How to Become a Data Analyst | Advice from a Senior Data Analyst

Data analysis is among the most in-demand skills in 2021. It is a field with many facets - from working with raw data to uncover actionable insights to knowing how to showcase these in a way that’s easy to consume, data analysis can help any project - big or small.

As someone who has worked in the data analytics industry for a long time, I get asked a lot of questions by new data analysts or people who want to change their careers. 

So let’s look at some commonly asked questions along with some extra advice from a seasoned data analyst.

Here are the questions that I’m going to answer

• What is data analysis?
• What does a data analyst do daily?
• What is the number one tool for analysts?
• What advice would you give to new analysts?
• What are the skills a data analyst needs?

What is data analysis?

So what is data analysis? Simply put, Data analysis is a process of inspecting, cleaning, modeling and presenting data to support business decision-making. 

This is a field you can jump in at any time in your career, and the great thing about it is that having a diverse background can often make you a more valuable analyst. So there’s a good chance that your past job experience will give you an edge depending on the domain or field that you're going after.

What does a data analyst do daily?

As a data analyst your daily work will consist of:

• Defining requirements - specifying what questions the analysis has to answer
• Data wrangling - cleaning and getting the data in shape for the next steps
• Ad hoc reporting - where you need to get a report out very quickly
• Analysis - the meaty part of the process. This is where you actually analyze the data
• Visualization - presenting the findings in a way that’s easy to understand for non-analysts

Majority of your time will be spent on data wrangling, so you're going to be cleaning and getting data in shape.

What is the number one tool for analysts?

Microsoft Excel !!

The most common tool used to collect and deliver data is still Excel - and it’s not going anywhere anytime soon. Many people are familiar with Excel and you should definitely take some time to get yourself comfortable with it.

Some other tools and skills that you should try to get familiar with are:

• BI tools such as Power BI and Alteryx
• Data Visualization software such as Tableau and Power BI
• SQL for Database querying - extracting data from a database. There are many tools you can use for this but the language is very similar for each.

What advice would you give to new analysts?

A lot of times we forget that we will have to convince others of our findings, and also put it in a format that is digestible for most people.

So the first thing I would recommend is to develop PowerPoint skills. These skills will help you to present your findings in a format that would make it easy to understand for most people.

Second would be to become better at storytelling. Telling a story by adding context  and a narrative while presenting the data would go a long way for you. Don't just present the data.

Next, learn how to read the room. Improve your EQ. Sometimes the people you would be presenting to might have a different handle on the data or might not have access to the same tools as you do. Speaking in a more sophisticated manner or dumbing down might be more helpful depending on the situation. You’ll also have to know how to understand the questions that people are asking.

Fourth, be tool agnostic - meaning stay unbiased towards the use of different technology tools and software. This is because many companies use different tools depending on their contracts, so you should get used to as many tools as possible. 

But don’t worry. Once you learn the fundamentals, you’ll be able to pick up the workings of any new tool.

Last, I would recommend building a portfolio for yourself as it would showcase your plus points and that you are capable of analysis. It is much easier to show the work you’ve done than explain the same in words.

What are the skills a data analyst needs?

The skills needed by a data analyst are divided into three types, entry level, intermediate, and advanced.

Entry Level

• The most important thing an entry level analyst needs to learn is statistics. As an entry level analyst you are mostly going to communicate basic statistics so the earlier you get used to things like mean, mode, variance, standard deviations, etc. the better it will be for you.
• Another thing you will be asked to do a lot is to prepare spreadsheets. You need to know how to use Excel and Google Sheets at entry level. So get to mastering some functions like SUMIF, COUNTIF, VLOOKUP, etc.

Intermediate level

• You should learn SQL which would allow you to extract data from a database. Usually the IT team handles such tasks, but knowing how to write your own SQL queries to identify and shape data would give you an edge and make you a more valuable analyst.
• Data visualization is another important skill you must hone in order to make sure that whenever you present your data, people are able to understand and grasp the information you are providing with ease. 

Advanced

• For advanced data analysts the next skill you should learn Python or R. This will allow you to perform tasks such as making statistical modeling predictions and forecasting. Even though these tasks can be performed in BI tools, having knowledge of these skills is asked from a data analyst at an advanced level.

Now let’s look at some important focus areas for putting your best foot forward in this exciting new field.

Analysis Framework

Definitely establish an analysis framework - meaning a pre-decided method to best tackle the problem.

With an established analysis framework you will be better able to handle the large amount of data you possess and learn how to ask the business stakeholders the correct questions.

• The first question you would want answered is what does the success look like for the project. The goal at hand can be different for each project. For example,  get more signups registered for an app or to increase the revenue of the company. Make sure the goal is always Singular and Focused.
• Second, look for KPIs that support the goal - three to five top metrics at most. Ideally these should not be very diverse.
• Next you want to find out what the trends are. For example, how does it change over time or how is this affected by other factors. You’d  want to look for the top two-three causes for the trends discovered.
• Lastly based on your analysis, you’d define the action that you are suggesting to solve the issue. One thing to look out for is to not create very complex fixes, as you want quick fixes in order to get quick wins. These quick wins can then be broken into solving a bigger problem.

This is an example of a simple analysis framework. Of course you can change up the framework according to your requirements but following a framework is always the right way to tackle a large amount of data rather than haphazardly doing analysis indefinitely. 

Storytelling

I would like to touch up on storytelling again and expand on this skill because at the end of the day you want to convince people about your findings and subsequent suggestions.

You want to be able to:

• Create an analysis
• Take that analysis and visualize it
• Create a narrative
• And then give context to it

The only way you can establish a narrative is by getting the right context. You’d do this by working closely with the business owners or stakeholders. So make sure you focus on storytelling as it is very important for an analyst.

Certifications

I also get asked about different certifications available online and if any of them are actually good. I would recommend two certifications for people looking to dive into data analysis. These courses are Google Data Analytics Professional Certificate, and the IBM Data Analyst Professional Certificate. Although these certifications provide proof to the employers that you have completed some practical training and possess the skills required for data analysis, you can always learn analysis on your own.

If you want to see some projects here are couple of analysis projects that I have prepared: