This guide gives you 91 ways to practice Python — from quick exercises to real projects and helpful courses. Whether you’re a beginner or preparing for a job, there’s something here for you.

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Table of Contents

1. Hands-On Courses
2. Free Exercises
3. Projects
4. Online Tutorials

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Hands-On Courses

Some Python programming courses let you learn and code at the same time. You read a short lesson, then solve a problem in your browser. It’s a fast, hands-on way to learn.

Each course below includes at least one free lesson you can try.

Python Basics Courses

• Python Syntax & Variables
• For Loops & Dictionaries
• Functions & Jupyter Notebook

Data Analysis & Visualization Courses

• Pandas & NumPy Fundamentals
• Data Visualization Fundamentals
• Storytelling with Data Visualization

Data Cleaning Courses

• Data Cleaning & Analysis
• Advanced Data Cleaning Techniques
• Data Cleaning Guided Project

Machine Learning Courses

• Intro to Supervised Learning
• Intro to Unsupervised Learning
• Linear Regression Modeling
• Gradient Descent Modeling
• Logistic Regression Modeling
• Decision Trees & Random Forests Modeling
• Optimizing ML Models

AI & Deep Learning Courses

• APIs for AI
• LLM Chatbots
• Deep Learning with TensorFlow

Probability & Statistics Courses

• Intro to Statistics
• Intermediate Statistics
• Probability Basics
• Conditional Probability

Hypothesis Testing

These courses are a great way to practice Python online, and they're all free to start. If you're looking for more Python courses, you can find them on Dataquest's course page.

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Free Python Exercises

Exercises are a great way to focus on a specific skill. For example, if you have a job interview coming up, practicing Python dictionaries will refresh your knowledge and boost your confidence.

Each lesson is free to start.

Beginner Python Exercises

• Basic mathematical operators — Use Python to perform calculations and printing results to the screen.
• Variables and data types — Work with variables and doing calculations with variables.
• Lists and loops — Practice using Python lists and for loops.
• Dictionaries 1 — Use dictionaries in Python.
• Dictionaries 2 — More practice with dictionaries and frequency tables.
• Lists — Practice using lists in Python.
• Conditional statements (if-else) — Use conditional statements in Python.
• Sets — Practice using sets in Python.

Intermediate Python Programming

• Python functions — Define and call functions.
• Intermediate Python functions — Practice more advanced usage of functions like returning multiple values.
• Object oriented programming (OOP) — Define classes, methods, and attributes.

Data Handling and Manipulation with NumPy

• Index selection — Select values from ndarrays.
• Creating ndarrays — Create ndarrays with specific values and shapes.
• Ndarray methods — Use ndarray methods to perform calculations.
• Broadcasting — Work with ndarrays with different shapes and using broadcasting to create ndarrays.
• Boolean masks — Select data from ndarrays using boolean masks
• Datatypes — Work with NumPy datatypes
• Sorting — Practice sorting ndarrays
• Stacking and splitting — Stack and split ndarrays

Data Handling and Manipulation with pandas

• Pandas series — Use and build pandas series.
• Creating and manipulating dataframes — Create and manipulate pandas dataframes.
• Selecting data with pandas — Select data from dataframes.
• Loading and exploring data — Load data into dataframes and explore it.
• Pandas boolean masks — Use boolean masks to select data from dataframes.
• Pandas data cleaning — Clean data in a dataframe.

Data Analysis

• Cleaning and preparing data — Write functions to remove incorrect characters and fill missing values.
• Data analysis basics — Manipulate data from CSV files using Python dictionaries and functions.
• Working with dates and times — Practice with the datetime module in Python.

Complexity and Algorithms

• Time complexity of algorithms — Identify the type of time complexity of Python functions.
• Constant time complexity — Find the constant time complexity of functions.
• Logarithmic complexity — Practice finding the logarithmic time complexity of functions.
• Sorting algorithms — Create and work with sorting algorithms in Python.
• Space complexity — Practice space complexity by writing Python functions.
• Explore our full library of Python practice problems to continue improving your skills.

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Python Projects

Projects are one of the best ways to practice Python. Doing projects helps you remember syntax, apply what you’ve learned, and build a portfolio to show employers.

Here are some projects you can start with right away:

Beginner Projects

• App Market Analysis (free) — Analyze historical data to see which apps attract the most users.
• Word Guessing Game (free) — Create an interactive word-guessing game in Python.
• Garden Simulator Game (free) — Build a text-based game using object-oriented programming and randomness.
• Food Ordering App — Create a simple, interactive food ordering app with Python.

Data Analysis Projects

• Movie Ratings Analysis (free) — Check for bias in movie ratings data.
• Hacker News Data (free) — Analyze Hacker News posts with Python string handling and data management.
• eBay Car Sales — Work with scraped car sales data using Python.
• Traffic Analysis on I-94 — Visualize traffic data using pandas and Jupyter Notebook.
• Exchange Rate Visualizations — Create multiple plots to tell a story with data.
• Employee Exit Surveys — Analyze survey data to answer stakeholder questions.
• NYC High School SAT Analysis — Explore SAT performance by demographics using scatter plots and maps.

Data Engineering Projects

• Fast CSV Queries (free) — Build an inventory system with efficient Python data structures.
• Wikipedia Data Analysis (free) — Search and process large datasets using Python and MapReduce.
• Crime Reports Database — Build a PostgreSQL database with proper tables and schemas.

Machine Learning & AI Projects

• Predict Heart Disease (free) — Build a model to predict patient risk.
• Predict Insurance Costs — Use Python linear regression to estimate insurance costs.
• Dynamic AI Chatbot — Create a chatbot that can track conversation history and switch personas.

If none of these spark your interest, there are plenty of other Python projects to try.

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Online Python Tutorials

If exercises, courses, or projects aren’t your thing, blog-style tutorials are another way to learn Python. They’re great for reading on your phone or when you can’t code directly.

Core Python Concepts (Great for Beginners)

• Strings
• Dictionaries
• Data Structures
• Classes
• Lists
• If Statements
• Complete Python Guide — A collection of tutorials, practice problems, cheat sheets, guided projects, and FAQs.

Intermediate Techniques

• Datetime
• Ternary Operator
• Subprocess
• Math Module
• Reading Files
• Lambda Functions

Data Analysis & Data Science

• Reset Index in pandas
• GroupBy in pandas
• Getting Started with APIs
• Introduction to Keras
• Support Vector Machines (SVMs)
• NumPy, pandas & Data Visualization Guide — A collection of tutorials, practice problems, cheat sheets, guided projects, and FAQs.
• Data Cleaning Guide — A collection of tutorials, practice problems, cheat sheets, guided projects, and FAQs.

The web is full of thousands of beginner Python tutorials. Once you know the basics, you can find endless ways to practice Python online.

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FAQs

Where can I practice Python programming online?

1. Dataquest.io: Offers dozens of free interactive practice questions, lessons, project ideas, walkthroughs, tutorials, and more.
2. HackerRank: A popular site for interactive coding practice and challenges.
3. CodingGame: A fun platform that lets you practice Python through games and coding puzzles.
4. Edabit: Provides Python challenges that are great for practice or self-testing.
5. LeetCode: Helps you test your skills and prepare for technical interviews with Python coding problems.

How can I practice Python at home?

1. Install Python on your machine.

You can download Python directly here, or use a program like Anaconda Individual Edition that makes the process easier. If you don’t want to install anything, you can use an interactive online platform like Dataquest and write code right in your browser.

2. Work on projects or practice problems.

Find a good Python project or some practice problems to apply what you’re learning. Hands-on coding is one of the best ways to improve.

3. Make a schedule.

Plan your practice sessions and stick to them. Regular, consistent practice is key to learning Python effectively.

4. Join an online community.

It's always great to get help from a real person. Reddit has great Python communities, and Dataquest's Community is great if you're learning Python data skills.

Can I practice Python on mobile?

Yes! There are many apps that let you practice Python on both iOS and Android.

However, mobile practice shouldn’t be your main way of learning if you want to use Python professionally. It’s important to practice installing and working with Python on a desktop or laptop, since that’s how most real-world programming is done.

If you’re looking for an app to practice on the go, a great option is Mimo.

With AI advancing so quickly, should I still practice Python?

Absolutely! While AI is a powerful support tool, we can’t always rely on it blindly. AI can sometimes give incorrect answers or generate code that isn’t optimal.

Python is still essential, especially in the AI field. It’s a foundational language for developing AI technologies and is constantly updated to work with the latest AI advancements.

Popular Python libraries like TensorFlow and PyTorch make it easier to build and train complex AI models efficiently. Learning Python also helps you understand how AI tools work under the hood, making you a more skilled and knowledgeable developer.

There are lots of great reasons why you should learn Microsoft Power BI. Adding Power BI to your resume is a powerful boost to your employability—pun fully intended!

But once you've decided you want to learn Power BI, what's the best way to actually do it? This question matters more than you might think. With so many learning options available—from expensive bootcamps to free YouTube tutorials—choosing the wrong approach can cost you time, money, and motivation. If you do some research online, you'll quickly discover that there are a wide variety of options, and a wide variety of price tags!

The best way to learn Power BI depends on your learning style, budget, and timeline. In this guide, we'll break down the most popular approaches so you can make an informed decision and start building valuable data visualization skills as efficiently as possible.

How to learn Power BI: The options

In general, the available options boil down to various forms of these learning approaches:

1. In a traditional classroom setting
2. Online with a video-based course
3. On your own
4. Online with an interactive, project-based platform

Let’s take a look at each of these options to assess the pros and cons, and what types of learners each approach might be best for.

1. Traditional classroom setting

One way to learn Microsoft Power BI is to embrace traditional education: head to a local university or training center that offers Microsoft Power BI training and sign up. Generally, these courses take the form of single- or multi-day workshops where you bring your laptop and a teacher walks you through the fundamentals, and perhaps a project or two, as you attempt to follow along.

Pros

This approach does have one significant advantage over the others, at least if you get a good teacher: you have an expert on hand who you can ask questions and get an immediate response.

However, it also frequently comes with some major downsides.

Cons

The first is cost. While costs can vary, in-person training tends to be one of the most expensive learning options. A three-day course in Power BI at ONLC training centers across the US, for example, costs $1,795 – and that’s the “early bird” price! Even shorter, more affordable options tend to start at over $500.

Another downside is convenience. With in-person classes you have to adhere to a fixed schedule. You have to commute to a specific location (which also costs money). This can be quite a hassle to arrange, particularly if you’re already a working professional looking to change careers or simply add skills to your resume – you’ll have to somehow juggle your work and personal schedules with the course’s schedule. And if you get sick, or simply have an “off” day, there’s no going back and retrying – you’ll simply have to find some other way to learn any material you may have missed.

Overall

In-person learning may be a good option for learners who aren’t worried about how much they’re spending, and who strongly value being able to speak directly with a teacher in an in-person environment.

If you choose to go this route, be sure you’ve checked out reviews of the course and the instructor beforehand!

2. Video-based online course

A more common approach is to enroll in a Power BI online course or Power BI online training program that teaches you Power BI skills using videos. Many learners choose platforms like EdX or Coursera that offer Microsoft Power BI courses using lecture recordings from universities to make higher education more broadly accessible.

Pros

This approach can certainly be attractive, and one advantage of going this route is that, assuming you choose a course that was recorded at a respected institution, you can be reasonably sure you’re getting information that is accurate.

However, it also has a few disadvantages.

Cons

First, it’s generally not very efficient. While some folks can watch a video of someone using software and absorb most of the content on the first try, most of us can’t. We’ll watch a video lecture, then open up Power BI to try things for ourselves and discover we have to go back to the video, skipping around to find this or that section to be able to perform the right steps on our own machine.

Similarly, many online courses test your knowledge between videos with fill-in-the-blank and multiple-choice quizzes. These can mislead learners into thinking they’ve grasped the video content. But getting a 100

Second, while online courses tend to be more affordable than in-person courses, they can still get fairly expensive. Often, they’re sold on the strength of the university brand that’ll be on the certificate you get for completing the course, which can be misleading. Employers don’t care about those sorts of certificates. When it comes to Microsoft Power BI, Microsoft’s own PL-300 certification is the only one that really carries any weight.

Some platforms address these video-based learning challenges by combining visual instruction with immediate hands-on practice. For example, Dataquest's Learn to Visualize Data in Power BI course lets you practice creating charts and dashboards as concepts are introduced, eliminating the back-and-forth between videos and software.

Lastly, online courses also sometimes come with the same scheduling headaches as in-person courses, requiring you to wait to begin the course at a certain date, or to be online at certain times. That’s certainly still easier than commuting, but it can be a hassle – and frustrating if you’d like to start making progress now, but your course session is still a month away.

Overall

Online courses can be a good option for learners who tend to feel highly engaged by lectures, or who aren’t particularly concerned with learning in the fastest or most efficient way.

3. On your own

Another approach is to learn Power BI on your own, essentially constructing your own curriculum via the variety of free learning materials that exist online. This might include following an introduction Power BI tutorial series on YouTube, working through blog posts, or simply jumping into Power BI and experimenting while Googling/asking AI what you need to learn as you go.

Pros

This approach has some obvious advantages. The first is cost: if you find the right materials and work through them in the right order, you can end up learning Power BI quite effectively without paying a dime.

This approach also engages you in the learning process by forcing you to create your own curriculum. And assuming you’re applying everything in the software as you learn, it gets you engaged in hands-on learning, which is always a good thing.

Cons

However, the downside to that is that it can be far less efficient than learning from the curated materials found in Power BI courses. If you’re not already a Power BI expert, constructing a curriculum that covers everything, and covers everything in the right order, is likely to be difficult. In all likelihood, you’ll discover there are gaps in your knowledge you’ll have to go back and fill in.

Overall

This approach is generally not going to be the fastest or simplest way to learn Power BI, but it can be a good choice for learners who simply cannot afford to pay for a course, or for learners who aren’t in any kind of rush to add Power BI to their skillset.

4. Interactive, project-based platform

Our final option is to use interactive Power BI courses that are not video-based. Platforms like Dataquest use a split-screen interface to introduce and demonstrate concepts on one side of the screen, embedding a fully functional version of Power BI on the other side of the screen. This approach works particularly well for Power BI courses for beginners because you can apply what you're learning as you're learning it, right in the comfort of your own browser!

Pros

At least in the case of Dataquest, these courses are punctuated with more open-ended guided projects that challenge you to apply what you've learned to build real projects that can ultimately be part of your portfolio for job applications.

The biggest advantage of this approach is its efficiency. There's no rewatching videos or scanning around required, and applying concepts in the software immediately as you're learning them helps the lessons "stick" much faster than they otherwise might.

For example, Dataquest's workspace management course teaches collaboration and deployment concepts through actual workspace scenarios, giving you practical experience with real-world Power BI administration tasks.

Similarly, the projects force you to synthesize and reinforce what you’ve learned in ways that a multiple-choice quiz simply cannot. There’s no substitute for learning by doing, and that’s what these platforms aim to capitalize on.

In a way, it’s a bit of the best of both worlds: you get course content that’s been curated and arranged by experts so you don’t have to build your own curriculum, but you also get immediate hands-on experience with Power BI, and build projects that you can polish up and use when it’s time to start applying for jobs.

These types of online learning platforms also typically allow you to work at your own pace. For example, it’s possible to start and finish Dataquest’s Power Bi skill path in a week if you have the time and you’re dedicated, or you can work through it slowly over a period of weeks or months.

When you learn, and how long your sessions last, is totally up to you, which makes it easier to fit this kind of learning into any schedule.

Cons

The interactive approach isn’t without downsides, of course. Learners who aren’t comfortable with reading may prefer other approaches. And although platforms like Dataquest tend to be more affordable than other online courses, they’re generally not free.

Overall

We feel that the interactive, learn-by-doing approach is likely to be the best and most efficient path for most learners.

Understanding DAX: A key Power BI skill to master

Regardless of which learning approach you choose, there's one particular Power BI skill that deserves special attention: DAX (Data Analysis Expressions). If you're serious about becoming proficient with Power BI, you'll want to learn DAX as part of your studies―but not right away.

DAX is Power BI's formula language that allows you to create custom calculations, measures, and columns. Think of it as Excel formulas, but significantly more powerful. While you can create basic visualizations in Power BI without DAX, it's what separates beginners from advanced users who can build truly dynamic and insightful reports.

Why learning DAX matters

Here's why DAX skills are valuable:

• Advanced calculations: Create complex metrics like year-over-year growth, moving averages, and custom KPIs
• Dynamic filtering: Build reports that automatically adjust based on user selections or date ranges
• Career advancement: DAX knowledge is often what distinguishes intermediate from beginner Power BI users in job interviews
• Problem-solving flexibility: Handle unique business requirements that standard visualizations can't address

The good news? You don't need to learn DAX immediately. Focus on picking up Power BI's core features first, then gradually introduce DAX functions as your projects require more sophisticated analysis. Dataquest's Learn Data Modeling in Power BI course introduces DAX concepts in a practical, project-based context that makes these formulas easier to understand and apply.

Choosing the right starting point for beginners

If you're completely new to data analysis tools, choosing the right Power BI course for beginners requires some additional considerations beyond just the learning format.

What beginners should look for

The best beginner-friendly Power BI training programs share several key characteristics:

• No prerequisites assumed: Look for courses that start with basics like importing data and understanding the Power BI interface
• Hands-on practice from day one: Avoid programs that spend too much time on theory before letting you actually use the software
• Real datasets: The best learning happens with actual business data, not contrived examples
• Portfolio projects: Choose programs that help you build work samples you can show to potential employers
• Progressive complexity: Start with simple visualizations before moving to advanced features like DAX

For complete beginners, we recommend starting with foundational concepts before diving into specialized training. Dataquest's Introduction to Data Analysis Using Microsoft Power BI is designed specifically for newcomers, covering everything from connecting to data sources to creating your first dashboard with no prior experience required!

Common beginner mistakes to avoid

Many people starting their Power BI learning journey tend to make these costly mistakes:

• Jumping into advanced topics too quickly: Learn the basics before attempting complex DAX formulas
• Focusing only on pretty visuals: Learn proper data modeling principles from the start
• Skipping hands-on practice: Reading about Power BI isn't the same as actually using it
• Not building a portfolio: Save and polish your practice projects for job applications

Remember, everyone starts somewhere. The goal isn't to become a Power BI expert overnight, but to build a solid foundation you can expand upon as your skills grow.

What's the best way to learn Power BI and how long will it take?

After comparing all these approaches, we believe the best way to learn Power BI for most people is through an interactive, hands-on platform that combines expert-curated content with immediate practical application.

Of course, how long it takes you to learn Power BI may depend on how much time you can commit to the process. The basics of Power BI can be learned in a few hours, but developing proficiency with its advanced features can take weeks or months, especially if you want to take full advantage of capabilities like DAX formulas and custom integrations.

In general, however, a learner who can dedicate five hours per week to learning Power BI on Dataquest can expect to be competent enough to build complete end-to-end projects and potentially start applying for jobs within a month.

Ready to discover the most effective way to learn Power BI? Start with Dataquest's Power BI skill path today and experience the difference that hands-on, project-based learning can make.

Will getting a SQL certification actually help you get a data job? There's a lot of conflicting answers out there, but we're here to clear the air.

In this article, we’ll dispel some of the myths regarding SQL certifications, shed light on how hiring managers view these certificates, and back up our claims with actual data. We'll also explore why SQL skills are more important than ever in the era of artificial intelligence and machine learning.

Do You Need a SQL Certification for an AI or Data Science Job?

It Depends. Learning SQL is more important than ever if you want to get a job in data, especially with the rapid advancements in artificial intelligence (AI) and machine learning (ML). For example, SQL skills are essential for accessing and preparing the massive datasets needed to train cutting-edge ML models, analyzing model performance, and deriving insights from AI outputs. But do you need an actual certificate to prove this knowledge? It depends on your desired role in the data science and AI field. 

When You DON'T Need a Certificate

Are you planning to work as a data analyst, data engineer, AI/ML engineer, or data scientist? 

Then, the answer is: No, you do not need a SQL certificate. You most certainly need SQL skills for these jobs, but a certification won’t be required. In fact, it probably won’t even help.

Here’s why.

What Hiring Managers Have to Say About SQL Certification

I interviewed several data science hiring managers, recruiters, and other professionals for our data science career guide. I asked them about the skills and qualifications they wanted to see in good job candidates for data science and AI roles.

Throughout my 200 pages of interview transcripts, the term “SQL” is mentioned a lot. It's clearly a skill that most hiring managers want to see, especially as data becomes the fuel for AI and ML models. But the terms “certification” and “certificate”? Those words don’t appear in the transcripts at all. 

Not a single person I spoke to thought certificates were important enough to even mention. Not even once!

In other words, the people who hire data analysts, data scientists and AI/ML engineers typically don’t care about certifications. Having a SQL certificate on your resume isn’t likely to impact their decision one way or the other.

Why Aren’t AI and Data Science Recruiters Interested in Certificates?

For starters, certificates in the industry are widely available and heavily promoted. But most AI and data science employers aren’t impressed with them. Why not? 

The short answer is that there’s no “standard” certification for SQL. Plus, there are so many different online and offline SQL certification options that employers struggle to determine whether these credentials actually mean anything, especially in the rapidly evolving fields of AI and data science.

Rather than relying on a single piece of paper that may or may not equate to actual skills, it’s easier for employers to simply look at an applicant’s project portfolio. Tangible proof of real-world experience working with SQL for AI and data science applications is a more reliable representation of skills compared to a generic certification. 

The Importance of SQL Skills for AI and Machine Learning

While certifications may not be necessary, the SQL skills they aim to validate are a requirement for anyone working with data, especially now that AI is everywhere.

Here are some of the key ways SQL powers today's most cutting-edge AI applications:

• Training Data Preparation: ML models are only as good as the data they're trained on. SQL is used heavily in feature engineering―extracting, transforming and selecting the most predictive data attributes to optimize model performance.
• Data Labeling and Annotation: For supervised machine learning approaches, SQL is used to efficiently label large training datasets and associated relevant metadata.
• Model Evaluation and Optimization: Data scientists and ML engineers use SQL to pull in holdout test data, calculate performance metrics, and analyze errors to iteratively improve models.
• Deploying AI Applications: Once a model is trained, SQL is used to feed in real-world data, return predictions, and log performance for AI systems running in production.

As you can see, SQL is an integral part of the AI workflow, from experimentation to deployment. That's why demonstrating SQL skills is so important for AI and data science jobs, even if a formal certification isn't required.

The Exception

For most roles in AI and data science, having a SQL certification isn’t necessary. But there are exceptions to this rule. 

For example, if you want to work in database administration as opposed to data science or AI/ML engineering, a certificate might be required. Likewise, if you’re looking at a very specific company or industry, getting SQL certified could be helpful.  

Which Flavor?

There are many "flavors" of SQL tied to different database systems and tools commonly used in enterprise AI and ML workflows. So, there may be official certifications associated with the specific type of SQL a company uses that are valuable, or even mandatory.

For example, if you’re applying for a database job at a company that uses Microsoft’s SQL Server to support their AI initiatives, earning one of Microsoft’s Azure Database Administrator certificates could be helpful. If you’re applying for a job at a company that uses Oracle for their AI infrastructure, getting an Oracle Database SQL certification may be required.

Cloud SQL

SQL Server certifications like Microsoft's Azure Database Administrator Associate are in high demand as more AI moves to the cloud. For companies leveraging Oracle databases for AI applications, the Oracle Autonomous Database Cloud 2025 Professional certification is highly valued.

So while database admin roles are more of an exception, even here skills and experience tend to outweigh certifications. Most AI-focused companies care mainly about your ability to efficiently manage the flow and storage of training data, not a piece of paper.

Most AI and Data Science Jobs Don’t Require Certification

Let’s be clear, though. For the vast majority of AI and data science roles, specific certifications are not usually required. The different variations of SQL rarely differ too much from “base” SQL. Thus, most employers won’t be concerned about whether you’ve mastered a particular brand’s proprietary tweaks.

As a general rule, AI and data science recruiters just want to see proof that you've got the fundamental SQL skills to access and filter datasets. Certifications don't really prove that you have a particular skill, so the best way to demonstrate your SQL knowledge on a job application is to include projects that show off your SQL mastery in an AI or data science context.

Is a SQL Certification Worth it for AI and Data Science?

It depends. Ask yourself: Is the certification program teaching you the SQL skills that are valuable for AI and data science applications, or just giving you a bullet point for your LinkedIn? The former can be worth it. The latter? Not so much. 

The price of the certification is also an important consideration. Not many people have thousands to spend on a SQL certification. Even if you do, there’s no good reason to invest that much; the return on your investment just won't be there. You can learn SQL interactively, get hands-on with real AI and data science projects, and earn a SQL certification for a much lower price on platforms like Dataquest.

What SQL Certificate Is Best?

As mentioned above, there’s a good chance you don’t need a SQL certificate. But if you do feel you need one, or you'd just like to have one, here are some of the best SQL certifications available with a focus on AI and data science applications:

Dataquest’s SQL Courses

These are great options for learning SQL for AI, data science and data analysis. You'll get hands-on with real SQL databases and we'll show you how to write queries to pull, filter, and analyze the data you need. For example, you can use the skills you'll gain to analyze the massive datasets used in cutting-edge AI and ML applications. All of our SQL courses offer certifications that you can add to your LinkedIn after you’ve completed them. They also include guided projects that you can complete and add to your GitHub and resume to showcase your SQL skills to potential employers!

If you complete the Dataquest SQL courses and want to go deeper into AI and ML, you can enroll in the Data Scientist in Python path.

Microsoft’s Azure Database Administrator Certificate

This is a great option if you're applying to database administrator jobs at companies that use Microsoft SQL Server to support their AI initiatives. The Azure certification is the newest and most relevant certification related to Microsoft SQL Server.

Oracle Database SQL Certification

This could be a good certification for anyone who’s interested in database jobs at companies that use Oracle.

Cloud Platform SQL Certifications

AWS Certified Database - Specialty: Essential if you're targeting companies that use Amazon's database services. Covers RDS, Aurora, DynamoDB, and other AWS data services. Learn more about the AWS Database Specialty certification.

Google Cloud Professional Data Engineer: Valuable for companies using BigQuery and Google's data ecosystem. BigQuery has become incredibly popular for analytics workloads. Check out the Google Cloud Data Engineer certification.

Snowflake SnowPro Core: Increasingly important as Snowflake becomes the go-to cloud data warehouse for many companies. This isn't traditional SQL, but it's SQL-based and highly relevant. Explore Snowflake's certification program.

Koenig SQL Certifications

Koenig offers a variety of SQL-related certification programs, although they tend to be quite pricey (over $1,500 USD for most programs). Most of these certifications are specific to particular database technologies (think Microsoft SQL Server) rather than being aimed at building general SQL knowledge. Thus, they’re best for those who know they’ll need training in a specific type of database for a job as a database administrator.

Are University, edX, or Coursera Certifications in SQL Too Good to Be True for AI and Data Science? 

Unfortunately, Yes.

Interested in a more general SQL certifications? You could get certified through a university-affiliated program. These certification programs are available either online or in-person. For example, there’s a Stanford program at EdX. And programs affiliated with UC Davis and the University of Michigan can be found at Coursera.

These programs appear to offer some of the prestige of a university degree without the expense or the time commitment. Unfortunately, AI and data science hiring managers don’t usually see them that way.

Why Employers Aren’t Impressed with SQL Certificates from Universities

Employers know that a Stanford certificate and a Stanford degree are very different things. These programs rarely include the rigorous testing or substantial AI and data science project work that would impress recruiters. 

The Flawed University Formula for Teaching SQL

Most online university certificate programs follow a basic formula:

• Watch video lectures to learn the material.
• Take multiple-choice or fill-in-the-blank quizzes to test your knowledge.
• If you complete any kind of hands-on project, it is ungraded, or graded by other learners in your cohort.

This format is immensely popular because it is the best way for universities to monetize their course material. All they have to do is record some lectures, write a few quizzes, and then hundreds of thousands of students can move through the courses with no additional effort or expense required. 

It's easy and profitable for the universities. That doesn't mean it's necessarily effective for teaching the SQL skills needed for real-world AI and data science work, though, and employers know it. 

With many of these certification providers, it’s possible to complete an online programming certification without ever having written or run a line of code! So you can see why a certification like this doesn’t hold much weight with recruiters.

How Can I Learn the SQL Skills Employers Want for AI and Data Science Jobs?

Getting hands-on experience with writing and running SQL queries is imperative for aspiring AI and data science practitioners. So is working with real-world projects. The best way to learn these critical professional skills is by doing them, not by watching a professor talk about them.

That’s why at Dataquest, we have an interactive online platform that lets you write and run real SQL queries on real data right from your browser window. As you’re learning new SQL concepts, you’ll be immediately applying them to relevant data science and AI problems. And you don't have to worry about getting stuck because Dataquest provides an AI coding assistant to answer your SLQ questions. This is hands-down the best way to learn SQL.

After each course, you’ll be asked to synthesize your new learning into a longer-form guided project. This is something that you can customize and put on your resume and GitHub once you’re finished. We’ll give you a certificate, too, but that probably won’t be the most valuable takeaway. Of course, the best way to determine if something is worth it is always to try it for yourself. At Dataquest, you can sign up for a free account and dive right into learning the SQL skills you need to succeed in the age of AI, with the help of our AI coding assistant.