#sql video tutorial

[hydralisk98]

My strengths and ambitions

Generic skills and forces:

Research, "Creative" Writing & Analysis (atlases, encyclopedias, reference works...)

Multimedia Collaging and Video Compositing

Analog Media and Digital Data Preservation

Composing tracker music

Technical documentation

(explorable?) multimedia explainers

History (& alternate history)

Information technologies

Historical hardware & software

Sidestream software & "operating" systems

Linguistics?

Casual spirituality topics?

TTRPG crawl campaign setting & systemic designs...

Toybox tools and manifestation games...

Causal collectible card games and expansive board game mods...

Demoscene and game modding...

Vector / Retro Aliased Raster illustrations

Animations & interactive (A?)SVG cartoons

Free, Libre and Open Source movement (open culture, open hardware and open software, et cetera.)

Computer builds and overall customization (especially the looks but also functionality, both virtual and physically informed)

Sensible, caring and detail-oriented.

Specific skills (existing and upcoming) and emphasis points for my career

Page pixel dolls, banner blinkies, panels & other animated GIF graphics...

Printables (blanks, worksheet, guestbooks, greeting cards...)

Stickers & clipart

Stationery (especially legacy/obsoleted types)

Desktop organizers with bundled theme packs (even cursors, icons, sounds, widgets and almost everything else under the sun?)

Monero+Liquid online shop

ClipStudioPaint (upcoming...)

WordPerfect from Corel (upcoming...)

OpenMPT + MilkyTracker (tracker music)

ReNoise (upcoming...)

LibreOffice

GIMP with G'MIC

Krita with G'MIC

GrafX2 (upcoming...)

Kate & KDevelop

Version control with Git (GitHub, GitLab, GitTea...)

Homebrew responsive yet retro stylized HTML5/CSS3 static sites/blogs with Neocities + Hexo (and Jekyll?)

XML+XSL

SQL?

Vim & Emacs

Linux + BSD commands with both Bash and Fish shells

KDE Plasma

Linux/BSD/POSIX certifications?

Common Lisp & Nim (with C bindings?)

WDC & KKIT

SLADE & GZDOOM

Godot + Qodot

Hammer++

Entrepreneurship / autonomous work?

Dreams and projects:

Video rental-store full-stack ticket database

Fiction atlases and themed pointcrawl semi-historical adventures...

Cozy game levels for raycasters and doom-clones

Cozy social spaces in Qodot & Hammer++

Cartoon pitches & short animated explainers

Toybox sets and modular building easy assemblers...

TTRPG campaign setting and supplementary rulesets...

Stylized yesterweb responsive pages

Analog media production (music video and data Digipaks)

SVG stickers, PDF printables and OGG content...

Fully custom developer desktop environment themes & scripted auto-riced workflows

Tutorials, listicles and other long-form content threads...

Raw art files and game-ready asset / clipart bundles

Manifestation / affirmation Tarot-sized card deques

Alternate technological implementations...

Dumbphone / "feature phone", two-way pagers and other dumb specialized devices... designing.

Constructed language(s) with nuanced dialects and poetic audio recordings...

Library economy in diecast car miniature worlds for sci-fi films

My very own guidebooks and curations

Lofi illustrations, podcast covers and album digipaks with feelies...

I hope that does sum up my professional + hobby goals well enough.

[carrernext]

SQL Online Course with Certificate | Learn SQL Anytime

Master SQL from the comfort of your home with our comprehensive SQL online course with certificate. Perfect for beginners and professionals alike, this course is designed to equip you with the skills needed to work confidently with databases and data queries. Through step-by-step video tutorials, interactive assignments, and real-world projects, you’ll learn how to retrieve, manipulate, and analyze data using SQL across platforms like MySQL, PostgreSQL, and Microsoft SQL Server. The curriculum covers core concepts including SELECT statements, joins, subqueries, aggregations, and database functions—making it ideal for aspiring data analysts, developers, and business intelligence professionals. Upon completion, you’ll receive an industry-recognized certificate that showcases your expertise and can be added to your resume or LinkedIn profile. With lifetime access, self-paced modules, and expert support, this online SQL course is your gateway to mastering one of the most in-demand data skills today. Enroll now and become SQL-certified on your schedule.

[pallaviicert]

Introduction to Microsoft Azure Basics: A Beginner's Guide

Cloud computing has revolutionized the way businesses are run, facilitating flexibility, scalability, and innovation like never before. One of the leading cloud providers, Microsoft Azure, is a robust platform with an unparalleled set of services that cover from virtual machines and AI to database management and cybersecurity. Be it a developer, IT expert, or an interested individual curious about the cloud, getting a hold of Azure fundamentals can be your gateway into an exciting and future-proof arena. In this beginner's tutorial, we'll learn what Azure is, its fundamental concepts, and the most important services you should know to begin your cloud journey. What is Microsoft Azure? Microsoft Azure is a cloud computing service and platform that has been developed by Microsoft. It delivers many cloud-based services such as computing, analytics, storage, networking, and other services. These services are made available by selecting and using those for the purpose of building new applications as well as running already existing applications. Launched in 2010, Azure has developed at a lightning pace and now caters to hybrid cloud environments, artificial intelligence, and DevOps, becoming the go-to choice for both enterprises and startups.

Why Learn Azure? • Market Demand: Azure skills are in demand because enterprises use it heavily. • Career Growth: Azure knowledge and certifications can be a stepping stone to becoming a cloud engineer, solutions architect, or DevOps engineer. • Scalability & Flexibility: Solutions from Azure can be offered to businesses of all types, ranging from startups to Fortune 500. • Integration with Microsoft Tools: Smooth integration with offerings such as Office 365, Active Directory, and Windows Server. Fundamental Concepts of Microsoft Azure Prior to services, it would be recommended to familiarize oneself with certain critical concepts which constitute the crux of Azure.

1. Azure Regions and Availability Zones Azure can be had in every geographic area globally, regions being divided within them. Within regions, redundancy and resiliency can be had through availability zones—separate physical data centers within a region. 2. Resource Groups A resource group is a container holding Azure resources that belong together, such as virtual machines, databases, and storage accounts. It helps group and manage assets of the same lifecycle.

3. Azure Resource Manager (ARM) ARM is Azure's deployment and management service. It enables you to manage resources through templates, REST APIs, or command-line tools in a uniform way. 4. Pay-As-You-Go Model Azure has a pay-as-you-go pricing model, meaning you pay only for what you use. It also has reserved instances and spot pricing to optimize costs.

Top Azure Services That Every Beginner Should Know Azure has over 200 services. As a starter, note the most significant ones by categories like compute, storage, networking, and databases. 1. Azure Virtual Machines (VMs) Azure VMs are flexible compute resources that allow you to run Windows- or Linux-based workloads. It's essentially a computer in the cloud. Use Cases: • Hosting applications • Executing development environments • Executing legacy applications

2. Azure App Services It's a fully managed service for constructing, running, and scaling web applications and APIs. Why Use It? • Automatically scales up or down according to demand as well as remains always available • Multilanguage support (.NET, Java, Node.js, Python) • Bundled DevOps and CI/CD 3. Azure Blob Storage Blob (Binary Large Object) storage is appropriate to store unstructured data such as images, videos, and backups. Key Features: • Greatly scalable and secure • Allows data lifecycle management • Accessible using REST API

4. Azure SQL Database This is a managed relational database service on Microsoft SQL Server. Benefits: • Automatic updates and backups • Embedded high availability • Has hyperscale and serverless levels 5. Azure Functions It is a serverless computing service that runs your code in response to events. Example Use Cases: • Workflow automation • Parsing file uploads • Handling HTTP requests 6. Azure Virtual Network (VNet) A VNet is like a normal network in your on-premises environment, but it exists in Azure. Applications: • Secure communication among resources • VPN and ExpressRoute connectivity

• Subnet segmentation for better control

Getting Started with Azure 1. Create an Azure Account Start with a free Azure account with $200 credit for the initial 30 days and 12 months of free-tier services. 2. Discover the Azure Portal The Azure Portal is a web-based interface in which you can create, configure, and manage Azure resources using a graphical interface. 3. Use Azure CLI or PowerShell For command-line fans, Azure CLI and Azure PowerShell enable you to work with resources programmatically. 4. Learn with Microsoft Learn Microsoft Learn also offers interactive, role-based learning paths tailored to new users. Major Azure Management Tools Acquiring the following tools will improve your resource management ability: Azure Monitor A telemetry data gathering, analysis, and action capability for your Azure infrastructure. Azure Advisor Offers customized best practice advice to enhance performance, availability, and cost-effectiveness. Azure Cost Management + Billing Assists in tracking costs and projects costs in advance to remain within budget.

Security and Identity in Azure Azure focuses a great deal of security and compliance. 1. Azure Active Directory (Azure AD) A cloud identity and access management. You apply it to manage identities and access levels of users for Azure services. 2. Role-Based Access Control (RBAC) Allows you to define permissions for users, groups, and applications to certain resources. 3. Azure Key Vault Applied to securely store keys, secrets, and certificates.

Best Practices for Azure Beginners • Start Small: Start with straightforward services like Virtual Machines, Blob Storage, and Azure SQL Database. • Tagging: Employ metadata tags on resources for enhanced organization and cost monitoring. • Monitor Early: Use Azure Monitor and Alerts to track performance and anomalies. • Secure Early: Implement firewalls, RBAC, and encryption from the early stages. • Automate: Explore automation via Azure Logic Apps, ARM templates, and Azure DevOps.

Common Errors to Prevent • Handling cost management and overprovisioning resources lightly. • Not backing up important data. • Not implementing adequate monitoring or alerting. • Granting excessive permissions in Azure AD. • Utilizing default settings without considering security implications. Conclusion

Microsoft Azure is a strong, generic platform for supporting a large variety of usage scenarios—everything from a small web hosting infrastructure to a highly sophisticated enterprise solution. The key to successful cloud computing, however, is an understanding of the basics—ground-level concepts, primitive services, and management tools—is well-served by knowledge gained here. And that's just the start. At some point on your journey, you'll come across more complex tools and capabilities, but from a strong base, it is the secret to being able to work your way through Azure in confidence. Go ahead and claim your free account, begin trying things out, and join the cloud revolution now.

Website: https://www.icertglobal.com/course/developing-microsoft-azure-solutions-70-532-certification-training/Classroom/80/3395

[nmietbbsr]

How to Become a Data Scientist as a CSE Student?

If you’re a CSE (Computer Science and Engineering) student, chances are you’ve already heard the term data science quite a few times. It’s one of the fastest-growing career fields in tech—and for good reason. Companies across the world rely on data to make decisions, and they need skilled people who can make sense of all that information.

So, how do you become a data scientist when you’re still a student? Let’s break it down step by step.

Understand What a Data Scientist Really Does

A data scientist is someone who collects, cleans, and analyzes large amounts of data to help businesses or organizations make smarter decisions. This can involve writing code, using math, working with databases, and communicating findings clearly.

It’s not just about writing programs—it’s also about thinking critically and solving real-world problems with the help of data.

Step 1: Get Your Basics Right

As a CSE student, you already have a head start. Your curriculum likely includes core subjects like programming, algorithms, databases, and computer networks. Pay close attention to:

Python or R (Python is more widely used in data science)

SQL for handling data in databases

Math and Statistics, especially probability, linear algebra, and basic statistics

Data Structures and Algorithms for handling data efficiently

These skills form the foundation of data science work.

Step 2: Learn Data Science Tools and Libraries

Once you’re comfortable with basic programming and math, it’s time to explore tools used by data scientists. You don’t need to master everything at once—start small.

NumPy and Pandas (for handling and analyzing data)

Matplotlib and Seaborn (for creating charts and graphs)

Scikit-learn (for machine learning models)

Jupyter Notebooks (for writing and sharing your code easily)

Plenty of free tutorials and YouTube videos can help you practice these.

Step 3: Take Online Courses and Certifications

While your college of engineering and technology in Bhubaneswar may offer some electives or workshops, adding extra courses can boost your learning. Platforms like Coursera, edX, Udemy, and Kaggle offer beginner-friendly courses in data science and machine learning.

Look for beginner tracks with practical projects. Try courses like:

"Introduction to Data Science in Python"

"Machine Learning" by Andrew Ng

"Python for Data Science and AI" by IBM

These certifications can also strengthen your resume later on.

Step 4: Work on Real Projects

Theory is important, but nothing beats hands-on experience. Try simple projects first:

Analyze sales or weather data

Build a movie recommendation system

Predict house prices using public datasets

Visualize COVID-19 data trends

You can find datasets on sites like Kaggle, UCI Machine Learning Repository, or Google Dataset Search. Try to write a short blog post or upload your project to GitHub so others can see your work.

Step 5: Join Internships and Communities

Real-world experience can help you build both skills and confidence. Look for internships related to data analysis, data science, or even software roles that deal with data.

Also, join online communities like:

Kaggle (for competitions and discussions)

Reddit’s r/datascience

LinkedIn groups focused on analytics

Discord or Telegram groups for peer learning

You’ll learn a lot from seeing what others are doing and sharing your progress too.

Step 6: Build a Portfolio

Before applying for full-time jobs, put together a portfolio. This can include:

2–3 well-documented projects

A GitHub profile with clean code

A short resume highlighting your tools and skills

A LinkedIn profile that reflects your interests in data science

If you're studying at a college of engineering and technology in Bhubaneswar, consider joining tech fests, project expos, or coding clubs that give you space to present your work.

Conclusions

Becoming a data scientist as a CSE student doesn’t require fancy degrees or expensive tools. What you do need is consistency, curiosity, and a clear plan. Start with your basics, explore tools, build small projects, and grow step by step.

The field of data science is full of opportunities—and your classroom is just the beginning.

[codezup]

"Transforming Unstructured Data into Insights with SQL and NoSQL"

Transforming Unstructured Data into Insights with SQL and NoSQL Introduction In today’s data-driven world, organizations are facing an explosion of unstructured data, including text, images, videos, and audio files. This data can provide valuable insights, but it requires specialized tools and techniques to extract meaningful information. In this tutorial, we will explore how to transform…

[cloudolus]

Amazon RDS Performance Insights | Monitor and Optimize Database Performance

Amazon RDS Performance Insights is an advanced monitoring tool that helps you analyze and optimize your database workload in Amazon RDS and Amazon Aurora. It provides real-time insights into database performance, making it easier to identify bottlenecks and improve efficiency without deep database expertise.  

Key Features of Amazon RDS Performance Insights:  

✅ Automated Performance Monitoring – Continuously collects and visualizes performance data to help you monitor database load.   ✅ SQL Query Analysis – Identifies slow-running queries, so you can optimize them for better database efficiency.   ✅ Database Load Metrics – Displays a simple Database Load (DB Load) graph, showing the active sessions consuming resources.   ✅ Multi-Engine Support – Compatible with MySQL, PostgreSQL, SQL Server, MariaDB, and Amazon Aurora.   ✅ Retention & Historical Analysis – Stores performance data for up to two years, allowing trend analysis and long-term optimization.   ✅ Integration with AWS Services – Works seamlessly with Amazon CloudWatch, AWS Lambda, and other AWS monitoring tools.  

How Amazon RDS Performance Insights Helps You:  

🔹 Troubleshoot Performance Issues – Quickly diagnose and fix slow queries, high CPU usage, or locked transactions.   🔹 Optimize Database Scaling – Understand workload trends to scale your database efficiently.   🔹 Enhance Application Performance – Ensure your applications run smoothly by reducing database slowdowns.   🔹 Improve Cost Efficiency – Optimize resource utilization to prevent over-provisioning and reduce costs.  

How to Enable Amazon RDS Performance Insights:   1️⃣ Navigate to AWS Management Console.   2️⃣ Select Amazon RDS and choose your database instance.   3️⃣ Click on Modify, then enable Performance Insights under Monitoring.   4️⃣ Choose the retention period (default 7 days, up to 2 years with paid plans).   5️⃣ Save changes and start analyzing real-time database performance!  

Who Should Use Amazon RDS Performance Insights?   🔹 Database Administrators (DBAs) – To manage workload distribution and optimize database queries.   🔹 DevOps Engineers – To ensure smooth database operations for applications running on AWS.   🔹 Developers – To analyze slow queries and improve app performance.   🔹 Cloud Architects – To monitor resource utilization and plan database scaling effectively.  

Amazon RDS Performance Insights simplifies database monitoring, making it easy to detect issues and optimize workloads for peak efficiency. Start leveraging it today to improve the performance and scalability of your AWS database infrastructure! 🚀  

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[carrernext]

SQL Online Course with Certificate | Learn SQL & Get Certified

Boost your career with our SQL Online Course with Certificate! This comprehensive program is designed for beginners and professionals to master SQL fundamentals, advanced querying, database management, and performance optimization. Learn at your own pace with expert-led video tutorials, interactive exercises, and real-world projects. Upon completion, receive an industry-recognized certificate to showcase your expertise. Whether you're an aspiring data analyst, database administrator, or software developer, this course equips you with in-demand SQL skills. Enroll now and start your journey toward SQL mastery!

[fromdevcom]

Those days are gone when programming could only be done by the likes of Mark Zuckerberg. Now, if you are interested in creating a new website, make an existing website more interactive or just redesign a WordPress theme, there are websites that offer beginner level training for most of these requirements. There are websites that cater to you as per your preference – for beginners who have little to no coding experience, there are websites that utilize interactive teaching methods that make learning easy and fun. For people who have some experience in coding, there are more straightforward websites that deal more cold with hard codes and do not rely on fancy teaching methods. Below are best websites to learn programming from: Codeacademy Codeacademy.com is one of the most popular to learn programming from. You can learn programming with JavaScript, PHP, Web Fundamentals, Python and Ruby among others. There are panels when you start a course that explains the fundamentals and also lets you practice by writing code. Codeschool Codeschool.com is another great website for a beginner to learn programming in a fun, interactive manner. The courses offered are Ruby, JavaScript, HTML/CSS, Python, iOS and Git. This website is more popular for offering more in-depth courses that enable you to become an expert in that particular program. Codeavengers Codeavengers.com is a website that lets you learn to code and build websites, apps and games in a fun and effective way. The courses offered are – HTML/CSS, JavaScript, Game Development, jQuery and Web Development. This website is again a very useful website for people that are new to the programming world. HTML5 rocks Html5rocks.com is a Google project which was launched in 2010. This is an advanced website for people who already have a basic understanding of coding and website development; features tutorials, resources and articles for the latest updates. Treehouse Treehouse.com is a website where a user can learn from over 1000 videos created by experts and covers topics like web designing, coding, building an app and business among others. Freecodecamp Freecodecamp.com is website that allows you to learn and work with other developers to create an app which could be used by nonprofits to power their growth – which can actually help you land a job! Udacity Udacity.com is another website to learn programming that offers programs that is calls “nano degrees”. The courses have been designed with partnership with Industry leaders like Google. CodeHS Codehs.com is another fun website to learn programming from where they use puzzles and problem solving challenges to learn programming. Hackpledge.org This is website which features a community of members that take the “pledge” to offer help to the next generation of coders or learn from some of the best in the industry and take your craft to the next level. Khanacademy Khanacademy.org is a website that was started in 2006 and features courses not only for general subjects but also offers courses on computer programming like JavaScript, SQL and HTML/CSS. Helen Evans, Marketing manager of JobTonic, has been in the career development field for 5 years. She likes to share interesting tips to help people find their dream job. Her goal is to share what she has learned about searching job. Dream job is closer than you imagine!

[samurai-teacher]

🚀 HACKING with Parrot OS in Docker! (EASY Windows 11 Setup) 🚀 hashtag#ParrotOS hashtag#Docker hashtag#HackingTutorial hashtag#EthicalHacking hashtag#NmapDemo In this video, I’ll show you how to set up Parrot OS, a powerful Linux distro for ethical hacking and cybersecurity, inside a Docker container on Windows 11. This lightweight setup is perfect for running hacking tools without the need for bulky virtual machines! 👉 Here’s what you’ll learn in this video: How to install Docker on Windows 11. Pulling and running the Parrot OS container. We are troubleshooting common Docker issues, which include running containers with the --privileged flag. We are installing tools such as Nmap in the Parrot OS container. This is a hands-on demonstration of utilizing Nmap for conducting network scans. 💡 Key Commands from the Video: Pull the Parrot OS Docker image: bash Copy code docker pull parrotsec/core Run the container with elevated privileges to troubleshoot tool permissions. css Copy code docker run -it --privileged --name parrotos parrotsec/core bash Install Nmap inside the container: sql Copy code apt update && apt install nmap -y Scan a target using Nmap: Copy code nmap scanme.nmap.org 🎯 By the end of this tutorial, you’ll have a fully functional Parrot OS container with the tools you need to explore cybersecurity tasks securely and efficiently! ⚠️ Important Reminder: Always use ethical practices and only scan systems you own or have permission to test. 📌 Like, comment, and subscribe for more practical tutorials on hacking, cybersecurity, and tech tools! Let me know in the comments if you want me to cover other tools or topics next! ✨❤️ Your Support Ignites Our Passion! ❤️✨ 🍵 BuyMeACoffee: https://lnkd.in/g2xVtifJ ▶️ YouTube: https://lnkd.in/gjHhgH6Z 🎵 TikTok: https://lnkd.in/giqyvF57 🌐 Website: https://lnkd.in/gCGpf8Gr 📘 Facebook: https://lnkd.in/gi9M53uU 💼 LinkedIn: https://lnkd.in/gnQNRCxv 📸 Instagram: https://lnkd.in/gKEptjaW 🛒 Amazon Recommendations: https://amzn.to/3XMR1qr Hidden Camera Products: https://amzn.to/3Zig0SJ

[suhailms]

Master .NET Development: From Beginner to Expert

Are you ready to dive into the world of software development? Our Master .NET Development course is designed to provide a comprehensive, step-by-step learning experience, inspired by the engaging and user-friendly approach of platforms like JazAcademy.

Course Overview This course offers an in-depth exploration of .NET, one of the most widely-used frameworks for building powerful and scalable applications. Whether you're a beginner looking to understand the basics or an experienced developer aiming to sharpen your skills, this course will guide you through every stage of the development process.

What You'll Learn In this course, we’ll take you on a journey from the fundamentals of .NET all the way to advanced topics, similar to JazAcademy’s emphasis on layering knowledge for maximum retention. You’ll explore:

Introduction to .NET: Understand the basics, from what .NET is to how it works across different platforms (Windows, macOS, and Linux).

C# Programming Language: Learn C#, the primary language for .NET development. You’ll master key programming concepts such as object-oriented programming, LINQ, async/await, and more.

Building Web Applications with ASP.NET: Dive into web development with ASP.NET Core, learning to build robust and scalable web applications.

Working with Databases: Learn how to connect and interact with databases using Entity Framework and SQL Server.

UI Development: Build rich desktop applications using WPF (Windows Presentation Foundation) and other UI frameworks.

Deploying .NET Applications: Master deployment techniques for your applications and understand how to take your project live.

Why Take This Course? This course is designed to be hands-on, with plenty of opportunities to practice and apply what you’ve learned, just like JazAcademy’s philosophy of integrating theory with practice. Expect to work on real-world projects, quizzes, and assignments that will help solidify your knowledge.

Key Features:

Interactive Learning: A variety of learning formats, including video tutorials, interactive coding exercises, and live coding sessions, similar to JazAcademy’s dynamic teaching methods.

Progress Tracking: Keep track of your progress with milestones, just like JazAcademy offers detailed learning paths to help you stay on course.

Community Engagement: Join a thriving community of learners and developers to share your progress, get feedback, and solve challenges together.

Who Is This Course For? This course is perfect for anyone wanting to learn or improve their .NET development skills. Whether you're a beginner starting from scratch or an experienced developer looking to enhance your skillset, you’ll find the material accessible, challenging, and rewarding.

Get Started Today! Don’t miss out on this opportunity to become a .NET developer. With JazAcademy’s inspired approach, you’ll be on the path to mastering the tools and techniques used by developers worldwide. Start your .NET journey now and see what you can achieve!

[subb01]

How to Prepare for a Data Analytics Course: A Comprehensive Guide

Data analytics is a field that’s not only in demand but also vital for decision-making in today’s data-driven world. Whether you're looking to upskill for your career or start a new journey, preparing well for a data analytics course is crucial to getting the most out of your learning experience. In this blog, we’ll walk you through practical tips to help you prepare and excel in your data analytics course.

1. Understand the Basics of Data Analytics

Before starting any course, it's beneficial to have a foundational understanding of what data analytics entails. Data analytics involves collecting, processing, and interpreting data to make informed decisions. Start by familiarizing yourself with basic concepts like data types, data collection methods, and the importance of data visualization.

Tip: Watch introductory videos to get an overview of the field and develop a solid base. These YouTube live sessions can be excellent starting points:

Intro to Data Analysis

What is Data Analytics?

2. Brush Up on Your Mathematical Skills

Data analytics heavily relies on statistics and basic mathematics. Revising these subjects will enable you to better understand analytical concepts like probability, distributions, hypothesis testing, and regression analysis.

Recommendations:

Focus on high school-level statistics and algebra.

Utilize free online courses or math refresher tutorials on platforms like Khan Academy.

3. Familiarize Yourself with Common Data Analytics Tools

Being acquainted with the tools used in data analytics can give you a significant head start. Some common tools include:

Excel: Used for basic data analysis and visualization.

SQL: Essential for handling and querying large datasets.

Python or R: Popular programming languages for data manipulation and advanced analytics.

Tableau: A powerful tool for data visualization.

Tip: Try introductory tutorials to practice using these tools. Explore tutorials on YouTube like:

Mastering Data Visualization

4. Develop Problem-Solving Skills

Data analytics is not just about handling data; it’s about finding meaningful insights to solve problems. Cultivating a problem-solving mindset will enable you to think critically about data-driven challenges.

Exercise:

Practice by taking real-world datasets and formulating questions they could answer.

Engage in puzzles or logic games to improve analytical thinking.

5. Learn Basic Programming

While some courses may not require coding, having an understanding of basic programming concepts will be immensely helpful, especially when working with large data sets and creating automation scripts.

Languages to Focus On:

Python: Widely used due to its simplicity and extensive libraries like Pandas and NumPy.

R: Particularly useful for statistical analysis and data visualization.

Resources:

Check out free courses on platforms like Coursera, or view live programming tutorials on YouTube.

6. Join Data Analytics Communities

Joining communities and engaging with peers can provide motivation and support as you prepare for and complete your course. Online communities such as Kaggle, Stack Overflow, and dedicated LinkedIn groups are excellent for asking questions, finding resources, and networking with professionals.

7. Get a Hands-On Approach with Data Projects

Theoretical knowledge is valuable, but practical experience is essential. Start with small projects, such as analyzing publicly available datasets or participating in beginner-level data analytics competitions.

Tip: Engage with video content that showcases real-world data project walkthroughs, like:

Practical Data Analysis Example

8. Set Clear Learning Goals

Identify what you want to achieve with your data analytics course. Are you looking to advance in your current job, switch careers, or just expand your knowledge? Setting clear goals will help you stay motivated and focused.

Final Thoughts

Preparation is key to successfully navigating any learning journey. By following these tips and leveraging the resources mentioned, you’ll set yourself up for success in your data analytics course. Be consistent, practice regularly, and stay curious to make the most out of your learning experience.

Explore More: Don’t miss out on insightful live sessions that can boost your understanding of data analytics. Watch these informative sessions:

Data Analytics Introduction

Deep Dive into Data Analysis Tools

By taking these preparatory steps, you’ll be ready to dive into your data analytics course with confidence!

[fromdevcom]

Data and artificial intelligence are two of the most prominent terms in this day and age. The rapid development of the technology surrounding data and AI has changed how we approach problems related to them. One of the best ways to deal with AI and data is with the help of cloud computing. Thanks to cloud computing, we can easily simplify a majority of tasks.One such fabulous cloud computing technology that offers the best solutions to dealing with both AI and data lakes is IBM Cloud. In this article, we will cover the basics of how to get started and comfortable with the IBM cloud environment and how to perform numerous applications and utilities. Some of the options available on IBM Cloud allow users to develop high-quality projects with ease. Let us begin by exploring these features and characteristics from scratch. Getting started on IBM Cloud is fairly easy due to the vast array of tutorials and video guides available on the internet. The IBM Cloud platform is also extremely user-friendly so that all users can develop and implement their project ideas. Do a simple Google search, and you will land upon the IBM Cloud website. Create your account or log in if you already have an account. Note that one of the prerequisites is that you must be at least sixteen years of age and must agree to follow their terms of use.Once you successfully log in to your account, you will come to the IBM Cloud dashboard. On this page, you will see a bunch of icons and functionalities that you can implement to build projects. Find the blue icon labeled ‘create a new resource’ towards the top-right corner of the window. Clicking the ‘create resource’ menu option will direct you to the following page. Here, you can select resource options like Cloud Foundry or the Watson Studio for performing your required functions and operations. I will quickly demonstrate how you can set up your Watson Studio. Click on the icon as shown below to enter the next step of your creation process.  Click on Watson Studio, and you will be asked to choose your preferred plan depending on your monetary limits. I have chosen the free pricing option, which is suitable for most simple tasks. It is a great starting point for first-time users of the IBM Cloud services. However, we will use the IBM Cloud Command Line Interface (CLI) to perform these functions and build our data lake and object storage with Jupyter Notebooks and Watson Studio directly.Now, with the help of IBM Cloud’s object storage, data lakes, and Jupyter Notebooks, I will demonstrate how to perform the operations of data lakes and AI. We can utilize object storage and data lakes to store large quantities of information as raw data files. Once we link the query data to the object storage with SQL queries, we can implement the refining and analysis of the data in the Jupyter Notebooks of the IBM Watson Studio.Below are some code samples to get started with working on AI and data lakes. In the first few code blocks, we are implementing certain essential services to get started with the various functionalities. ibmcloud loginibmcloud target --cfLet us now initialize all the default groups for the working process. ibmcloud resource groupsibmcloud target -g The next few steps include creating all the necessary elements for operating on data lakes, object storage, and the Jupyter Notebooks. The first command will create the data lake in the cloud with the object storage features. The second command will create additional SQL queries to operate on the data. Finally, the third command will create the IBM Watson Studio. ibmcloud resource service-instance-create data-lake-cos cloud-object-storage lite globalibmcloud resource service-instance-create data-lake-sql sql-query lite us-southibmcloud resource service-instance-create data-lake-studio data-science-experience free-v1 us-southAfter this, you can work on uploading and working data elements by combining SQL queries with Jupyter Notebooks.  ConclusionIn this article,

we have discussed the high potential that IBM Cloud holds for solving problems related to AI and data science. It offers numerous tools and technologies to users to explore and build high-level projects. The best part about working with IBM Cloud is that there are many tutorial guides, certifications, and resources available to each individual to benefit from and utilize.For further information and reading, I recommend checking out a few article resources. You can check out articles on working with object storage and Jupyter Notebooks to gain more insight into how you can handle data objects. I also highly recommend checking out the reference article on the IBM Cloud Data Lake website to understand this topic better. You can also check out an IBM Cloud YouTube series.

[greatonlinetrainingsposts]

Is It Necessary to Have Programming Experience Before Taking a Full SAS Course?

When considering whether to take a SAS programming full course, one of the most common questions for potential learners is whether prior programming experience is required. While having a background in programming can certainly be helpful, it is not an absolute necessity for learning SAS programming. This article explores the factors to consider, the importance of programming experience in SAS online training, and how anyone—regardless of their previous experience—can successfully master SAS.

Understanding SAS Programming

SAS programming is a powerful software suite used for data management, statistical analysis, and business intelligence. It is widely used across industries like healthcare, finance, marketing, and government due to its ability to handle large datasets, perform advanced analytics, and generate detailed reports. SAS offers a user-friendly environment for data scientists, analysts, and statisticians to work with data, making it an excellent tool for both beginners and experienced professionals.

A SAS programming full course typically covers a variety of topics, including data manipulation, data analysis, report generation, and advanced statistical techniques. While SAS programming uses its own specific syntax and environment, it shares similarities with other programming languages such as SQL and Python, making it easier for those with experience in those languages to adapt. But the real question remains: Do you need to be familiar with programming to learn SAS?

 The Role of Programming Experience in Learning SAS

Before diving into whether SAS programming requires programming experience, it's important to understand what skills are required to succeed in a SAS programming full course. Here's a breakdown:

1. Basic Computer Literacy:

While SAS programming itself doesn't require advanced technical skills, it does require a basic level of computer literacy. Understanding how to use computers, navigate file systems, and manage software installation will be important for any course. As long as you're comfortable using a computer, accessing training materials, and following along with video tutorials or live sessions, you will be able to handle the foundational aspects of a SAS course.

2. Data Analysis Mindset:

The core of SAS programming revolves around data analysis. Having a strong understanding of data concepts—such as datasets, variables, observations, and data types—will make it easier to grasp the functionality of SAS. Some experience with basic statistics or familiarity with how data is structured (in spreadsheets or databases) can provide a helpful foundation. However, even without prior experience, anyone with a curiosity about data can learn SAS programming.

3. No Need for Advanced Programming Skills:

Unlike languages like Python or Java, which require knowledge of programming concepts like loops, functions, and object-oriented programming, SAS programming is relatively easy for beginners to pick up. SAS programming focuses on procedures and statements that manipulate data, making it more accessible to users without a programming background. The software uses an easy-to-understand syntax, and many of the tasks, such as creating reports or running analyses, can be done with a limited amount of coding.

For those new to programming, SAS online training typically begins with basic concepts, gradually progressing toward more advanced techniques. A SAS programming tutorial often introduces step-by-step instructions, helping beginners understand the syntax and logic used in the language. In these introductory lessons, you’ll learn how to import data, clean data, and run basic analyses—all without needing complex programming knowledge.

4. Learning Path and Resources:

A SAS programming full course is designed with learners of all levels in mind. If you're new to programming, many courses start with the basics, offering a gradual progression toward more complex topics. For example, in the initial stages, you may focus on simple commands and syntax, progressing to more intricate procedures and statistical analysis later in the course. In addition to tutorials, learners have access to a variety of resources such as sample code, practice exercises, and dedicated forums where you can ask questions and clarify doubts.

For those with some experience in programming, these courses will serve as an opportunity to transition to the SAS programming environment. Having an understanding of logic and computational thinking will certainly accelerate your learning process, but it's not a requirement.

Advantages of Taking a SAS Course with No Programming Background

If you're considering SAS online training and don't have any previous programming experience, here are some advantages of learning SAS without prior knowledge of programming languages:

1. Structured Learning: 

The key to mastering SAS programming (or any new skill) is a well-structured learning path. A SAS programming full course typically starts from the very basics and provides a clear progression, so even beginners can follow along. You don’t need to worry about missing foundational concepts, as these will be introduced at the right pace.

2. User-Friendly Interface:

Unlike traditional programming languages, SAS offers an intuitive and user-friendly interface. It’s designed to make data analysis accessible to non-programmers. The built-in procedures and wizards in SAS make it easier for users to accomplish tasks without needing to write complex code.

3. Learn by Doing:

With SAS programming, much of the learning process is hands-on. Through assignments and practice exercises, you'll have opportunities to apply what you've learned in real-world scenarios. Whether you’re importing data, cleaning data, or generating reports, you'll gain valuable experience through practice, even without extensive programming knowledge.

4. Support for Beginners:

Most SAS online training courses offer ample support for beginners. With video tutorials, live webinars, and interactive exercises, you can quickly become comfortable with the software. Many courses also provide access to instructors or mentors who can assist you if you're struggling with a concept. This support system ensures that you never feel lost or overwhelmed.

When Programming Experience Can Be Helpful

While programming experience isn’t required, having some basic familiarity with logic and coding concepts can help make learning SAS faster and easier. If you have experience with any of the following, you may find it easier to adapt to SAS:

1. SQL: Since SAS programming involves working with datasets, having a basic understanding of querying databases with SQL can help when it comes to understanding how data is manipulated.

2. Python or R: If you’ve worked with Python or R, you will recognize similarities in how SAS programming handles data structures and statistical procedures. The logic of data analysis is quite universal, so experience with one language may help you understand SAS concepts faster.

3. Excel: If you have experience with Excel or other spreadsheet software, you’ll already be familiar with many of the concepts used in SAS programming, such as data tables, rows, columns, and basic statistics.

While these skills may give you an edge, they're not strictly necessary to succeed in learning SAS. SAS programming is designed to be beginner-friendly, and there are plenty of resources available to help you succeed regardless of your previous experience.

 Conclusion: Can You Learn SAS Without Programming Experience?

In conclusion, SAS programming does not require previous programming experience, and it’s absolutely possible to take a SAS programming full course without a coding background. A well-structured course will guide you from the basics of the software to more advanced techniques, with support along the way. The most important thing is to have a curiosity and interest in data analysis, as SAS is a tool designed to make complex tasks accessible to beginners.If you’re considering taking SAS online training, rest assured that your success doesn’t depend on knowing how to code. With the right resources and a step-by-step approach, anyone can learn SAS programming and apply it to real-world data challenges. Whether you're completely new to programming or have some familiarity with data analysis tools, a SAS programming full course will equip you with the skills necessary to thrive in today’s data-driven world.

[arundigitalmarketer]

Database Courses

Introduction

Databases are essential for storing, organizing, and managing data in today's digital age. A database course equips you with the skills to design, implement, and maintain efficient and reliable databases.

Types of Databases

Relational Databases: Organize data in tables with rows and columns. Examples include MySQL, PostgreSQL, and Oracle.

NoSQL Databases: Store data in a flexible and scalable format. Examples include MongoDB, Cassandra, and Redis.

Key Skills Covered in a Database Course

Database Concepts: Understand database terminology, normalization, and data integrity.

SQL (Structured Query Language): Learn to query, manipulate, and manage data in relational databases.

Database Design: Design efficient database schemas and data models.

Performance Optimization: Optimize database performance through indexing, query tuning, and data partitioning.

Data Modeling: Create data models to represent real-world entities and their relationships.

Database Administration: Manage database security, backups, and recovery.

NoSQL Databases (Optional): Learn about NoSQL databases and their use cases.

Course Structure

A typical database course covers the following modules:

Introduction to Databases: Overview of database concepts and their importance.

Relational Database Design: Learn about database normalization and ER (Entity-Relationship) diagrams.

SQL Fundamentals: Master SQL syntax for querying, inserting, updating, and deleting data.

Database Administration: Understand database security, backup and recovery, and performance tuning.

Advanced SQL: Explore advanced SQL features like subqueries, joins, and window functions.

NoSQL Databases (Optional): Learn about NoSQL databases and their use cases.

Database Performance Optimization: Optimize database queries and indexes for efficient performance.

Case Studies: Apply learned concepts to real-world database scenarios.

Choosing the Right Course

When selecting a database course, consider the following factors:

Database type: Choose a course that focuses on relational databases or NoSQL databases based on your needs.

Course format: Choose between online, in-person, or hybrid formats based on your preferences and learning style.

Instructor expertise: Look for instructors with practical experience in database administration and design.

Hands-on projects: Prioritize courses that offer hands-on projects to gain practical experience.

Community and support: A supportive community of students and instructors can be valuable during your learning journey.

Career Opportunities

A database course can open doors to various career paths, including:

Database Administrator (DBA)

Data Analyst

Data Engineer

Software Developer

Business Intelligence Analyst

Popular Online Platforms for Database Courses

Udemy: Offers a wide range of database courses for all levels.

Coursera: Provides specialized database courses from top universities.

Codecademy: Offers interactive database lessons and projects.

edX: Provides database courses from top universities, including MIT and Harvard.

Pluralsight: Offers comprehensive database courses with video tutorials and hands-on exercises.

Additional Tips

Practice regularly: Consistent practice is key to becoming proficient in database management.

Stay updated: The database landscape is constantly evolving, so it's important to stay up-to-date with the latest technologies and trends.

Build a portfolio: Showcase your database projects and skills on a personal website or portfolio platform.

Network with other database professionals: Attend meetups, conferences, and online communities to connect with other database professionals and learn from their experiences.

By following these steps and continuously learning and practicing, you can become a skilled database professional.

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How to Get Into Data Science Expert From Non-Tech Background

Data Science is one of the most sought-after fields today, offering lucrative career opportunities and the chance to work on cutting-edge technologies. However, many people from non-technical backgrounds wonder if it's possible to transition into this field. The answer is a resounding yes! With the right approach and resources, anyone can become a successful Data Scientist. This guide will walk you through the process, from understanding why Data Science is an excellent career choice to detailed steps on how to make the transition.

Why Data Science?

Data Science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Here are some compelling reasons to consider a career in Data Science:

High Demand: With the exponential growth of data, companies are increasingly relying on data-driven decisions, leading to a surge in demand for Data Scientists.

Lucrative Salaries: Data Scientists are among the highest-paid professionals in the tech industry.

Versatility: Data Science skills are applicable across various industries, from healthcare to finance to entertainment.

Innovation: Work on exciting projects involving machine learning, artificial intelligence, and big data.

Skills Required to Become a Data Scientist

Transitioning into Data Science requires acquiring a specific set of skills. Here are the essential skills you need to develop:

Technical Skills

Programming Languages: Proficiency in languages like Python and R is crucial.

Statistics and Mathematics: A solid understanding of statistics, probability, and linear algebra.

Data Manipulation and Analysis: Skills in using tools like Pandas, NumPy, and SQL.

Machine Learning: Knowledge of machine learning algorithms and frameworks like TensorFlow and Scikit-Learn.

Data Visualization: Ability to visualize data using tools like Matplotlib, Seaborn, and Tableau.

Soft Skills

Problem-Solving: Strong analytical and problem-solving skills to make sense of complex data.

Communication: Ability to convey technical information to non-technical stakeholders.

Curiosity: A natural curiosity to explore data and uncover hidden patterns.

Is It Hard to Transition into Data Science from Non-Tech Background?

Transitioning into Data Science from a non-tech background is challenging but entirely feasible. The key is dedication and structured learning. Many successful Data Scientists have made the switch from non-technical fields such as business, healthcare, and social sciences. With a well-planned roadmap and the right resources, you can overcome the initial hurdles and build a successful career in Data Science.

How to Get Into Data Science From Non-Tech Background – Step-by-Step Roadmap

Here's a detailed step-by-step roadmap to guide you through your transition into Data Science:

Understand the Basics: Start with understanding the fundamentals of Data Science. Read articles, watch introductory videos, and attend webinars to get a sense of what Data Science entails.

Enroll in a Data Science Course: Enroll in a structured Data Science training program. iconGen IT Solutions offers comprehensive Data Science training in Chennai that covers all the essential skills needed to become a Data Scientist.

Learn Programming: Begin with learning Python, as it is the most widely used language in Data Science. Utilize online resources, tutorials, and practice coding regularly.

Master Statistics and Mathematics: Brush up on your knowledge of statistics and mathematics. Focus on topics such as probability, descriptive statistics, inferential statistics, and linear algebra.

Work on Data Manipulation and Analysis: Learn to manipulate and analyze data using libraries such as Pandas and NumPy. Practice with datasets to get comfortable with data wrangling.

Dive into Machine Learning: Understand the basics of machine learning algorithms and how to implement them using frameworks like Scikit-Learn and TensorFlow.

Develop Data Visualization Skills: Learn how to create compelling data visualizations using tools like Matplotlib, Seaborn, and Tableau. Visualization is crucial for communicating your findings effectively.

Build a Portfolio: Start working on projects to build a portfolio. Showcase your skills by solving real-world problems and publishing your work on platforms like GitHub.

Network with Professionals: Join Data Science communities, attend meetups, and network with professionals in the field. Networking can provide valuable insights and job opportunities.

Apply for Internships and Jobs: Apply for internships or entry-level positions to gain practical experience. Leverage your portfolio and network to find opportunities.

Continuous Learning: Data Science is a rapidly evolving field. Stay updated with the latest trends and continuously enhance your skills by taking advanced courses and certifications.

Entry-Level: ₹6,00,000 - ₹8,00,000 per annum

Mid-Level: ₹10,00,000 - ₹15,00,000 per annum

Senior-Level: ₹20,00,000 - ₹30,00,000+ per annum

Entry-Level: $70,000 - $90,000 per annum

Mid-Level: $100,000 - $130,000 per annum

Senior-Level: $150,000 - $200,000+ per annum