Is it true that getting an entry-level job as a data analyst has become more difficult? The short answer is yes. So the question is should you still learn how to become a data analyst in 2025? The short answer is also yes.
More about this later in the blog but first let’s talk about how you can become a data analyst in 2025. In this blog we’re going to talk about the toolbox and the skill set you need in order to become a data analyst. I’ll walk you through all the skills you need to learn and develop starting with the foundations.
There are seven steps that I’m going to be sharing in this blog plus a bonus one so keep reading till the end. So what does a data analyst do? A data analyst collects, organizes, and analyzes data from various sources to identify trends and patterns and then they communicate their findings through visualization and reports to help business make more informed decisions. Step one that you need to follow is learn to code.
The main language that you will be using as a data analyst is SQL. So what I would recommend here is that start with SQL from intro, go to intermediate, and it’s great if you can get to the advanced level. SQL is your bread and butter as a data analyst so the better you get at it the better your chances are of doing the job but also passing the coding interview.
More about the coding interviews later in the blog. Some of my resources for learning SQL if you’re just starting out include SQL Bolt, SQL Zoo, my favorite one W3Schools, and HackerRank. These tools are great if you’re just starting out intro SQL and you also want to practice.
The next coding language that you should be learning is Python. Now here I do have a disclaimer that if you are going to be competing in a remote job market which means that the data analyst applicant pool is wider where you are competing in a global market, the chances are Python becomes a mandatory coding language that you need to know as a data analyst. In my previous blog I have mentioned that Python is good to know.
I really want to mention here is that if you really want a remote job you should be learning how to use Python as a data analyst specifically how to use libraries such as Pandas, NumPy for doing data analysis and data exploration. So if you are planning to learn Python, learn all these skills so you can be ready for the global job market.
The next thing you’re going to do is step two. So step two is going to be learning Excel and how to do data analysis in Excel. Chances are you’ll be spending a lot of your time in Excel writing SQL then going to Excel and that would include like analyzing your data, making sense of the data, creating pivot tables, creating summaries, creating visualizations.
So make sure that you have a solid understanding of how to use Excel and if you know Excel you can easily transfer your skills to other tools like Google Sheets and whatnot. So I would just suggest to focus on Excel. These are the exact skills you need to learn for Excel.
Another tool that you should be learning as part of step two after you have learned Excel or in parallel get familiar with the visualization tools or reporting tool such as Tableau, PowerBI, MicroStrategy, QuickSight, Google Data Studio. These tools are important for you as a data analyst because you will be reporting visualizations in those tools. There are tons of free resources for example Tableau has free one-on-one course that you can go and take and learn how to use Tableau.
They also have a free version that you can download on your desktop and learn how to work with Tableau. So just pick one tool and stick to it. Once you learn one tool it’s easy to transfer your skills to another tool such as Power BI and other reporting tools and reporting tools that are available out there.
There are tons of resources online. You can search up YouTube video. Alexa analyst has a bunch of videos on Excel and Tableau so you can definitely go learn more about it there.
All right let’s say you have covered step one learn to code cover step two learn Excel and data visualization tools. Now it’s time for step three. Here what I would recommend is you start with statistics.
Statistics will give you the knowledge you need to make sense of the data using the tools you have learned so far. Don’t get it? Let me give you an example. For example what does the data distribution look like? What does it actually mean? Is the data skewed? Is it normally distributed? You don’t know those answers yet but once you learn statistics you will be easily able to answer these questions by looking at your data, summarizing your data, understanding how your data looks like so you can come up with better data analysis approaches to solve the problem that you’re solving.
So this is why statistics is very very important. These are the statistics skills that you should be learning as a data analyst. These are some of my favorite free resources where you can go and learn statistics one-on-one specifically focused on data analyst role.
Okay let’s say you have made it to step three. Honestly you know plenty now to actually do your job as a data analyst. So now we’re going to focus on step four which is your communication and storytelling skills.
All right by now you basically have all the hard skills you need to know to do your job as a data analyst. But now what you need is solid communication and good storytelling so you can effectively communicate your work to others. Believe me having solid communication as a data analyst is one of the biggest strengths that you can have because the chances are that you will be working with stakeholders before the project, during the project, after the project, after you have done all that awesome work you actually have to take that work either put it in a presentation deck or put it in a document or do some other form of communication where you have to tell the story with data.
And when you have good communication good storytelling you’re going to shine as a data analyst. Still don’t get it? Let me give you an example of what I mean when I say storytelling. Let’s say you have analyzed amazon sales data over the last six months and you have found some interesting patterns.
For example sales volume has increased but the average order size has decreased. To make sense of this data you need to craft a story around why this is happening. Of course you’ll use analytical skills but also need to figure out how to present these insights in a slide or in a presentation that can be understood by the leaders and the business decision makers.
What is the narrative that you’re going to tell with presentation in these documents? That’s exactly what I mean by storytelling. There are three things you need to keep in mind when telling a story with data. Number one understand the interest and expectation so you can tailor your story.
Number two speak their language. If you’re talking to a senior leader speak their language. If you’re talking to a non-tech person speak their language.
So understand how to speak to other people in their language when presenting. And number three is anticipate their questions so you can answer their questions in the documents and the presentations that you’re preparing. In terms of learning resources on how to become a good storyteller this is a book recommendation that I can give you if you want to go more in depth.
But one other thing that I would recommend you to do is start writing blogs. Writing microblogs is one of the best ways to practice your communication skills and be better at it. You can also use ChatGPT here to craft your storytelling.
One of my favorite ways to use ChatGPT and other generative AI tools is to create a structure of how I’m going to tell a story starting with like what is the objective, what is the goal, what is the benefit, what is the outcome from this presentation. So be smart about the tools that you have available to you and always practice before you actually head into that meeting where you’re going to be presenting that data. All right we’re moving fast.
We’re on step five which is to build hands-on project. If you follow all the steps by now you have all the skills required to become a data analyst. You’re actually a data analyst by now.
So now the question is how do you actually stand out in the job market? How do you develop these skills further and how do you land that job? The number one thing that you need to do in order to land that job once you have learned everything start building projects. You want to do this step for two reasons. One is to show that you actually know what you have learned and by doing projects is the only way other people can know that you have all these skills.
Number two you really want to stand out in the job market and by having realistic industry relevant projects you can actually stand out among all the pool of candidates that you’re competing with. In terms of the projects I would suggest picking five projects. This is just an arbitrary number you can pick however many you think are reasonable.
Five is a good number because you can show all the skills that you have learned. For example, SQL, Python, Excel, data visualization, data manipulation, analytical skills, storytelling skills and whatnot. So make sure that when you’re doing these projects these projects should show either SQL, Python, Excel, data visualization with reporting tools, statistics, analytics, storytelling and communication.
And one project doesn’t have to show all of these skills in one project. You can be a little bit smarter about it and kind of diversify. One project could be all about just doing SQL trying to do a lot of data manipulation.
Second project could be about doing uh going deeper into data visualization tools such as Tableau and so on. So let’s say you’ve done these projects now it’s time to showcase these projects. Some of the places where you should be showing these projects is on GitHub where you can create your own GitHub profile and upload your projects there.
Second one is you can create your websites and build a portfolio around that. Third one which I highly recommend is on LinkedIn. Let’s say if you have done some data analysis, show it on your profile but also make a post about it or write a blog about it.
Remember the first question I asked in the video? Is it difficult to get a job as a data analyst? The short answer is yes. In my opinion the reason this is the case is because the supply of the data analyst has grown exponentially compared to the demand. This is true for two reasons.
Number one this is partially because it’s easier to become a data analyst. For example I just shared a lot in this 10 minute video. And two remote jobs.
Remote jobs have more competition because there is a wider pool of candidates to choose from. Remember earlier I mentioned that Python is a required skill if you’re competing in a global market? That applies here too. Wider pools of candidates mean more talent, more competition, low probability of landing a job.
Regardless of this you should feel confident in your skills and the only way to do that is if you have put in the work not just in learning the skills but also preparing for the interview. So how exactly do you do that? That brings me to my bonus step eight. I can’t keep a count.
What step are we on? What exactly do you need to do in order to stand out in the job market? There are two things you need to focus on. One is on your resume show the hands-on projects that you have worked on. Internships, side projects and whatnot.
Show it on your resume. Have somebody else review it because if you’re not getting calls that means the chances are there’s something wrong with your resume. So have somebody review it.
Mention numbers, show impact and show the tools and skills that you have used to solve those problems. Number two is interview skills. I have said it in so many of my previous videos before knowing the work versus interviewing are two different skill set because in an interview skill set you are in a time-sensitive fashion solving a complex SQL problem in five minutes.
Does that happen in real life when you go to do your actual work and you’re given 10 minutes to solve this business problem that you’re given? No. So these are two different skills. So in order to land that job you need to be very good at interviewing and the only way you can be good at interviewing is if you practice on how to solve coding problems in time sensitive.
The more you practice the better you will get. The two resources that I would recommend here is Stratascratch and LeetCode. You can also use ChatGPT for mock interviews as well as for crafting interview questions.
For example you can ask it can you give me interview questions for a data analyst role. You can also be very smart and feed it the job description for it to craft interview questions so you can practice. So let’s say you have followed all the steps from step one through six.
You have done all the hard work. You have landed in interviews. You have done an amazing job in your interviews.
Now I want to give you a bonus tip because in the current economy everybody should be following this advice. So the chances of doing really well on your interview means that you will end up with a job offer and possibly multiple offers in best-case scenario. So it’s time to negotiate your salary.
I was able to negotiate an extra $10,000 when I got an offer as a data analyst out of school as my first job and $100,000 more because of salary negotiation in my last job. So if you’re interested in crafting your salary negotiation skills so you don’t leave money on I’m actually launching a master class which is specifically focused on tech salary negotiation that teaches you all the components of salary negotiation and which levers which strategies you should be using when it comes to salary negotiation. For example if the recruiter asks you what is your salary expectation what should you say.
All right these are all the tips I had for you. I hope this roadmap was helpful.