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datavalleyai · 2 years ago

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Data Engineering Fundamentals Every Data Engineer Should Know

Data engineering is essential for modern data-driven organizations. A data engineer’s expertise in collecting, transforming, and preparing data is fundamental to extracting meaningful insights and driving strategic initiatives. Data engineering is a field that is constantly evolving, and it is important to stay up-to-date on the latest trends and technologies. In this article, we delve into the foundational concepts that every data engineer should be well-versed in.

1. Data Pipeline Architecture

At the heart of data engineering lies the design and construction of data pipelines. These pipelines serve as pathways for data to flow from various sources to destinations, often involving extraction, transformation, and loading (ETL) processes. Understanding different pipeline architectures, such as batch processing and real-time streaming, is essential for efficiently handling data at scale.

2. Big Data Foundations: SQL and NoSQL Databases

Data engineers should be familiar with both relational and NoSQL databases. Relational databases offer structured storage and support for complex queries, while NoSQL databases provide flexibility for unstructured or semi-structured data. Mastering database design, indexing, and optimization techniques is crucial for managing data effectively.

3. Python for Data Engineering

Python’s extensive libraries and packages make it a powerful tool for data engineering tasks. From data manipulation and transformation to connecting with APIs and databases, Python’s flexibility allows data engineers to perform a variety of tasks using a single programming language. Python is a powerful language for data engineering, with capabilities for automation, integration, exploration, visualization, API interaction, error handling, and community support.

4. Data Transformation

Raw data often requires cleaning and transformation to be useful. Data engineers should be skilled in data transformation techniques, including data normalization, aggregation, and enrichment. Proficiency in tools like Apache Spark or SQL for data manipulation is a fundamental aspect of this process.

5. Cloud Services: AWS Certified Data Analytics Specialty

As organizations shift towards cloud computing, data engineers must be well-versed in cloud services. Familiarity with platforms like AWS, Google Cloud, or Azure is essential for building scalable and cost-effective data solutions. Understanding how to set up and manage cloud-based data storage, computing, and processing is a key skill.

Become an AWS data analytics expert with Datavalley’s comprehensive course. Learn data collection, storage, processing, and pipelines with Amazon S3, Redshift, AWS Glue, QuickSight, SageMaker, and Kinesis. Prepare for the certification exam and unlock new career possibilities.

6. Data Modeling

Data modeling involves designing the structure of databases to ensure data integrity and efficient querying. Data engineers should be comfortable with conceptual, logical, and physical data modeling techniques. Properly designed data models facilitate optimized storage and retrieval of information.

7. Distributed Data Processing

In the age of big data, distributed data processing frameworks like Hadoop and Spark are essential tools for data engineers. Learning how to use these frameworks allows you to process large datasets in parallel efficiently. Learn distributed data processing with Big Data Hadoop, HDFS, Apache Spark, PySpark, and Hive. Gain hands-on experience with the Hadoop ecosystem to tackle big data challenges.

8. Data Quality and Validation

Ensuring data quality is paramount. Data engineers should know how to implement data validation checks to identify and rectify anomalies or errors. Proficiency in data profiling, outlier detection, and data cleansing techniques contributes to accurate and reliable analysis.

9. Version Control and Collaboration

Data engineering often involves collaboration within teams. Understanding version control systems like Git ensures efficient collaboration, code management, and tracking of changes. This is crucial for maintaining the integrity of data engineering projects.

10. Data Lake Table Format Framework

Data lakes are becoming increasingly prevalent. Exploring the table format framework within data lakes allows data engineers to efficiently organize and manage vast amounts of diverse data. Learn about Delta Lake and Hudi for data lake management. Delta Lake provides data consistency, reliability, and versioning. Hudi offers stream processing and efficient data ingestion. Work on real-world projects to elevate your expertise.

11. Scalability and Performance

Scalability is a core consideration in data engineering. Data engineers should comprehend techniques for horizontal and vertical scaling to handle growing data volumes. Optimizing query performance and database indexing contribute to efficient data processing.

12. Security and Compliance

Data security and compliance are paramount in data engineering. Data engineers should be well-versed in encryption, access control, and compliance regulations such as GDPR. Implementing robust security measures safeguards sensitive data.

Conclusion

In conclusion, every data engineer should have a thorough understanding of these fundamental concepts. Data professionals need expertise in specialized topics and DevOps principles to navigate data complexities, lead organizations to data-driven excellence, and remain at the forefront of innovation.

Data engineers can utilize their skills in creating efficient data pipelines and ensuring data quality and security to unlock the full potential of data for insights that drive organizational growth. Data engineers need to master essential skills to stay ahead of the data landscape and drive transformative insights.

Become a Data Engineer

Datavalley’s Big Data Engineer Masters Program helps you develop the skills necessary to become an expert in data engineering. It offers comprehensive knowledge in Big Data, SQL, NoSQL, Linux, and Git. The program provides hands-on training in big data processing with Hadoop, Spark, and AWS tools like Lambda, EMR, Kinesis, Athena, Glue, and Redshift. You will gain in-depth knowledge of data lake storage frameworks like Delta Lake and Hudi. Work on individual projects designed to equip the learners with hands-on experience. By the end of this course, you will have the skills and knowledge necessary to design and implement scalable data engineering pipelines on AWS using a range of services and tools.

#data engineering #datavalley #dataexperts #data engineer #big data

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amalgjose · 5 years ago

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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.

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maphyorg · 5 years ago

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Cheatsheets for AI, Neural Networks, Machine Learning, Big Data, etc.

Neural Networks

Neural Networks Cheat Sheet

Neural Networks Graphs

Neural Networks Graphs Cheat Sheet

Neural Network Cheat Sheet

Machine Learning Overview

Machine Learning Cheat Sheet

Machine Learning: Scikit-learn algorithm

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

Machine Learning Cheat Sheet

Scikit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine learninglibrary for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Scikit-Learn Cheat Sheet

MACHINE LEARNING : ALGORITHM CHEAT SHEET

This machine learning cheat sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution. First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job.

MACHINE LEARNING ALGORITHM CHEAT SHEET

Python for Data Science

Python Data Science Cheat Sheet

Big Data Cheat Sheet

TensorFlow

In May 2017 Google announced the second-generation of the TPU, as well as the availability of the TPUs in Google Compute Engine.[12] The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs provide up to 11.5 petaflops.

TesorFlow Cheat Sheet

Keras

In 2017, Google’s TensorFlow team decided to support Keras in TensorFlow’s core library. Chollet explained that Keras was conceived to be an interface rather than an end-to-end machine-learning framework. It presents a higher-level, more intuitive set of abstractions that make it easy to configure neural networks regardless of the backend scientific computing library.

Keras Cheat Sheet

Numpy

NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy address the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy.

Numpy Cheat Sheet

Pandas

The name ‘Pandas’ is derived from the term “panel data”, an econometricsterm for multidimensional structured data sets.

Pandas Cheat Sheet

Data Wrangling

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

Data Wrangling Cheat Sheet

Pandas Data Wrangling Cheat Sheet

Data Wrangling with dplyr and tidyr

Data Wrangling with dplyr and tidyr Cheat Sheet

Scipy

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]

Scipy Cheat Sheet

Matplotlib

matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-orientedAPI for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural“pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.[2] SciPymakes use of matplotlib.

pyplot is a matplotlib module which provides a MATLAB-like interface.[6]matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

Matplotlib Cheat Sheet

Data Visualization

Data Visualization Cheat Sheet

ggplot cheat sheet

PySpark

Pyspark Cheat Sheet

Big-O

Big-O Algorithm Cheat Sheet

Big-O Algorithm Complexity Chart

BIG-O Algorithm Data Structure Operations

Big-O Array Sorting Algorithms

from WordPress https://maphyorg.wpcomstaging.com/3118.html

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kajalkumari1 · 5 years ago

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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.

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speedysuitfun-blog · 6 years ago

Text

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

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.

0 notes

maphyorg · 6 years ago

Text

Cheatsheets for AI, Neural Networks, Machine Learning, Big Data, etc.

Neural Networks

Neural Networks Cheat Sheet

Neural Networks Graphs

Neural Networks Graphs Cheat Sheet

Neural Network Cheat Sheet

Machine Learning Overview

Machine Learning Cheat Sheet

Machine Learning: Scikit-learn algorithm

Machine Learning Cheat Sheet

Scikit-Learn

Scikit-Learn Cheat Sheet

MACHINE LEARNING : ALGORITHM CHEAT SHEET

MACHINE LEARNING ALGORITHM CHEAT SHEET

Python for Data Science

Python Data Science Cheat Sheet

Big Data Cheat Sheet

TensorFlow

TesorFlow Cheat Sheet

Keras

Keras Cheat Sheet

Numpy

Numpy Cheat Sheet

Pandas

The name ‘Pandas’ is derived from the term “panel data”, an econometricsterm for multidimensional structured data sets.

Pandas Cheat Sheet

Data Wrangling

Data Wrangling Cheat Sheet

Pandas Data Wrangling Cheat Sheet

Data Wrangling with dplyr and tidyr

Data Wrangling with dplyr and tidyr Cheat Sheet

Scipy

Scipy Cheat Sheet

Matplotlib

pyplot is a matplotlib module which provides a MATLAB-like interface.[6]matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.