.

You want to leverage existing Hadoop/Spark clusters to run your deep learning applications, which can be then dynamically shared with other workloads (e.g., ETL, data warehouse, feature engineering, classical machine learning, graph analytics, etc.)

I think the 3rd reason listed above says it all.  In addition, let’s say I have a LOT of data and I want to do deep learning.  Well, having a lot of data is definitely a good thing.  But even running my deep learning training on my enormous amount of data takes a long time on a single machine running 1 GPU because I have to load data into memory in batches, and I have a LOT of data.  So what do can I do?

Well, the next thing I might do is invest in another GPU and run my deep learning using a framework like Horovod.  Or I might get another machine with 1 or more GPUs, so now I have 2 machines with >=1 GPUs per machine and I use Horovod to do my deep learning.  What could go wrong?  Right?  Well the issue now becomes $$$$$.

If you’ve ever purchased a GPU instance on AWS or bought a laptop with a NVIDIA GPU, you might have noticed that GPUs ARE EXPENSIVE!!

So how might I train my neural network without breaking the bank?  This is where BigDL comes into play!

If you’ve ever tried to spin up a EMR cluster on AWS, you might have noticed something as well, EMR CLUSTERS ARE CHEAP!!

Hooray!  Now it’s might be possible to train my neural network without breaking the bank!!

Getting Started

Okay, so now I’m interested, what do I do next?

The next thing I might do, is to start reading the BigDL documentation.

https://bigdl-project.github.io/0.10.0/

As you read through this, you might notice that you have some options here.  You can try to install BigDL on a cluster right off the bat, or you can test out BigDL on a single machine using PySpark and the pip install.

Lesson #1:  Test our your code using a single machine install first!  

In this article, we choose to test out BigDL on a single machine using PySpark and the pip install.  Why?  Well the truth is that we started off with the cluster install and we ran into issues, and then it was hard to debug. 

Getting this to run on a cluster isn’t necessarily hard, but as you’re training your neural network you’ll start to ask yourself “Is it my code that’s wrong or did I mess up the install”?  So it’s easier to get your code running and working on a single instance (as a test environment) and then you can run that same code on a cluster.  Now if you hit any issues you know the issue is most likely related to running it in cluster mode since you already tested your code on a single instance.

This was a hard lesson, and we really suggest doing things this way.  Okay, so we’re sold on getting this running on a single machine first, so how do we do that?

If you’ve browsed the BigDL documentation you might notice they have a bunch of examples.  Hooray again!  We have some base code to work off of!  

Are there issues with the examples?  Of course!  We wouldn’t be writing this article if there wasn’t.  So now what?

Have no fear!  We have more advise to assist!!  (And that’s why we wrote this article).

Lesson #2:  If you have a Windows machine, use a Linux VM on your local machine or an Amazon Workspace.

The first thing to note is that you need a Max or Linux OS to run the PySpark\Python install-from-pip option.  

https://github.com/intel-analytics/BigDL/blob/master/docs/docs/PythonUserGuide/install-from-pip.md

Being a Windows user, I’ve installed extra memory (24GB) on my machine and I use Virtual Box with a Ubuntu image.

https://www.virtualbox.org/

https://ubuntu.com/

If you don’t have a lot of memory on your machine, you can also try Amazon Workspaces which gives you a Linux machine in the cloud (note that this is a RedHat flavor of Linux, so you need to use ‘yum’ instead of ‘apt-get’).

https://aws.amazon.com/workspaces/

Lesson #3:  You need JDK>=8

If you don’t have JDK>=8, and you’re on Linux, you can run the following lines:

sudo apt install open-jdk-8-jdk

sudo update-alternatives --config java

Lesson #4:  Get your own data and run through the example line by line.

This is the final and best advice I can give.  After getting everything installed correctly (which wasn’t that hard) what really helped was not just running an example, but using different data and trying to get everything to run again.  We took data from a Kaggle competition (an image competition) and the hardest part was getting that data into a RDD of Sample format!

I’m not a big advocate of Jupyter, but it’s a great development environment, and we walked through every line and made sure every line was producing our expected result.  Again, the hardest part was getting the data into the format of RDD of Sample, and this took a LOT of playing around and code dissecting.  

In the end, we were able to get a neural network to train!

To view our code, you can check out the following GitHub repo.

https://github.com/yeamusic21/Bengali-AI-BigDL

Conclusion.

We didn’t get the neural network to train to any set level of accuracy, but we were able to get the code running and thus we could use our working code on a large cluster for a full training job.  In short, we showed we could get the code to work.  Again, to view our code, you can check out the following GitHub repo.

https://github.com/yeamusic21/Bengali-AI-BigDL

Thanks for reading and hope you found this helpful!  Feel free to post any questions in the comments section!

[speedysuitfun-blog]

Python Career Opportunities – Python Job Profiles

Python Jobs- Python Career Opportunities

Number of Python Jobs

While there’s a high demand and career opportunities for Python developers in India, the supply is really, really low. To testify this, we’ll take account of an HR professional statement. The professional was expected to recruit 10 programmers each for both Java and Python for a few projects. About a hundred good resumes flooded in for Java, but they received only 8 good ones for Python. So, while they had to go through a long process to filter out good candidates, with Python, they had no choice but to take those 8 candidates.

What does this tell us about the situation? Even though Python has really easy syntax, we really need more people in India to consider it. But then, this is what makes it a great opportunity for an Indian with the skills. When we talk about the number of jobs, there may not be too many for Python in India. But we have an excellent number of jobs per Python programmer. This a good news about Python Careers

Not very long ago, one of India’s unicorn software companies faced a dilemma. It had won a $200 million (Rs. 1200 crore) contract with a large US bank to develop an app store for them. But the company lacked enough dexterous Python programmers. Since Python was the best language for the project, it ended up paying thrice the billing amount to a group of freelance Python programmers in the US instead.

Job boards like Indeed and Naukri offer around 20,000 to 50,000 job listings for Python and this shows that Python career opportunities in India are High. Python Careers are good to go with. The below screenshot from indeed job trends shows job trends in Python compared to other languages.

Python Career Opportunities – Python job Trends

Source: Indeed Job Trends

Types of Python Jobs

So what types of jobs can you land with Python?

Well, for one, Python Scope is intensive use in data science and analysis. Clients often want hidden patterns extracted from their data pools. It is also preferred in Machine Learning and Artificial Intelligence. Data scientists love Python. Also, in our article on Applications of Python, we read about NumPy, SciPy, scikit-learn, pandas, IPython notebook. These are some useful libraries available for Python, and they let us explore the advanced areas of Python and different Python career opportunities

.

Python Career Opportunities – Python Careers

a. Job Profiles:

With Python on your resume, you may end up with one of the following positions in a reputed company:

i. Software Engineer

Analyze user requirements

Write and test code

Write operational documentation

Consult clients and work closely with other staff

Develop existing programs

ii. Senior Software Engineer

Develop high-quality software architecture

Automate tasks via scripting and other tools

Review and debug code

Perform validation and verification testing

Implement version control and design patterns

iii. DevOps Engineer

Deploy updates and fixes

Analyze and resolve technical issues

Design procedures for maintenance and troubleshooting

Develop scripts to automate visualization

Deliver Level 2 technical support

iv. Data Scientist

Identify data sources and automate collection

Preprocess data & analyze it to discover trends

Design predictive models and ML algorithms

Perform data visualization

Propose solutions to business challenges

v. Senior Data Scientist

Supervise junior data analysts

Build analytical tools to generate insight, discover patterns, and predict behavior

Implement ML and statistics-based algorithms

Propose ideas for leveraging possessed data

Communicate findings to business partners

 Future of Python

In our write-up on Applications of Python, we saw where Python finds its use. But what about the future? While many top companies are stuck with Java, Python is one of the new technologies. The future is bright for Python with:

Python Career Opportunities – Python Future

a. Artificial Intelligence

Artificial Intelligence is the intelligence displayed by machines. This is in contrast to the natural intelligence displayed by humans and other animals. It is one of the new technologies taking over the world. When it’s about AI, Python is one of the first choices; in fact, it is one of the most-suited languages for it.

For this purpose, we have different frameworks, libraries, and tools dedicated to let AI replace human efforts. Not only does it help with that, but it also raises efficiency and accuracy. AI gives us speech recognition systems, autonomous cars, and so. The following tools and libraries ship for these branches of AI:

Machine Learning- PyML, PyBrain, scikit-learn,  MDP Toolkit, GraphLab Create, MIPy

General AI- pyDatalog, AIMA, EasyAI, SimpleAI

Neural Networks- PyAnn, pyrenn, ffnet, neurolab

Natural Language and Text Processing- Quepy, NLTK, genism

b. Big Data

Big Data is the term for data sets so voluminous and complex that traditional data-processing application software are inadequate in dealing with them.

Python has helped Big Data grow, its libraries allow us to analyze large amount of data across clusters:

Pandas

scikit-learn

NumPy

SciPy

GraphLab Create

IPython

Bokeh

Agate

PySpark

Dask

c. Networking

Python also lets us configure routers and switches, and lets us perform other network-automation tasks cost-effectively. For this, we have the following libraries:

Ansible

Netmiko

NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support)

Pyeapi

Junos PyEZ

PySNM

Paramiko SSH

All these technologies rely on Python today and tomorrow.

Top Organizations Using Python

With its extreme popularity and powerfulness, Python is preferred by unicorns too:

Python Career Opportunities – Top Companies Using Python

a. NASA

The National Aeronautics and Space Administration uses Workflow Automation System (WAS), an application written and developed in Python. It was developed by NASA’s shuttle-support contractor USA (United Space Alliance). NASA also uses Python for APOD(Astronomy Picture Of the Day), API, PyTransit, PyMDP Toolbox, EVEREST.

b. Google

Who, on this Earth, lives and doesn’t know Google? We use it for everything- sometimes, even to find answers to life’s deepest questions. Google uses Python for its internal systems, and its APIs for report-generation, log analysis, A/Q and testing, and writing core search-algorithms.

c. Nokia

This one reminds me of Nokia 3310, that pocket phone that could break a tile. Nokia makes use of PyS60 (Python for S60). It also uses PyMaemo(Python for Maemo) for its S60(Symbian), and Maemo(Linux) software platforms.

d. IBM

An American multinational technology company headquartered in New York, IBM uses Python for its factory tool control applications.

e. Yahoo! Maps

Maps is an online mapping portal by Yahoo! It uses Python in many of its mapping lookup services and addresses.

f. Walt Disney Feature Animation

WDFA uses Python as a scripting language for animation. All the magic that happens in Disneyland has a bit of Python behind it.

Payscale in Python

In section 4, we saw a rough approximate of how much a Python professional makes. In section 3, we saw some job profiles. So, how does each profile fair in this department?

Python Career Opportunities – Python Salary

Software Engineer – $103,035/yr

Sr. Software Engineer – $129,328/yr

DevOps Engineer – $115,666/yr

Data Scientist – $117,345/yr

Sr. Data Scientist – $136,633/yr

These statistics have been sourced from payscale.com and indeed.com.

Why Must You Learn Python

So, after all this Python career opportunities talk, why must you learn Python? What has it to offer to you? What is the cope of Python? Let’s see.

Python Career Opportunities – Why Python

Its simplicity and conciseness make it perfect for beginners.

It has a large community that continuously contributes to its development.

Because of the highly demand-supply ratio, it provides excellent career opportunities, especially in India.

We have a number of frameworks to make web development easy as pie.

Python is the chosen language for Artificial Intelligence and Machine Learning.

Raspberry Pi, a microcomputer, lets us make our own DIYs with Python, at prices that do not blast holes in your pockets.

Both startups and corporates, make extensive use of Python, thanks to its powerfulness and simplicity.

Python replaced Java as the second-most popular language on GitHub, with 40 percent more pull requests opened this year than last.

Python Career Opportunities

Source: GitHub –The State of the Octoverse 2017

So, this was all about our blog post on Python Career Opportunities

Conclusion: Python Scope

Now that you know what doors Python can open for you and what are the different Python Career opportunities, which one will you take? Let us know in the comments.

Want to crack your upcoming Python Interviews? – Practice Most Asked Python Interview Questions

If you have any question on Python Career Opportunities, please drop a comment.

[avenuehunter663]

Ubuntu For Docker

Ubuntu is a Debian-based Linux operating system based on free software. Docker is an application that simplifies the process of managing application processes in containers. In this tutorial, you'll install and use Docker Community Edition (CE) on Ubuntu 20.04.

There are two methods for installing Docker on Ubuntu 16.04. One method involves installing it on an existing installation of the operating system. The other involves spinning up a server with a tool called Docker Machine that auto-installs Docker on. Systemctl enable docker. Docker installed on ubuntu 16.04 server, check it using the command below. And you will get the docker version 1.x installed on the system. Step 2 - Install and Configure Portainer. Portainer can be installed as a docker container and standalone without docker container. Download a Docker Image in Ubuntu. To run a Docker container, first, you need to download an image from Docker Hub – provides free images from its repositories. For example, to download a Docker image called CentOS 7, issue the following command.

Docker Compose is a Python program that lets you easily deploy multiple containers on a server.

Getting started with Anaconda¶ Anaconda Individual Edition contains conda and Anaconda Navigator, as well as Python and hundreds of scientific packages. When you installed Anaconda, you installed all these too. Conda works on your command line interface such as Anaconda Prompt on Windows and terminal on macOS and Linux. Install Anaconda. In this step, we will install the Anaconda Python software on your system. This step assumes you have sufficient administrative privileges to install software on your system. Double click the downloaded file. Follow the installation wizard. Anaconda python install mac. Anaconda is a package manager, an environment manager, and Python distribution that contains a collection of many open source packages. An installation of Anaconda comes with many packages such as numpy, scikit-learn, scipy, and pandas preinstalled and is also the recommended way to install Jupyter Notebooks. Then instead of just calling pip install, you can use the module flag -m with python so that it uses the anaconda python for the installation. Python -m pip install This installs the package to the anaconda library directory rather than to the library directory associated with (the non-anaconda) pip. If you have a CDH cluster, you can install the Anaconda parcel using Cloudera Manager. The Anaconda parcel provides a static installation of Anaconda, based on Python 2.7, that can be used with Python and PySpark jobs on the cluster.

As you start exploring Docker, you'll learn that often to run a certain web-app, you'll need to run various services (like database, web-server etc) in different containers.

Deploying multiple containers is a lot easier with Docker Compose.

In this tutorial, you'll learn two ways of installing Docker Compose on Ubuntu:

Installing Docker Compose from Ubuntu's repository: Easier method but may not have the latest version of docker compose

Installing the latest Docker Compose using PIP: Gets you the newer docker compose version

Keep in mind that to use Docker Compose, you must have Docker installed on Ubuntu.

Install Docker Compose from Ubuntu's repository

This is the easiest and recommend method. Unless you need the latest Docker Compose version for some specific reasons, you can manage very well with the docker compose version provides by Ubuntu.

Docker Compose is available in the universe repository of Ubuntu 20.04 and 18.04 so make sure to enable it first:

You probably won't need it but no harm in updating the local cache:

Now you can install Docker Compose in Ubuntu using this command:

You can check that Docker Compose is installed successfully by checking its version:

It should show an output like this:

Install the latest Docker Compose on Ubuntu using PIP

PIP stands for 'PIP Installs Package'. It's a command-line based package manager for installing Python applications.

Since Docker Compose is basically a Python program, you can use PIP to install it.

But before you do that, you need to install PIP on Ubuntu first.

Enable the universe repository first.

Step 4: Click the download icon in the upper right corner, looking like a down arrow in a cloud, and select a folder to download iCloud photos to your PC or Mac. Note: Actually, you can easily download all iCloud photos by pressing Ctrl + A to select all photos if you have less than 1000 items there, then clicking on the download button. How to download photos from iCloud via iCloud.com Follow these directions to save copies of your photos and videos from iCloud.com to your Apple device. On your iPhone, iPad, or iPod Touch. ICloud Photos works with the Photos app to keep your photos and videos securely stored in iCloud, and up to date on your iPhone, iPad, iPod touch, Mac, Apple TV, and iCloud.com. How it works iCloud Photos automatically keeps every photo and video you take in iCloud, so you can access your library from any device, anytime you want. Icloud photos download to mac.

Install PIP now: Mac os 10.7 2 download free.

Now that you have PIP installed use it to install Docker Compose for all users on your Linux system:

Ubuntu Docker For Gns3

Check the Docker Compose version to ensure that it is installed successfully:

Docker For Linux Ubuntu

You can see that Docker Compose installed via PIP is more recent version.

I hope you were able to successfully install Docker Compose on Ubuntu with this tutorial. Questions and suggestions are welcome.

Become a Member for FREE

Ubuntu Core For Docker

Become a member to get the regular Linux newsletter (2-4 times a month) and access member-only contents.

Ubuntu For Dockerfile

Join the conversation.

[rafi1228]

Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!

What you’ll learn

Use Python and Spark together to analyze Big Data

Learn how to use the new Spark 2.0 DataFrame Syntax

Work on Consulting Projects that mimic real world situations!

Classify Customer Churn with Logisitic Regression

Use Spark with Random Forests for Classification

Learn how to use Spark’s Gradient Boosted Trees

Use Spark’s MLlib to create Powerful Machine Learning Models

Learn about the DataBricks Platform!

Get set up on Amazon Web Services EC2 for Big Data Analysis

Learn how to use AWS Elastic MapReduce Service!

Learn how to leverage the power of Linux with a Spark Environment!

Create a Spam filter using Spark and Natural Language Processing!

Use Spark Streaming to Analyze Tweets in Real Time!

Requirements

General Programming Skills in any Language (Preferrably Python)

20 GB of free space on your local computer (or alternatively a strong internet connection for AWS)

Description

Learn the latest Big Data Technology – Spark! And learn to use it with one of the most popular programming languages, Python!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that we’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you’ll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!

We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion!

If you’re ready to jump into the world of Python, Spark, and Big Data, this is the course for you!

Who this course is for:

Someone who knows Python and would like to learn how to use it for Big Data

Someone who is very familiar with another programming language and needs to learn Spark

Created by Jose Portilla Last updated 11/2018 English English [Auto-generated]

Size: 1.55 GB

   Download Now

https://ift.tt/2TpvMcY.

The post Spark and Python for Big Data with PySpark appeared first on Free Course Lab.

[thinkdash]

Reduce both experimentation time and training time for neural networks by using many GPU servers.

On June 8, 2017, the age of distributed deep learning began. On that day, Facebook released a paper showing the methods they used to reduce the training time for a convolutional neural network (RESNET-50 on ImageNet) from two weeks to one hour, using 256 GPUs spread over 32 servers. In software, they introduced a technique to train convolutional neural networks (ConvNets) with very large mini-batch sizes: make the learning rate proportional to the mini-batch size. This means anyone can now scale out distributed training to 100s of GPUs using TensorFlow. But that’s not the only advantage of distributed TensorFlow: you can also massively reduce your experimentation time by running many experiments in parallel on many GPUs. This reduces the time required to find good hyperparameters for your neural network.

Methods that scale with computation are the future of AI. —Rich Sutton, father of reinforcement learning

In this tutorial, we will explore two different distributed methods for using TensorFlow:

Running parallel experiments over many GPUs (and servers) to search for good hyperparameters

Distributing the training of a single network over many GPUs (and servers), reducing training time

We will provide code examples of methods (1) and (2) in this post, but, first, we need to clarify the type of distributed deep learning we will be covering.

Model parallelism versus data parallelism

Some neural networks models are so large they cannot fit in memory of a single device (GPU). Google’s Neural Machine Translation system is an example of such a network. Such models need to be split over many devices (workers in the TensorFlow documentation), carrying out the training in parallel on the devices. For example, different layers in a network may be trained in parallel on different GPUs. This training procedure is commonly known as "model parallelism" (or "in-graph replication" in the TensorFlow documentation). It is challenging to get good performance, and we will not cover this approach any further.

In "data parallelism" (or “between-graph replication” in the TensorFlow documentation), you use the same model for every device, but train the model in each device using different training samples. This contrasts with model parallelism, which uses the same data for every device but partitions the model among the devices. Each device will independently compute the errors between its predictions for its training samples and the labeled outputs (correct values for those training samples). Because each device trains on different samples, it computes different changes to the model (the "gradients"). However, the algorithm depends on using the combined results of all processing for each new iteration, just as if the algorithm ran on a single processor. Therefore, each device has to send all of its changes to all of the models at all the other devices.

In this article, we focus on data parallelism. Figure 1 illustrates typical data parallelism, distributing 32 different images to each of the 256 GPUs running a single model. Together, the total mini-batch size for an iteration is 8,092 images (32 x 256).

Figure 1. In data parallelism, devices train with different subsets of the training data. Image courtesy of Jim Dowling.

Synchronous versus asynchronous distributed training

Stochastic gradient descent (SGD) is an iterative algorithm for finding optimal values, and is one of the most popular algorithms for training in AI. It involves multiple rounds of training, where the results of each round are incorporated into the model in preparation for the next round. The rounds can be run on multiple devices either synchronously or asynchronously.

Each SGD iteration runs on a mini-batch of training samples (Facebook had a large mini-batch size of 8,092 images). In synchronous training, all of the devices train their local model using different parts of data from a single (large) mini-batch. They then communicate their locally calculated gradients (directly or indirectly) to all devices. Only after all devices have successfully computed and sent their gradients is the model updated. The updated model is then sent to all nodes along with splits from the next mini-batch. That is, devices train on non-overlapping splits (subset) of the mini-batch.

Although parallelism has the potential for greatly speeding up training, it naturally introduces overhead. A large model and/or slow network will increase the training time. Training may stall if there is a straggler (a slow device or network connection). We also want to reduce the total number of iterations required to train a model because each iteration requires the updated model to be broadcast to all nodes. In effect, this means increasing the mini-batch size as much possible such that it does not degrade the accuracy of the trained model.

In their paper, Facebook introduced a linear scaling rule for the learning rate that enables training with large mini-batches. The rule states that “when the minibatch size is multiplied by k, multiply the learning rate by k,” with the proviso that the learning rate should be increased slowly over a few epochs before it reaches the target learning rate.

In asynchronous training, no device waits for updates to the model from any other device. The devices can run independently and share results as peers, or communicate through one or more central servers known as "parameter" servers. In the peer architecture, each device runs a loop that reads data, computes the gradients, sends them (directly or indirectly) to all devices, and updates the model to the latest version. In the more centralized architecture, the devices send their output in the form of gradients to the parameter servers. These servers collect and aggregate the gradients. In synchronous training, the parameter servers compute the latest up-to-date version of the model, and send it back to devices. In asynchronous training, parameter servers send gradients to devices that locally compute the new model. In both architectures, the loop repeats until training terminates. Figure 2 illustrates the difference between asynchronous and synchronous training.

Figure 2. Asynchronous and synchronous training with stochastic gradient descent (SGD). Image courtesy of Jim Dowling.

Parameter server architecture

When parallel SGD uses parameter servers, the algorithm starts by broadcasting the model to the workers (devices). In each training iteration, each worker reads its own split from the mini-batch, computing its own gradients, and sending those gradients to one or more parameter servers. The parameter servers aggregate all the gradients from the workers and wait until all workers have completed before they calculate the new model for the next iteration, which is then broadcast to all workers. The data flow is shown in Figure 3.

Figure 3. Parameter server architecture for synchronous stochastic gradient descent. Image courtesy of Jim Dowling.

Ring-allreduce architecture

In the ring-allreduce architecture, there is no central server that aggregates gradients from workers. Instead, in a training iteration, each worker reads its own split for a mini-batch, calculates its gradients, sends its gradients to its successor neighbor on the ring, and receives gradients from its predecessor neighbor on the ring. For a ring with N workers, all workers will have received the gradients necessary to calculate the updated model after N-1 gradient messages are sent and received by each worker.

Ring-allreduce is bandwidth optimal, as it ensures that the available upload and download network bandwidth at each host is fully utilized (in contrast to the parameter server model). Ring-allreduce can also overlap the computation of gradients at lower layers in a deep neural network with the transmission of gradients at higher layers, further reducing training time. The data flows are shown in Figure 4.

Figure 4. Ring-allreduce architecture for synchronous stochastic gradient descent. Image courtesy of Jim Dowling.

Parallel experiments

So far, we have covered distributed training. However, many GPUs can also be used for parallelizing hyperparameter optimization. That is, when we want to establish an appropriate learning rate or mini-batch size, we can run many experiments in parallel using different combinations of the hyperparameters. After all experiments have completed, we can use the results to determine whether more experimentation is needed or whether the current hyperparameter values are good enough. If the hyperparameters are acceptable, you can use them when training your model on many GPUs.

Two uses for distributed GPUs in TensorFlow

The following sections illustrate how to use TensorFlow for parallel experiments and distributed training.

Parallel experiments

It’s easy to parallelize parameter sweeps over many GPUs, as we need a central point only to schedule the experiments. TensorFlow does not provide built-in support for starting and stopping TensorFlow servers, so we will use Apache Spark to run each TensorFlow Python program in a PySpark mapper function. Below, we define a launch function that takes as parameters (1) the Spark session object, (2) a map_fun that names the TensorFlow function to be executed at each Spark executor, and (3) an args_dict dictionary containing the hyperparameters. Spark can run many Tensorflow servers in parallel by running them inside a Spark executor. A Spark executor is a distributed service that executes tasks. In this example, each executor will calculate the hyperparameters it should use from the args_dict using its executor_num to index into the correct param_val, and then run the supplied training function with those hyperparameters.

def launch(spark_session, map_fun, args_dict): """ Execute a ‘map_fun’ for each hyperparameter combination from the dictionary ‘args_dict’ Args: :spark_session: SparkSession object :map_fun: The TensorFlow function to run (wrapped inside a Spark mapper function) :args_dict: hyperparameters to insert as arguments for each TensorFlow function """ sc = spark_session.sparkContext # Length of the list of the first list of arguments represents the number of Spark tasks num_tasks = len(args_dict.values()[0]) # Create a number of partitions (tasks) nodeRDD = sc.parallelize(range(num_tasks), num_tasks) # Execute each of the hyperparameter arguments as a task nodeRDD.foreachPartition(_do_search(map_fun, args_dict)) def _do_search(map_fun, args_dict): def _wrapper_fun(iter): for i in iter: executor_num = i arg_count = map_fun.func_code.co_argcount names = map_fun.func_code.co_varnames args = [] arg_index = 0 while arg_count > 0: # Get arguments for hyperparameter combination param_name = names[arg_index] param_val = args_dict[param_name][executor_num] args.append(param_val) arg_count -= 1 arg_index += 1 map_fun(*args) return _wrapper_fun

The mnist TensorFlow training function can now be called from within Spark. Note that we only call launch once, but for each hyperparameter combination, a task is executed on a different executor (four in total):

args_dict = {'learning_rate': [0.001, 0.0001], 'dropout': [0.45, 0.7]} def mnist(learning_rate, dropout): """ An implementation of FashionMNIST should go here """ launch(spark, mnist, args_dict):

Distributed training

We will briefly cover three frameworks for distributed training on TensorFlow: native Distributed TensorFlow, TensorFlowOnSpark, and Horovod.

Distributed TensorFlow

Distributed TensorFlow applications consist of a cluster containing one or more parameter servers and workers. Because workers calculate gradients during training, they are typically placed on a GPU. Parameter servers only need to aggregate gradients and broadcast updates, so they are typically placed on CPUs, not GPUs. One of the workers, the chief worker, coordinates model training, initializes the model, counts the number of training steps completed, monitors the session, saves logs for TensorBoard, and saves and restores model checkpoints to recover from failures. The chief worker also manages failures, ensuring fault tolerance if a worker or parameter server fails. If the chief worker itself dies, training will need to be restarted from the most recent checkpoint.

One disadvantage of Distributed TensorFlow, part of core TensorFlow, is that you have to manage the starting and stopping of servers explicitly. This means keeping track of the IP addresses and ports of all your TensorFlow servers in your program, and starting and stopping those servers manually. Generally, this leads to a lot of switch statements in your code to determine which statements should be executed on the current server. Therefore, we will make life easier by using a cluster manager and Spark. Hopefully, you will never have to write code like this, defining a ClusterSpec manually:

tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]}) tf.train.ClusterSpec({ "worker": [ "worker0.example.com:2222", "worker1.example.com:2222", "worker2.example.com:2222" ], "ps": [ "ps0.example.com:2222", "ps1.example.com:2222" ]}) … if FLAGS.job_name == "ps": server.join() elif FLAGS.job_name == "worker": ….

It is error-prone and impractical to create a ClusterSpec using host endpoints (IP address and port number). Instead, you should use a cluster manager such as YARN, Kubernetes, or Mesos to reduce the complexity of configuring and launching TensorFlow applications. The main options are either a cloud managed solution (like Google Cloud ML or Databrick’s Deep Learning Pipelines), or a general-purpose resource manager like Mesos or YARN.

TensorFlowOnSpark

TensorFlowOnSpark is a framework that allows distributed TensorFlow applications to be launched from within Spark programs. It can be run on a standalone Spark cluster or a YARN cluster. The TensorFlowOnSpark program below performs distributed training of Inception using the ImageNet data set.

The new concepts it introduces are a TFCluster object to start your cluster, as well as to perform training and inference. The cluster can be started in either SPARK mode or TENSORFLOW mode. SPARK mode uses RDDs to feed data to TensorFlow workers. This is useful for building integrated pipelines from Spark to TensorFlow, but it is a performance bottleneck because there is only one Python thread to serialize the RDD to a the feed_dict for a TensorFlow worker. TENSORFLOW input mode is generally preferred, as data can be read using a more efficient multi-threaded input queue from a distributed filesystem, such as HDFS. When a cluster is started, it launches the TensorFlow workers and parameter servers (potentially on different hosts). The parameter servers only execute the server.join() command, while workers read the ImageNet data and perform the distributed training. The chief worker has task_id ‘0’.

The following program collects the information needed to use Spark to start and manage the parameter servers and workers on Spark.

from __future__ import absolute_import from __future__ import division from __future__ import print_function from pyspark.context import SparkContext from pyspark.conf import SparkConf from tensorflowonspark import TFCluster, TFNode from datetime import datetime import os import sys import tensorflow as tf import time def main_fun(argv, ctx): # extract node metadata from ctx worker_num = ctx.worker_num job_name = ctx.job_name task_index = ctx.task_index assert job_name in ['ps', 'worker'], 'job_name must be ps or worker' from inception import inception_distributed_train from inception.imagenet_data import ImagenetData import tensorflow as tf # instantiate FLAGS on workers using argv from driver and add job_name and task_id print("argv:", argv) sys.argv = argv FLAGS = tf.app.flags.FLAGS FLAGS.job_name = job_name FLAGS.task_id = task_index print("FLAGS:", FLAGS.__dict__['__flags']) # Get TF cluster and server instances cluster_spec, server = TFNode.start_cluster_server(ctx, 4, FLAGS.rdma) if FLAGS.job_name == 'ps': # `ps` jobs wait for incoming connections from the workers. server.join() else: # `worker` jobs will actually do the work. dataset = ImagenetData(subset=FLAGS.subset) assert dataset.data_files() # Only the chief checks for or creates train_dir. if FLAGS.task_id == 0: if not tf.gfile.Exists(FLAGS.train_dir): tf.gfile.MakeDirs(FLAGS.train_dir) inception_distributed_train.train(server.target, dataset, cluster_spec, ctx) # parse arguments needed by the Spark driver import argparse parser = argparse.ArgumentParser() parser.add_argument("--epochs", help="number of epochs", type=int, default=5) parser.add_argument("--steps", help="number of steps", type=int, default=500000) parser.add_argument("--input_mode", help="method to ingest data: (spark|tf)", choices=["spark","tf"], default="tf") parser.add_argument("--tensorboard", help="launch tensorboard process", action="store_true") (args,rem) = parser.parse_known_args() input_mode = TFCluster.InputMode.SPARK if args.input_mode == 'spark' else TFCluster.InputMode.TENSORFLOW print("{0} ===== Start".format(datetime.now().isoformat())) sc = spark.sparkContext num_executors = int(sc._conf.get("spark.executor.instances")) num_ps = int(sc._conf.get("spark.tensorflow.num.ps")) cluster = TFCluster.run(sc, main_fun, sys.argv, num_executors, num_ps, args.tensorboard, input_mode) if input_mode == TFCluster.InputMode.SPARK: dataRDD = sc.newAPIHadoopFile(args.input_data, "org.tensorflow.hadoop.io.TFRecordFileInputFormat", keyClass="org.apache.hadoop.io.BytesWritable", valueClass="org.apache.hadoop.io.NullWritable") cluster.train(dataRDD, args.epochs) cluster.shutdown()

Note that Apache YARN does not yet support GPUs as a resource, and TensorFlowOnSpark uses YARN node labels to schedule TensorFlow workers on hosts with GPUs. The previous example can also be run on Hops YARN that does support GPUs as a resource, enabling more fine-grained sharing of CPU and GPU resources.

Fault tolerance

A MonitoredTrainingSession object can be created to automatically recover a session’s training state from the latest checkpoint in the event of a failure.

saver = tf.train.Saver(sharded=True) is_chief = True if FLAGS.task_id == 0 else False with tf.Session(server.target) as sess : # sess.run(init_op) # re-initialze from checkpoint, if there is one. saver.restore(sess, ...) while True: if is_chief and step % 1000 == 0 : saver.save(sess, "hdfs://....") with tf.train.MonitoredTrainingSession(server.target, is_chief) as sess: while not sess.should_stop(): sess.run(train_op)

Spark will restart a failed executor. If the executor is not the chief worker, it will contact the parameter servers and continue as before because a worker is effectively stateless. If a parameter server dies, the chief worker can recover from the last checkpoint after a new parameter server joins the system. The chief worker also saves a copy of the model every 1,000 steps to serve as the checkpoint. If the chief worker itself fails, training fails, and a new training job has to be started, but it can recover training from the latest complete checkpoint.

Horovod

There are two ring-allreduce frameworks available for TensorFlow: tensorflow.contrib.mpi_collectives (contributed by Baidu) and Uber’s Horovod, built on Nvidia’s NCCL 2 library. We will examine Horovod, as it has a simpler API and good performance on Nvidia GPUs, as shown in Figure 5. Horovod is installed using pip, and it requires the prior installation of Open MPI and NCCL-2 libraries. Horovod requires fewer changes to TensorFlow programs than either Distributed TensorFlow or TensorFlowOnSpark. It introduces an hvd object that has to be initialized, and has to wrap the optimizer (hvd averages the gradients using allreduce or allgather). A GPU is bound to this process using its local rank, and we broadcast variables from rank 0 to all other processes during initialization.

A Horovod Python program is launched using the mpirun command. It takes as parameters the hostname of each server as well as the number of GPUs to be used on each server. An alternative to mpirun is to run Horovod from within a Spark application using the Hops Hadoop platform, which automatically manages the allocation of GPUs to Horovod processes using HopsYARN. Currently, Horovod has no support for fault-tolerant operation, and the model should be checkpointed periodically so that after a failure, training can recover from the latest checkpoint.

import horovod.tensorflow as hvd ; import tensorflow as tf def main(_): hvd.init() loss = ... tf.ConfigProto().gpu_options.visible_device_list = str(hvd.local_rank()) opt = tf.train.AdagradOptimizer(0.01) opt = hvd.DistributedOptimizer(opt) hooks = [hvd.BroadcastGlobalVariablesHook(0)] train_op = opt.minimize(loss)

Figure 5. Horovod/TensorFlow scales near linearly up to 10 GPUs on a DeepLearning11 server (cost: $15,000 U.S. dollars) when training with ResNet-101 on the ImageNet data set. Image courtesy of Jim Dowling.

Deep learning hierarchy of scale

Having seen many of the distributed training architectures for TensorFlow and large mini-batch stochastic gradient descent (SGD), we can now define the following hierarchy of scale. The top of the pyramid is currently the most scalable approach on TensorFlow, the allreduce family of algorithms (including ring-allreduce), and the bottom is the least scalable (and hence the slowest way to train networks). Although parallel experiments are complementary to distributed training, they are, as we have shown, trivially parallelized (with weak scaling), and thus are found lower on the pyramid.

Figure 6. Deep learning hierarchy of scale for synchronous SGD. Image courtesy of Jim Dowling.

Conclusion

Well done! You know now what distributed TensorFlow is capable of and how you can modify your TensorFlow programs for either distributed training or running parallel experiments. The full source code for the examples can be found here.

This post is part of a collaboration between O'Reilly and TensorFlow. See our statement of editorial independence.

Continue reading Distributed TensorFlow.

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[jobsinthefuture-blog]

Why Machine Learning is Required - Are You Ready to Get a Job With Intelligent Machines?

New Post has been published on http://jobsinthefuture.com/index.php/2017/11/06/why-machine-learning-is-required-are-you-ready-for-the-future-of-intelligent-machines/

Why Machine Learning is Required - Are You Ready to Get a Job With Intelligent Machines?

Updated November 6, 2017

As a graphic designer I perform a simple task ten times per day, every single day, and this task takes me roughly one minute to perform. Add that up over a forty eight week work year and that takes precious time away from critical design productivity.

To be exact that time comes out to:

144,000 seconds or 2,400 minutes or 40 hours or 1 full work week.

What is 1 minute ten times a day? No big deal, right? Well, Clearly it is a big deal!

The task I am performing is so simple that I could train a three year old to perform it, but it still has to be done. And there are child labor laws against this, remember?

Task: Export design document — configure the document into the printer settings — set the properties — click print.

Now imagine getting back one full week of precious work productivity every single year of your career, based on a thirty five year career.

Thirty five weeks of work would earn you back 1,400 hours over an entire career.

Machine learning is about giving us the ability to stop wasting time on menial tasks, like taking 40 hours per year to print a document, in order for productivity and creativity to reach its fullest potential.

Machine learning already has amazing job opportunities right now, but the future potential is even greater.

Fortune put out an article stating that IBM projects Data Science to soar 28% by 2020. I tell you this fact to understand the importance of Machine Learning. The growth of machine learning will easily match that of data science. To understand why let’s define the difference between the two outcomes of each practice.

Data Analyst vs. Machine Learning Engineer

“In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you’re analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination—charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. This is especially important to note—the “audience” for your output is human. As a Machine Learning engineer, on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning.”

Excerpt from Udacity Article

  Machine learning would give me the ability to save 40 hours per year in my job so that I could reallocate that time to more creative tasks. 

Data Science would tell me that I lost 40 hours per year and I need to figure out how to get that time back.

The difference between data science and machine learning is passive information vs active solution. Both are very necessary skills, but today we are going to focus on how to get you a job in machine learning.

The current need for Machine Learning

Excerpt from Forbes Article about the demand for Machine Learning:

“To stay competitive, companies need these specialists now and cannot wait five years for universities to produce graduates from new courses.”

The demand for machine learning is so high right now that companies are scrambling to find qualified individuals capable to perform the necessary tasks. Not only is this job incredibly future proof, but there are job openings available to you as soon as you develop the necessary skills.

What Skills Will You Need to Enter the Machine Learning Industry?

Computer Programming (It seems like every longterm job requires this skill)

Python will be the best language for you to get started in Machine Learning.

Algorithms and Statistical Analysis

This skill will help define where improvements can be made in a system.

Evaluation and Application Skills

Once you define an area within a system that is draining productivity you must evaluate the results and apply a solution. You will repeat this process many times as you attempt to boost productivity in a system.

Analyze, Evaluate, Test. Analyze, Evaluate, Test. Analyze, Evaluate, Test.

Become great at this skill!

Software Engineering

As a Machine Learning expert you will be called upon to help fix large systems within products or services. Your ability to carefully design these systems to avoid hang ups and crashes will be extremely important. Make sure you learn best practices for software engineers so that you are a invaluable asset to your client or company. Great Article by Tech Beacon about the best practices for Software Engineers.

  Where is the Best Place to Build Your Machine Learning Skills?

  Start By Learning Python to have a strong foundation for Machine Learning

Edureka’s Python course helps you gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and leveraging Apache Spark.

This course looks at python in a broad spectrum. You will not only understand how to apply python to Machine learning but you will also learn key data analysis concepts. This will increase you value and diversify your skills.

  Principles of Machine Learning Course from Microsoft

This course with Microsoft will get you in the door and help you to understand the basic operations of machine learning. you will learn how to explore classifications, what regressions are in machine learning, how to improve models and details on modeling, recommender systems (learn how to make the machine understand behaviors), and leverage hands on experience with R and python coding languages.

This course is taught by a professor from MIT and Duke, as well as a Senior Content Developer at Microsoft.

  Data Structures and Software Design at the University of Pennsylvania through Edx.org

If you are already proficient in Python and you understand the concepts of data analysis for machine learning then this is the course for you. Once you have a grasp on those two concepts you must become proficient and executing and deploying machine learning software. There are professional best practices and industry standards in order to effectively collaborate on projects for machine learning.

Make sure you are strong in either python or Java before starting this course. If you have developed you skill in Python but you are unfamiliar with java. Take a free course to brush up on your skills: Intro to Java

  Stay up to date on the latest posts and resources as well and get your Copy of The Ultimate Guide to Future Proof Your Career. An Invaluable resource to ensure that you are secure within the Jobs in the Future!

Click Here to Download the Ultimate Guide Now!

[rafi1228]

Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!

BIG DATA

Created by Jose Portilla

Last updated 11/2018

English

English [Auto-generated]

  What you’ll learn

Use Python and Spark together to analyze Big Data

Learn how to use the new Spark 2.0 DataFrame Syntax

Work on Consulting Projects that mimic real world situations!

Classify Customer Churn with Logisitic Regression

Use Spark with Random Forests for Classification

Learn how to use Spark’s Gradient Boosted Trees

Use Spark’s MLlib to create Powerful Machine Learning Models

Learn about the DataBricks Platform!

Get set up on Amazon Web Services EC2 for Big Data Analysis

Learn how to use AWS Elastic MapReduce Service!

Learn how to leverage the power of Linux with a Spark Environment!

Create a Spam filter using Spark and Natural Language Processing!

Use Spark Streaming to Analyze Tweets in Real Time!

Requirements

General Programming Skills in any Language (Preferrably Python)

20 GB of free space on your local computer (or alternatively a strong internet connection for AWS)

BIG DATA9637

Description

Learn the latest Big Data Technology – Spark! And learn to use it with one of the most popular programming languages, Python!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that we’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you’ll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!

We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion!

If you’re ready to jump into the world of Python, Spark, and Big Data, this is the course for you!

Who this course is for:

Someone who knows Python and would like to learn how to use it for Big Data

Someone who is very familiar with another programming language and needs to learn Spark

Size: 1.3GB

  DOWNLOAD TUTORIAL

The post SPARK AND PYTHON FOR BIG DATA WITH PYSPARK appeared first on GetFreeCourses.Me.

[jobsinthefuture-blog]

How to Become a Robotics Engineer with Tutorials and Training Courses for Beginners

New Post has been published on http://jobsinthefuture.com/index.php/2017/10/24/how-to-become-a-robotics-engineer-with-tutorials-and-training-courses-for-beginners/

How to Become a Robotics Engineer with Tutorials and Training Courses for Beginners

Updated October 24, 2017

Becoming a Robotics Engineer will truly set you apart as an individual with an eye on the future!

The time is already here, robotics engineers are being called on from all facets of the workforce, but don’t worry it is not to late to dive into this incredible opportunity.

The Salary for a Robotics Engineer has ability to start at around 50k and top off somewhere in the 200k range!

You know you want to get started in robotics? Don’t wait a moment longer.

Start developing skills at edX.org, one of the highest rated online course providers in the world: Courses on Robotics

  What is developing in robotics and why get involved?

It is clear that robots are changing the way we live. Consider the airport kiosk, or automatic tellers machines (ATMs). These are not iRobot creations walking around in the same way as humans, but they have taken the place of the human presence and they have automated a task that was performed by a human for decades, even centuries.

Airport Kiosks and Automatic Teller Machines are technological advancements that we have become very familiar with. In fact, if you ask most people I am sure they find these “robots” very convenient, and they are grateful for them. Looking into the future robotics is growing at an exponential rate and the technology that is developing will bring more opportunities to humans rather than the fear of stealing away opportunities.

Self driving cars will give people hours a day back to read books, learn new skills, or simply catch up on some rest on the commutes around town. Industrial robots will replace workers in extreme hazardous situations. Consider the 12,000 people that die each year excavated and mining the precious resources we use to power the earth (coal, oil, and other minerals). This number could be drastically lowered.

Getting involved in robotics right now will position you to make these crucial changes to help make the world a safer and more innovative place. History shows that with the ushering in of technology more jobs were created, not taken away as doomsday futurist continue to preach from their soapboxes.

  Skills Needed to Become a Robotics Engineer

MATLAB: The standard robotics programming environment.

You should know how to use this tools to write functions, calculate vectors and produce visualizations.

Understand how to apply linear algebra, geometry, and group theory tools to configure and control the motion of manipulators and mobile robots.

C/C++ and/or python skills to run programs and run systems checks.

Background in Linux operating system

Hands on troubleshooting of both hardware and software

Development of Cloud applications like Azure or AWS

Bachelors degrees are usually listed on job applications, but show you skills, work experience, and proof of online certifications and this should not be an issue. (Skills trump a college degree)

  How to Become a Robotics Engineer – Online Education

  Master Robotics from the University of Pennsylvania

Learn how to design, build and program robots, and rise in the ranks or kick start a career in one of the fastest growing tech fields today!

  Python Certification from Edureka

Python course helps you gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.

If you lack coding knowledge this course will be highly beneficial to you!

Go from zero -to- hero with this python course from edureka.

  Microsoft Azure Certification

        Azure Certification Training is designed to help you pass the Microsoft Azure Certification Exam. You will learn to create and manage Azure Resource Manager Virtual Machines, design and implement a storage and data strategy, manage identity, application and network services together with mastering the concepts like Azure Ad, Azure Storage, Azure SDK, Azure Cloud Services, Azure SQL Database, Azure Web App.

Understanding the use of data management and resources is crucial. Many key aspects of robotics will be taking place within these environments.

 AWS SystemOps Certification

WS SysOps Certification Training is designed to help you pass the AWS Certified SysOps Administrator Associate Exam. Learn how to create automatable and repeatable deployments of networks and systems on the AWS platform using AWS features and tools related to configuration and deployment. You will also gain expertise in services like Cloudwatch, Cloudtrail, ELB, Route53, EC2, S3, Glacier, IAM and VPC

Another cloud based data environment to capture and understand information. You will want to do some research on your specific field of robotics in order to see which cloud system is best for your interests.

In the near future I will be creating a review on the differences between AWS and Azure. Stay Tuned!

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