Must-Have Data Analyst Skills: A Complete Guide

Every skill you develop as a data analyst adds to your efficiency and performance. But what if you aligned your skills with the actual stages of a data analyst's workflow? The result? A profile that stands out to recruiters. Turning you down would be nearly impossible.

In this article, we'll break down the key skills required for a data analyst role, mapped to the different stages of the workflow. This way, you'll not only know what to learn but also why it matters in real-world practice.

Know the Data Journey

In a typical day-to-day data analyst's role, the data journey comprises a series of six stages:

From Stages to Skillset

Stage #1: Set Goals (Objective of the Analysis)

An analyst's routine is very much like an expedition to uncover hidden patterns in data. And like any expedition, it cannot begin without a clear objective. A well-defined objective not only streamlines your process but also keeps you from falling off track midway.

To better understand each stage, let's follow the journey of data to insight with an example. We'll also see how each stage unfolds in a real workplace. Let's begin!

College hostel canteens and complaints are a match made in hell. You'll know better!

Why no momos on the menu?

Why so little variety in meals?

And the list goes on!

Here, while you can think of multiple areas of improvement, without a definite goal, you'll end up nowhere. Here are two solid goals for our analysis.

Pretty straightforward, right? And the skill you use for this stage? Pure common sense!

Well, that was a simple example. In workplace settings, you may sometimes receive direct objectives, but more often, the input comes as broad and vague business questions. This is where your critical thinking and communication skills help turn ambiguity into clear, specific and measurable goals.

Always remember, with clarity comes quality! That brings us to the next stage.

Stage #2: Gather Data (Data Collection / Extraction)

This is where you either collect fresh data or extract it from existing sources.

In our canteen scenario, both approaches work. On one hand, you might collect data through feedback forms, quick surveys, or group polls. On the other hand, you could extract sales logs from the canteen's billing system to see which items are selling the most.

What does this stage look like in an actual workplace?

Instead of just a handful of data, you would be handling huge datasets coming in from customer apps, websites, or transaction systems. With all that information stored in databases and servers, you would query the data, filter the relevant piece of information, and export it to your desired format for further analysis.

That leads us to our first and foremost technical skill as an analyst, querying the data. And how do you do that? With Structured Query Language, in short, SQL.

At this stage, curiosity is your best friend. Asking the right questions about where the data comes from and what it truly represents will help you collect meaningful information rather than mere numbers.

Stage #3: Clear Mess (Data Cleaning & Preparation)

Raw data, be it student surveys or canteen billing logs, is never perfect. Some students might leave answers blank, others might type random text, and sometimes you'll even see duplicate entries.

Let's say, a few students typed momos, and a few others typed dumplings. If you don't clean and standardise these responses, you'll miss the fact that they are actually talking about the same dish!

Cleaning such messy data is crucial, but the process itself can be repetitive and time-consuming. During this step, patience and attention to detail are what separate good analysis from guesswork.

Here are four technical skills for data cleaning in the workplace. See when each one comes in handy:

1. SQL again: Cleans data right at the source by removing errors, fixing inconsistencies, and filtering out what doesn't belong before export.
2. Spreadsheets: Microsoft Excel and Google Sheets have built-in functions to tidy up small datasets. You spot and correct duplicates, typos, and formatting issues after export.
3. Business Intelligence Tools: Power BI and Tableau Prep clean data visually, helping you reshape, split, or remove unwanted values while preparing dashboards.
4. Programming: Python and R act like heavy-duty cleaners, handling large, messy datasets and automating the entire cleaning process from start to finish.

And once the data is all set, you begin the actual analysis.

Stage #4: Analyse Data (Data Analysis)

The actual statistical analysis, reasoning and problem-solving begin here. You scrutinise data, manipulate it, ask questions, and look for trends, patterns, or anomalies.

For instance, you'll have to dig deeper into our canteen sales logs to understand which items are most purchased during weekends versus during lunch hours.

For this analysis at work, you can rely on the same four technical skills from the previous stage. With SQL and spreadsheets, you can summarise data, group values, and run quick calculations. With BI tools, you can explore data to spot seasonal patterns. Lastly, when your dataset is too large for spreadsheets to handle, you can use programming.

Stage #5: Picture Data (Data Visualisation)

Let's say, in our canteen data analysis, we've found these patterns:

1. 9 out of 10 want rice or roti at lunch
2. 1 in 3 go for lunch combos
3. 25% want healthier snacks
4. Tea & coffee sales peak at 11 AM
5. 60% of students spend less than ₹50 per day
6. Weekdays see 30% higher footfall compared to weekends

These are indeed based on calculations done on the spreadsheets. However, in a typical workplace, how you picture them matters just as much - after all, every stakeholder needs to get the message quickly.

So how do you do that? You let the data speak for itself by illustrating the same findings in Tables, Pie Charts, or Bar Graphs. Creativity is key. You need to choose the right type of visual that tells your data story without confusing the audience. Adaptability matters too because your visual should match what the stakeholder finds easy to follow, not just what looks fancy.

Coming to the technical skills, Excel and Google Sheets can give you quick visualisations. Tableau and Power BI can create interactive dashboards with which you can let the stakeholders filter and explore the data themselves. Lastly, for more customised visuals, you may use advanced plotting libraries in Python or R.

Stage #6 Present Insights (Reporting & Communication)

Numbers alone don't make an impact; how you explain them does. This is the stage at which you draw conclusions from the analysis and then make decisions or recommendations based on those conclusions. So, the catch is how you translate your findings into actionable insights.

Returning to our example, here are the key takeaways that align with the goals we initially set for our canteen menu.

1. The menu must always include rice or roti for lunch.
2. Strong combo demand indicates smart bundling can boost sales.
3. Sprouts, salads, and fruit bowls can be added to the menu.
4. Spending patterns can inform more effective pricing strategies.
5. Weekday rush and 11 AM beverages guide on when to stock and staff.

Clearly, decision-making, presentation, and collaboration are non-negotiable skills at this stage.Storytelling is another superpower to connect the dots and simplify findings for a non-technical audience.

The Complete Skill Set for Data Analysts

LHS = RHS

That's about the skills required for data analyst roles across the day-to-day workflow. Let's check how our recommendations align with real industry data.

A research into Naukri job postings, around 70-80% of entry-level data analyst roles call for a combination of these core skills:

1. SQL and Excel - They form the bedrock.
2. A visualisation tool - Tableau or Power BI
3. Statistical analysis - a growing necessity
4. Python fundamentals - a nice-to-have skill.

That pretty much sums it up. But hang on! There's more you can't ignore!

Clearly, foundational data analyst skills remain crucial, yet AI is reshaping how these professionals work. That’s why we keep updating our course curriculum to include the skills that data analysts need in today's age of AI.