How to Become a Data Analyst

I’ve been working with data for some 20 plus years as of the writing of this post. In the video below I captured my thoughts on the required hard and soft skills it takes to succeed as a data analyst. If you are looking to start your career in data as someone who has not yet graduated or as someone with tangential work experience, then this video will serve you well.

Do You Need a Computer Science Degree to be a Data Analyst?

This question is frequently asked by people such as yourself looking to make a move into data. The answer is no. You do not need a computer science degree to have a very successful data career. In the video I give my thoughts on computer science, but the reality is that although it may be helpful from a “getting a first job” perspective, it is not a requirement to succeed. Although I have an undergraduate computer science degree from Clark Atlanta University (shout-out to HBCU alums), some of the brightest minds I’ve worked with in the data space do not have a computer science degree. Bottom line; a formal computer science degree certainly helps but it is by no means necessary. All you need is the willingness to learn the tools and the perseverance to get your first data opportunity.

Hard Skills Required (View Video)

I’ll give you a hint, data visualization skills are a must and Tableau is the tool of choice for me.

Soft Skills Required

I’ll keep it short here and simply state that you should always look for ways to differentiate yourself and not just be seen as an interchangeable commodity worker. To paraphrase famed Harvard professor Michael Porter, a differentiation strategy advocates that a business must offer products or services that are valuable and unique to buyers above and beyond a low price. In this metaphor, think of yourself as a business and you bring multiple skill sets to your employer (other than being a single focus technical employee who can be easily outsourced for a lower price).

To be a differentiator, do not think of yourself as just being a tool specific analyst. Learn how to take requirements, communicate well, develop exceptional writing skills for business emails and documentation. Finally, learn how to present your analyses to people several pay grades above yourself when required. You want differentiation to be your competitive advantage. You do not want “low cost” to be your advantage for obvious reasons (if you’re like me, you want to be paid fairly for the value you provide).

Future Career Paths

In our jobs we desire mastery, autonomy and purpose. After a certain point in your career you may want to take a leap from the descriptive analytics path and move towards a predictive analytics path. Descriptive analytics (think data analyst or traditional business intelligence reporting analyst) deal with what has happened in the past while predictive analytics focus on what will most likely happen in the future. In order to level up in predictive analytics, you will need python, statistics, probability, and/or machine learning skills.

If you want to make the leap from data into management, you can consider obtaining an MBA or a masters degree in Management Information Systems. I happen to have an MBA from the Georgia Institute of Technology and a masters degree in Information Management from Syracuse. This may seem like a bit of overkill but I work in consulting where credentials are overly appreciated by clients (and I am a lifelong learner).

Interact with my Tableau resume here.

Conclusion

A career in data can be fun (in the early learning phases) and lucrative (mid to late career). In my case it has been a fulfilling career ever since I started work as a data analyst at General Motors many years ago. I turned myself from a commodity to a differentiator by not only learning the basics but also adding business understanding and a willingness to share what I know on this blog and my YouTube channel. I know that you can do the same. If you put in the time to learn along with the perseverance to land that first data role, you won’t need much luck at all to accomplish your goals.

Looking to land that first role or trying to move ahead in your current role? Then check out this post for the Keys for a Successful Career as a Data Analyst.

-Anthony Smoak

All views and opinions are solely my own and do not necessarily reflect those of my employer

I appreciate everyone who has supported this blog and my YouTube channel via merch. Please click here

Thank you!!

Advertisement

Build Better Sparklines in Tableau

So you want to add some spice to your bland looking Sparklines in Tableau? You have come to the right place (start by watching the video above). Let’s talk about how a Sparkline is defined per Wikipedia:

“A sparkline is a very small line chart, typically drawn without axes or coordinates. It presents the general shape of the variation (typically over time) in some measurement, such as temperature or stock market price, in a simple and highly condensed way. Sparklines are small enough to be embedded in text, or several sparklines may be grouped together as elements of a small multiple. Whereas the typical chart is designed to show as much data as possible, and is set off from the flow of text, sparklines are intended to be succinct, memorable, and located where they are discussed.”

Here are a few examples of Tableau specific sparklines in action (with latest complete month bubble indicators and reference lines): Notice how I do not include any data axes, but you can clearly recognize the data trends in the visuals.

Here is an example of how I used the sparklines demonstrated in the video to build a out a classic yet refined looking Tableau dashboard.

Interact with and download this workbook here.

For reference purposes I am going to list three formulas used in the completion of the sparklines, you’ll have to watch the video to learn how to put them together.

In this exercise I am using that standard Tableau Superstore data set which you can perform a Google search to find if you are using Tableau Public.

Calculated Fields

Calculated Field #1 (Name: SPRK_CircleMonths)

This calculated field puts a circle on the penultimate month data points. Penultimate is just a fancy SAT word way of saying “next to last”. When the month of the data point on the line chart (Order Date) equals the next to last order date month in the dataset, then return the Order Date.

//IF THE MONTH OF THE DATE ON THE LINE CHART EQUALS THE MONTH-1 OF THE MAXIMUM DATA POINT
// THEN RETURN THE DATE
If DATEPART('month',[Order Date]) = DATEPART('month',dateadd('month',-1,{MAX([Order Date])}))
Then [Order Date] END

Calculated Field #2 (Name: SPRK_CircleMonths)

This logic will be applied to the circles generated by the previous calculation SPRK_CircleMonths. Only the next to last month will meet the TRUE condition (which will be colored as red).

// IS THE MONTH OF THE CHART DATE EQUAL TO THE MOST RECENT DATE MONTH MINUS 1 MONTH
// E.G., NOV 2018 = NOV 2020 WILL RESOLVE TO TRUE DUE TO MATCHING MONTHS
DATETRUNC('month',[Order Date]) = DATEADD('month',-1,DATETRUNC('month',{max([Order Date])}))

Calculated Field #3 (Name: SPRK_RefLine Profit)

This logic will return the profit associated with the next to last month in the dataset to display on the reference line.

// RETURNS A VALUE USED FOR THE REFERENCE LINE
// IF THE MONTH OF THE DATE = THE MONTH OF THE MAXIMUM DATE MINUS 1 MONTH (GET A COMPLETE FIRST MONTH)
if DATETRUNC('month',[Order Date]) 
= DATEADD('month',-1,DATETRUNC('month',{max([Order Date])}))
THEN [Profit] END

When you put all the functions together in a manner according to the video, you end up with a more refined sparkline in my opinion. Big shoutout to the Data Duo for the inspiration on the dashboard I created and this technique. If you haven’t checked out any of their work make sure to do so.

Please like and subscribe on the Anthony B. Smoak YouTube channel.

All views and opinions are solely my own and do not necessarily reflect those of my employer

I appreciate everyone who has supported this blog and my YouTube channel via merch. Please click here

Thank you!!

Anthony B Smoak