Data is everywhere. Every click, purchase, or social media like creates mountains of information, but raw numbers do not tell a story. That is where data analysts come in. They turn messy datasets into actionable insights that help businesses grow.

Whether you're looking to become a junior data analyst or looking to level up, here are the top 11 data analyst skills every professional needs in 2025, including one optional skill that can help you stand out.

1. SQL

SQL (Structured Query Language) is the language of databases and is arguably the most important technical skill for analysts. It allows you to efficiently query and manage large datasets across multiple systems—something Excel cannot do at scale.

Example in action: Want last quarter's sales by region? SQL pulls it in seconds, no matter how huge the dataset.

Learning Tip: Start with basic queries, then explore joins, aggregations, and subqueries. Practicing data analytics exercises with SQL will help you build confidence and precision.

2. Excel

Since it’s not going anywhere, it’s still worth it to learn Microsoft Excel. Beyond spreadsheets, it offers pivot tables, macros, and Power Query, which are perfect for quick analysis on smaller datasets. Many startups or lean teams still rely on Excel as their first database.

Example in action: Summarize thousands of rows of customer feedback in minutes with pivot tables, then highlight trends visually.

Learning Tip: Focus on pivot tables, logical formulas, and basic automation. Once comfortable, try linking Excel to SQL queries or automating repetitive tasks to strengthen your technical skills in data analytics.

3. Python or R

Python and R are essential for handling big datasets, advanced analytics, and automation. Python is versatile for cleaning data, automation, and integrating analyses into workflows, while R excels at exploratory data analysis and statistical analysis.

Example in action: Clean hundreds of thousands of rows with Python’s pandas library in seconds, something that would take hours in Excel.

Learning Tip: Start with data cleaning and visualization, then move to complex analyses like regression or predictive modeling. Building these data analyst skills is critical for anyone working in data science. Of course, which is better to learn is still up for debate.

4. Data Visualization

Numbers alone rarely persuade anyone. Data visualization is how you make your insights clear and memorable. Tools like Tableau, Power BI, or Python/R libraries help you tell a story that anyone can understand.

Example in action: A simple line chart showing revenue trends can be far more persuasive than a table of numbers.

Learning Tip: Design visuals with your audience in mind. Recreate dashboards from online tutorials to practice clarity, storytelling, and your soft skills in communicating data analytics results.

5. Statistics & Analytics

Strong statistical analysis knowledge separates analysts who report numbers from those who generate insights. Skills like regression, correlation, hypothesis testing, and A/B testing help you interpret trends accurately.

Example in action: Before recommending a new marketing campaign, test whether the increase in sales is statistically significant or just random fluctuation.

Learning Tip: Focus on core probability and statistics concepts first, then practice applying them in projects. Our Probability and Statistics with Python skill path is a great way to learn theoretical concepts in a hands-on way.

6. Data Cleaning & Wrangling

Data rarely comes perfect, so data cleaning skills will always be in demand. Cleaning and transforming datasets, removing duplicates, handling missing values, and standardizing formats are often the most time-consuming but essential parts of the job.

Example in action: You want to analyze customer reviews, but ratings are inconsistent and some entries are blank. Cleaning the data ensures your insights are accurate and actionable.

Learning Tip: Practice on free datasets or public data repositories to build real-world data analyst skills.

7. Communication & Presentation Skills

Analyzing data is only half the battle. Sharing your findings clearly is just as important. Being able to present insights in reports, dashboards, or meetings ensures your work drives decisions.

Example in action: Presenting a dashboard to a marketing team that highlights which campaigns brought the most new customers can influence next-quarter strategy.

Learning Tip: Practice explaining complex findings to someone without a technical background. Focus on clarity, storytelling, and visuals rather than technical jargon. Strong soft skills are just as valuable as your technical skills in data analytics.

8. Dashboard & Report Creation

Beyond visualizations, analysts need to build dashboards and reports that allow stakeholders to interact with data. A dashboard is not just a fancy chart. It is a tool that empowers teams to make data-driven decisions without waiting for you to interpret every number.

Example in action: A sales dashboard with filters for region, product line, and time period can help managers quickly identify areas for improvement.

Learning Tip: Start with simple dashboards in Tableau, Power BI, or Google Data Studio. Focus on making them interactive, easy to understand, and aligned with business goals. This is an essential part of professional data analytics skills.

9. Domain Knowledge

Understanding the industry or context of your data makes you exponentially more effective. Metrics and trends mean different things depending on the business.

Example in action: Knowing e-commerce metrics like cart abandonment versus subscription churn metrics can change how you interpret the same type of data.

Learning Tip: Study your company’s industry, read case studies, or shadow colleagues in different departments to build context. The more you know, the better your insights and analysis will be.

10. Critical Thinking & Problem-Solving

Numbers can be misleading. Critical thinking lets analysts ask the right questions, identify anomalies, and uncover hidden insights.

Example in action: Revenue drops in one region. Critical thinking helps you ask whether it is seasonal, a data error, or a genuine trend.

Learning Tip: Challenge assumptions and always ask “why” multiple times when analyzing a dataset. Practice with open-ended case studies to sharpen your analytical thinking and overall data analyst skills.

11. Machine Learning Basics

Not every analyst uses machine learning daily, but knowing the basics—predictive modeling, clustering, or AI-powered insights—can help you stand out. You do not need this skill to get started as an analyst, but familiarity with it is increasingly valuable for advanced roles.

Example in action: Using a simple predictive model to forecast next month’s sales trends can help your team allocate resources more effectively.

Learning Tip: Start small with beginner-friendly tools like Python’s scikit-learn library, then explore more advanced models as you grow. Treat it as an optional skill to explore once you are confident in SQL, Python/R, and statistical analysis.

Where to Learn These Skills

Want to become a data analyst? Dataquest makes it easy to learn the skills you need to get hired.

With our Data Analyst in Python and Data Analyst in R career paths, you’ll learn by doing real projects, not just watching videos. Each course helps you build the technical and practical skills employers look for.

By the end, you’ll have the knowledge, experience, and confidence to start your career in data analysis.

Wrapping It Up

Being a data analyst is not just about crunching numbers. It is about turning data into actionable insights that drive decisions. Master these data analytics and data analyst skills, and you will be prepared to handle the challenges of 2025 and beyond.

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.

In podcast #286 we take the time to review an older video of John Cleese giving a talk about the Creative Process in Management. We thought this would be an outstanding conversation to draw parallels to the Business Intelligence world. There are so many different areas we can apply creative thinking in implementing Fabric & Power BI solutions.

You can check out that video by John Cleese here -> https://youtu.be/Pb5oIIPO62g?si=66KNV2I8p5ZlESzz

Episode 286 – Creativity in Power BI

Talking Points:

• What is Creativity, and what strategies can we use to be more creative?
• Discussion centered around the concept of being creative in handling data, building reports and fostering a data culture.
• Emphasize that creativity is not a talent but a mode of operation and discussed its relevance in the field of Business Intelligence.

Key Topics:

Where does creativity lie:

• Discussed the distinction between open and closed states.
• Strategies to foster creativity and measure its benefits.
• Dedicated time for creative thinking, challenging the conventional to-do list approach.
• Creating an oasis of quiet for pondering and problem-solving.
• Embracing humor as a catalyst for transitioning from closed to open thinking.

Overcoming Barriers:

• Identifying common obstacles to creativity within organizations dealing with technology and data.
• Evaluating the balance between analytical rigor and creative exploration in Power BI.

Applications and Tips:

• Exploring creativity in the adoption of BI practices, report building, and its application in day-to-day operations.
• Building reports with an open mindset.
• Allowing time for pondering before making decisions.
• Encouraging positive collaboration within a community.
• Applying the “art of the possible” by exploring new ideas.

Meeting Creativity:

• Examining the impact and value of injecting creativity into BI processes for organizational growth.
• Introducing humor in meetings to foster creativity.
• Building on ideas without fear of right or wrong.
• Utilizing random connections for innovative solutions.
• Creating a positive environment by avoiding negativity.

Report Building Process:

• Front-loading creativity in requirements gathering.
• Incorporating creative thinking in model design and building calculations for visuals.

This podcast episode is a treasure trove of insights for BI professionals looking to infuse creativity into their work, ultimately contributing to more innovative and effective business intelligence solutions. You can listen to the full conversation on the Explicit Measures podcast here:

As a special add-on for your enjoyment Tommy came up with a whole slew of jokes in the same vein that John Cleese told in the presentation. Feel free to use them in your next creative meeting!

Jokes Created by Tommy Puglia:

How many data scientists does it take to change a light bulb?
Three. One to replace the bulb, and two to model whether it was the most cost-effective light bulb choice.

How many machine learning experts does it take to change a light bulb?
Just one, but it will take thousands of tries to learn how to do it properly.

How many business analysts does it take to screw in a light bulb?
Two. One to assure everyone that everything is going according to the plan while the other screws the bulb into the water faucet.

How many BI consultants does it take to screw in a light bulb?
Only one, but they’ll first conduct a cost-benefit analysis to determine if the light bulb change will add value.

How many marketing analysts does it take to change a light bulb?
One, but they’ll also rebrand the room to make it look brighter.

How many sales analysts does it take to change a light bulb?
Just one, but they’ll convince you to upgrade to a smart bulb with a subscription plan.

How many data warehouse architects does it take to change a light bulb?
Two: one to change the bulb and another to ensure it integrates seamlessly with the existing lighting infrastructure.

How many AI developers does it take to change a light bulb?
They won’t. They’ll train a neural network to predict when the bulb will burn out and preemptively send a drone to replace it.

How many cloud storage experts does it take to change a light bulb?
None. They’ll just store light in the cloud and access it as needed.

How many Business Analysts does it take to screw in a light bulb?
Just one, but they will first interview everyone in the room to define the requirements for the ‘ideal light’ experience.

How many Data Analysts does it take to change a light bulb?
Two. One to replace the bulb, and the other to tell everyone how much brighter it could be with just a few more data points.

How many stakeholders does it take to change a light bulb?
Four. One to ask for a greener bulb, one to demand a cost-effective solution, one to insist on a smart bulb, and one to question why the bulb needs changing at all.

How many report requesters does it take to change a light bulb?
None. They’ll just ask for a daily report on the status of the light bulb but never actually replace it.

How many Data Analysts does it take to screw in a light bulb?
One, but by the time they’ve finished analyzing the best method, the technology for light bulbs has already changed.

How many stakeholders does it take to change a light bulb?
Five. One to change it and four to form a committee that debates whether it was better the old way.

How many report requesters does it take to change a light bulb?
None. They just keep requesting status updates on the darkness.

How many BI Consultants does it take to screw in a light bulb?
Two. One to assure the client that they’re leveraging cutting-edge lightbulb technology, and the other to outsource the actual screwing in to an intern.

How many BI Consultants does it take to change a light bulb at a large corporation?
An entire team, but the project will take three years and by the end, they’ll switch to a completely different kind of bulb.