What should your data analytics learning journey look like?

Posted on: June 4, 2024

Post Category: Data

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Disclaimer: take everything I say in this post as a guide – not hard rules for becoming a data analyst. Everyone will have different things to say, based on their experiences. But what follows is *my* opinion.

This post will cover some of the tools/skills you should be learning when building your career as an analyst – and how you should structure your learning.

So… what should you learn for data analytics?

I always have a hard time answering this question.

Because the role of a Data Analyst is tool-agnostic – meaning that you can be a Data Analyst regardless of the specific analytics tools or platforms you use (so long as you create value).

But I do know two tools you *must* learn regardless, and they are SQL and Excel.

For those who aren’t aware of what either of these tools are:

• SQL is a coding language that’s used to pull and manipulate information from databases – using chunks of code called queries.
• And Excel is… well, Microsoft Excel, the spreadsheeting tool.

And to sum up the reasons *why* they are important…

Most organisations use these tools – even the non-analysts:

• If you require critical business data, you will most likely pull this from a database or data warehouse – and to do that, you would use SQL.
• And if you are working with stakeholders who don’t come from a data background, which will be very likely, you might be asked to create/share your analysis using Excel.

I know there is a very big hate-bandwagon, when it comes to Excel… but in my view, you just need to know it. Do I think you need very knowledgeable about it, and have 3+ Excel projects? No. But I’d say at least know enough to be comfortable using it.

Regarding other popular tools like Power BI, Tableau, Python, and R, I would say they are all (very) good to know. They are not *as* essential – compared to SQL and Excel – but they are good to know.

For context, Power BI and Tableau are used for reporting, while Python and R are primarily used for automations, data processing and training machine learning models.

And how in-depth you should learn these tools, really depends on how technical you want your analyst job to be.

If you’re looking to do something less technical (akin to a Business Intelligence Analyst or an average Data Analyst), then focus more on Power BI and/or Tableau.

If you’re looking to do something more technical (akin to a Data Scientist), then focus more on Python and R.

So in essence, this is what I’d recommend: SQL, Excel, Power BI, Tableau, Python, and R.

But there are a few things I suggest you keep in mind (from my experience):

• You do not need to master both Power BI and Tableau. You can get away with being skilled with one and somewhat knowledgeable about the other. When you go into the industry, you will rarely (almost never) use both to support your work on a project. And if you’re very good at one, you can easily learn the other.
• Likewise, you do not need to master both Python and R. If I had to choose one to learn more of, it would be Python, because of its greater versatility. But, again, if you’re very good at one, you can easily learn the other.
• You only need to learn one dialect of SQL. There are also many dialects of SQL like MySQL, T-SQL, Oracle SQL, etc. The only (minor) difference between them is the syntax. You just need to learn one. It’s like choosing between learning American English, Australian English and British English.

Now, let me refer you to other Data Analyst creators on LinkedIn… what do *they* think about this topic?

• Annie Nelson (in her book, ‘How to Become a Data Analyst’) recommends learning SQL, Excel and a BI tool (either Tableau or Power BI).
• Kedeisha Bryan recommends learning Excel, PowerPoint, SQL, a BI tool (either Tableau or Power BI), and a programming language (either Python or R).

Everyone will have a different opinion on what you should learn, because there is no standard ‘Data Analyst’ role.

But I would recommend taking ideas from people who have achieved what you want to achieve, looking at job postings you would apply for, and creating your own learning checklist/plan.

After you figure out what tools you should learn, here’s how much you should learn…

Just enough to build a project for your portfolio.

This, I would say, only takes one comprehensive 3+ hour course – for each tool.

How you should structure your learning

If you’re following the DIY/self-taught Data Analyst pathway – or some variation of it – you have the freedom to structure how you learn the tools.

And to structure your learning, I’d opt an approach like this:

• Take ONE comprehensive course (highly rated and 5+ hours long) on each of the tools you want to learn.
• Fit in time to do personal projects, to apply what you learnt as soon as possible.
• When you get stuck, refer to online sources likes forums, youtube or AI.
• And only when you get *really* stuck, refer to another online course.

On top of this, I would also recommend *not* spending more than a month learning a tool. The longer you spend learning, the more you delay creating something tangible you can put on your resume – and the longer the time you could spend in the job market.

Limiting your learning to one course or one month for each tool will help you save time and money.

Your time is better spent applying your skills and building projects.

In terms of how many projects you should be building, you can build as many as you want… but when you’re applying for jobs, you’ll probably only show one (at most two) for any specific tool, on your resume. So start with one project, start applying for jobs, and keep improving and building more as you go along.

My journey – from beginner to two analytics internship offers in one-and-a-half years

My learning journey wasn’t a smooth one.

I started my learning journey during my second year of University, while having some time to learn outside of Uni studies – not just about data analytics, but also the different career options available to me.

So I’ll share a boiled down timeline of what I did:

• Jan-Mar 2020: I took online courses on MySQL, Python, R, Excel, Tableau, Power BI, Data Science, Statistics.
• Apr-Jul: I took online courses on machine learning and artificial intelligence, and I took part in various data competitions hosted through my Uni (which have ended up being additions to my portfolio and resume). Applied for ~5 analytics-related internships through my University and got rejected.
• Aug-Oct : Kept participating in competitions. Learnt more machine learning, and learnt about how to (actually) build a data analyst portfolio using GitHub and Tableau Public. Applied for the Tableau Ambassador Program and got accepted. Applied for ~5 more analytics-related internships and never heard back… at least for now.
• Nov 2020-Jan 2021: Received mentorship from a Lead Analyst for a large bank in the UK, and produced my first PERSONAL personal project. Received an interview offer for a small Actuarial firm in December, and commenced my first analytics internship in January.
• Feb-May 2021: Was hesitant about applying for more internships through my Uni because I kept getting rejected. But eventually applied for 2 more analytics internships in February. Received an offer to interview for one company in May – and accepted the job offer shortly after.

And here are some takeaways I would share from my experience:

• Put yourself out there, and find people who have similar goals; this will help you grow faster. Finding people who are like you will help you build accountability and feel motivated. All the data competitions I have done were with someone a year above me. He had a keener interest and stronger technical skills, and I kept wanting to team up with him. There were times I would’ve pulled out, but having him made me pull through and see our project from start to finish. So when you lack confidence, reach out to people who you could learn from. Put yourself out there and you might find someone who’s at a similar stage and can help you grow.
• Find mentors and connect with people. Connect with people who are ahead of you – analysts from the industry, previous Uni/bootcamp alumni who are where you want to be, etc. Because from those conversations, you will learn the things you should be doing to land a job. Without having the interactions I had during my student years, I would not have built a portfolio and sharted my ideas online – and I probably wouldn’t have landed my first analyst job.
• Be willing to create something even if you think it’s not your all. There’s always a mental barrier that stops people from sharing their work and even their resume – and it always costs people their growth and access to opportunities. People avoid submitting their work because they’re worried it’s not going to be received well. And people avoid submitting job applications because they think they’re not enough. Don’t do that. Share it anyway, get feedback and improve. And if you are very concerned about looking bad, talk to your close friends (so they can push over the edge), and start small – build projects where the stakes are low and keep raising the stakes when you build confidence.

Annie Nelson’s journey – from 0 to Data Analyst in 6 months

Annie’s journey, on the other hand, is a more streamlined one – which you can use for inspiration for your own learning journey:

(Below is an overview of her journey word-for-word)

• January: I did not know what data analytics was.
• February: Spent taking the Google Certificates course in Data Analytics learning SQL, Tableau, Excel, and R.
• March: Building a website, getting active on Linkedin, working on some portfolio projects.
• April: Taking a course in Python, deciding to try to switch careers, and getting my résumé and portfolio ready.
• May: A month of rejections. I applied to over 50 jobs and did not get very far with any of them.
• June: A strategy pivot. I spent June focused on building a portfolio, leveling up my skills, and talking about my skills and what I was learning on LinkedIn.
• July: The month I got my job, and did several other successful end-stage interviews.

Now, here is one thing I recommend for aspiring analysts to learn (and it’s not a tool)

In your first data analyst role, it is almost certain that you will produce a visualisation. And this visualisation could be a Power BI dashboard, a Tableau dashboard, a chart in an Excel spreadsheet, a chart in a Powerpoint slide deck… whatever.

But many analysts (aspiring *and* in the industry) will produce visualisations and not give it a second thought. They become caught up on learning *how* to make a chart using tools like Tableau, that they don’t really think about how to make them *look good*.

When being a data analyst, it’s important to clearly communicate data insights, and know how to build visualisations that are well-designed (so people understand them and want to keep looking at them). And if you want to learn this for your projects, and for the rest of your career, I would recommend start by learning: visual best practices.

Visual best practices are a set of guidelines you can use to create visualisations that are better-designed for your users. It helps you get the key insights across, and it makes them nicer to look at.

And it could be the difference that helps your data visualisation projects get more recognition/engagement when you share them.

A resource I would recommend to learn this would be the book ‘#MakeoverMonday’ by Andy Kriebel and Eva Murray.

It’s a very nice thing to know and apply. But learning the tools should still take precedence.

Closing remarks

As cliché as it sounds, no matter how much learning you do, you won’t know everything. And yes, it applies here when building up your knowledge to be a data analyst.

Even during my current role as a Data Analyst, after working full-time for a year, I’m still learning new tools (and how to use them in a collaborative setting).

The most important thing I would say for aspiring analysts is to just build proof that you are interested in building your career as a data analyst.

You don’t have to be a coding pro, or a flawless dashboard builder.

But you will need a few projects (and, even better, some relevant experience) to showcase that interest and your foundational knowledge of the required tools – on your resume.

Because at the end of the day you are applying for entry-level/junior analyst roles, and you should be someone who is coachable.

Having a good data analyst resume (with projects on it) is important – and you can learn how to write one here…

But you also need strategies while job searching – beyond sending online applications (cold) to 100+ openings – otherwise you won’t go very far. You can learn about how to improve your likelihood of success here.