Top Data Analyst Skills
By: Matt Przybyla
Tags: Data, Jobs, Data Analysis, Data Science, Careers

Table of Contents

Introduction SQL Spreadsheets Critical Thinking Statistical Programming Languages Data Visualization Summary References

Introduction

Data analysts can expect to be able to perform several different functions, ranging from quick tasks in an hour, to tasks spanned out over a month. With that being said, there are also skills that vary in technicality, as well as some soft skills, like communication, and all of these skills are incredibly important. In this article, I will discuss my opinion of these top five data analyst skills defined by Indeed [2], in addition to some other skills that I found particularly interesting. As a note, this article is intended for people who are looking to become a data analyst and want to know about the expected skills, as well as people who are generally interested in knowing the top skills for data analysts (if you are a seasoned data analyst, you most likely already know about all of these skills). Keep on reading if you would like to learn more about the top skills of data analysts in 2021.

SQL

Photo by Caspar Camille Rubin on Unsplash [3].

It comes as no surprise that this skill is number one. It is perhaps the most defining skill for a data analyst because it shows that data analysts, of course, need to be masters of data and analyzing it, which usually comes in the form of querying with SQL. Some queries might be requested by stakeholders for simple reports, while some queries can be more complex that are connected to other processes, such as datasets for training machine learning algorithms.

Here are some examples of how you could expect to use SQL as a data analyst:

Joining, aggregating, filtering data from a database table

Pulling CSV-like reports for stakeholders (raw data)

Creating more comprehensive reports from data like Tableau, Looker, etc.

Creating static or live connections (either one-off requests, or recurring requests setup in a report or dashboard)

As you can see, SQL is a powerful tool, and some data analysts might find themselves using SQL almost hourly, depending on the company and specific job description.

Spreadsheets

Photo by Mika Baumeister on Unsplash [4].

Frankly, sometimes, it can be easier to manage, view, and manipulate your data in a spreadsheet after querying it with SQL. Although it is not the most interesting skill, it is probably one of the ones that you will use the most frequently. Some popular spreadsheet platforms are Microsoft Excel and Google Sheets. I personally prefer Google Sheets because you can have live versions that are collaborative. You might be surprised by what you can do with a spreadsheet after you start answering some questions formed by stakeholders on the job.

Here are some examples of how you could expect to use spreadsheets as a data analyst:

Quickly visualizing data

Quickly sharing data

Highlighting certain data for comparisons

Ranking data

Grouping data (pivot table)

Utilizing the plethora of functions that spreadsheets include (or some of your homemade calculations)

Conditional formatting

While spreadsheets might not be the fanciest of skills to know, it is perhaps the most essential. You should expect to perform some of your work in spreadsheets because it is something that most stakeholders will know as well. Whereas SQL can be more specific, spreadsheets are a ‘language’ or form of communication that most people you are working with will know.

Critical Thinking

Photo by Jason Strull on Unsplash [5].

Of course, this skill is not unique to just data analysts, it is still important to note because of the tasks that you can expect to perform. It is difficult to give clear examples of critical thinking for all data analysts, since every job and company is of course different, but I will discuss some of the scenarios that I have encountered that you might expect on the job:

When creating dashboards, make sure that what you are grouping by is completed for that amount of time — so that you are not showing partial data — and making sure to note that to stakeholders

Note when there is missing data and if that is expected or not in your analysis

When working with stakeholders you will need to ask the right questions so that you are understanding their requests well

It is hard to say exactly what critical thinking steps you will take, but the overall gist is that you will need to be comfortable without knowing how to query right away, or the exact requirements of a task right away, until you employ critical thinking.

Statistical Programming Languages

Photo by Chris Ried on Unsplash [6].

Similar to spreadsheets, this skill may be referring to two specific skills of R and Python. However, it could also be referring to or encompassing SAS. Either way, all of these skills are incredibly powerful and can benefit you as a data analyst. Some data analysts might have a big part of their job centered around statistics and data science. This skill is useful for programmatically calculating statistical significance as well as A/B testing.

Here are some examples of when a data analyst would employ statical programming languages:

Programmatically calculating statistical significance

Using libraries that make statistics easier to access and apply

Performing experiments and hypothesis testing

Utilizing common statistical techniques like ANOVA’s (Analysis of Variance)

A/B testing for product comparisons

Outlier analysis

Data quality/data distribution

Regression

Feature/data importance

There are a ton of different functions that utilize statistical methodologies and can be performed efficiently with the use of programming languages. Not all data analysts are expected to have this skill, but if you do, you might be more competitive as an applicant. However, for some jobs or companies, it can be unnecessary, so it is up to you to understand the requirements from a job description to know for sure.

Data Visualization

Photo by Stephen Dawson on Unsplash [7].

The last skill in this article is somewhat a combination of all of the above skills. Because you will be collaborating a lot with stakeholders, the tasks or requests you will receive will usually start with data, and end with visualizing that same data. You can visualize data in a variety of ways, some of the more common ones are simple CSV reports, more complex Tableau reports with filters, Looker reports, and some through statistical programming languages like R, SAS, and Python.

Here are some examples of when a data analyst would employ data visualizations:

Articulating complex data in an easy digestible format (visual)

Sharing data or results to others easily

Comparing data

Viewing a lot of data in one spot, without having to scroll or flip through other locations

Visualizing data is one of the most beneficial skills for data analysts because it is the way in which you can translate your data, processing, querying, and results to others who are not data analysts, which are usually called your stakeholders.

Summary

While some of these skills might be obvious to some, my hope was to highlight the top skills for data analysts for those who are interested in the field. Of course, there are many more skills to discuss, some of which were lower on the ranking like machine learning, public speaking, and project management. These three other lower-ranked skills can be very beneficial to data analysts — ultimately depending on the specific job, which is why perhaps they were not ranked as high. If you are a data analyst who knows machine learning, you might be a better fit for a data scientist position. However, public speaking and project management are more necessary skills in data analytics, as you will have to know how to approach a request or project, manage it, and relay the results and insights to others, via public speaking.

To summarize, here are some key skills data analysts should know:

* SQL * Spreadsheets * Critical Thinking * Statistical Programming Languages * Data Visualization

I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with these skills discussed. Why or why not? What other skills do you think are important to point out in regards to data analyst requirements and expectations? These can certainly be clarified even further, but I hope I was able to shed some light on these skills for data analysts.

Thank you for reading!

I am not affiliated with any of these companies.

Please feel free to check out my profile, Matt Przybyla, and other articles, as well as subscribe to receive email notifications for my blogs by following the link below, or by clicking on the subscribe icon on the top of the screen by the follow icon, and reach out to me on LinkedIn if you have any questions or comments.

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References

[1] Photo by Ilya Pavlov on Unsplash, (2016)

[2] Indeed, 18 Key Skills for Data Analysts, (2021)

[3] Photo by Caspar Camille Rubin on Unsplash, (2017)

[4] Photo by Mika Baumeister on Unsplash, (2018)

[5] Photo by Jason Strull on Unsplash, (2017)

[6] Photo by Chris Ried on Unsplash, (2018)

[7] Photo by Stephen Dawson on Unsplash, (2018)