Top Data Science Skills By Job Role

Do you have a fair idea about data science? I hope, yes. How about its skill-sets?

For newbies, Data Science is the field which is intersecting Statistics, Mathematics, Programming/Technology and Business. Using a combination of all these components, insights, models can be drawn using the data. It was a term coined in 2001 by a Professor of Statistics, William S. Cleveland at Purdue University.

I read at least 5 articles from the web today to understand the nuances of this role. Little did I know prior to my research that there are lot many things attached to the buzzword “Data Science”.

Broadly speaking, the skill-sets required to be a Data Scientist (as the way many companies call the folks who work in Data Science domain) fall under the following skills viz,

  1. Business
  2. Technology
  3. Math & Modelling
  4. Programming
  5. Statistics

We can even sub-classify them into further such as Programming – R, SAS, Python, SQL, NOSQL to name a few.

Business2Community features an article written by Bob Hayes. He came up with a questionnaire listing 25 data science skills, captured the responses, analyzed and ranked the top 10 skills from the results based on the proficiency level as “Intermediate” criterion.

25 Skills in the Data Science by AnalyticsWeek and BusinessBroadway
25 Skills in the Data Science by AnalyticsWeek and BusinessBroadway

Top 10 Data Science skills in general are:

  1. S – Communication (87% possess skill)
  2. T – Managing Structured data (75%)
  3. M – Math (71%)
  4. B – Project management (71%)
  5. S – Data Mining and Viz Tools (71%)
  6. S – Science/Scientific Method (65%)
  7. S – Data Management (65%)
  8. B – Product design and development (59%)
  9. S – Statistics and statistical modeling (59%)
  10. B – Business development (53%)

It’s interesting to note the fact that Communication stands first when compared to other skills! Catering either to internal or external customers, the data scientists talk to business functions such as Marketing, HR, Operations, Finance etc.I think it makes sense because what’s the point if one works hard at developing a model but not conveying the results as per the business needs. Guess what? “Data Presentation” has become one of the top 10 skills in 2016 published by LinkedIn.

Bob also charted out the top skill sets by job role level. This one is another interesting perspective.


Top Data Science Skills by Job Role from Business Broadway
Top Data Science Skills by Job Role from Business Broadway

Researcher can focus more on Statistics; Business Manager on communication, project management; Developer mostly on the programming aspects and so on.

With that said, it’s very tough to focus on learning all the skill-sets of a typical data scientist at a single stretch. Depends on who you want to become, the above list would be beneficial for you. Hence, you can prioritize and narrow down to the list and start learning one at a time! If you’re already good at Statistical concepts, try learning how to program the techniques using “R” programming language. This way, I think one can steadily adapt to the data science skill-sets.

Please remember that there’s no one size fits all approach! If your buddy is good at programming because of his formal educational background being from a software discipline and moving faster on a learning curve, that’s perfectly okay for you to keep up at your pace depending on your comfort level. At the core of data science, you can be really good at one skill-set and know the basics & become eventually to an intermediate level at another skill-sets.

My focus will be on statistics to begin with. What is yours right now?


Skill Sets Required For Analytics

I’m a computer programmer. Can I transition my career or line of work towards Analytics?

That’s what I asked me a question 2-3 years. I didn’t have the answer back then. You too may be curious to know what it takes to be in the Analytics profession?

Firstly, you may have to understand the difference between analysis and analytics. To start with, let’s think through this from the top-down approach perspective.

  1. What’s the business problem you’re intending to solve? The problem could emerge from your corporate, government or any for that matter. Without understanding the context & background, it’ll be very difficult to structure the problem! To go through this, domain expertise is essential. Having this skill enables you to visualize the big picture from Problem to Probable Solutions.
  2. Next step is the “How” part. How are you going to solve the problem? You may gather the data, do some analysis using any applied mathematics or statistical concepts, understand the relationship hidden in the data and come up with recommendations. Tools and Programming skills are essential at this stage to perform the steps.


In a nutshell, this is my understanding when it comes to the skill sets required for Analytics.


Well, hold on, don’t jump into making your conclusion! The tools, techniques, programmings evolve and one might replace the other during the course of time. The way to approach in this typical field of Analytics is to become good in one skill and try to learn the other skill sets at the earliest.

You can choose to become a go-to person when it comes to business acumen and you may also learn the A,B,Cs of frequently used Statistical models, tools and programmings. In my case, I have relatively good experience over programming and hence my focus would be on learning statistics, gaining business knowledge.

Each skill set mentioned above is very vast in itself. For instance, there are many statistical models available. There are many programming tools and languages that can cater to analytics these days. When it comes to business, you can drill down to Sales, Marketing, HR, Finance, Operations and so on!

You can choose your level playing field and try to learn something new each day. That’s the key to elevate I believe.

Here are the generic skill sets that I think are most relevant:


I firmly believe that any skill can be learned. By and large, Analytics is a multi faceted field! Four critical non-technical skills that are required are:

  1. Curiosity: This is the most important trait one should have. Curiosity to learn, be it in any domain, matters a lot. You can be really good in Sales/Marketing and if you wish to take up an assignment in HR, you should be open-minded to learn new business aspects.
  2. Analytical Thinking: Most often, we face problems that would be really challenging to solve! You need to frame the structured problem statement from the unstructured & vague problem area.
  3. Problem-Solving: You don’t need to be a pro in say programming. When you’re clear about what problem you need to solve using programming, you can research and design an algorithm. In good old school days, we use paper and pen. Nostalgic, right? We can get back to that era again by drafting the solution using the same way which is effective. Read articles, whitepapers online which would help you understand how somebody resolved the problems.
  4. Insights Storytelling: Interpretation of data from the analysis you carried is yet another crucial step of the analytics project. You would need to present the findings/solution to the leadership team in a layman’s language! Conveying your efforts in the form of a story and influencing others to buy your deliverable is an art!


The above holds true for data scientist skill sets as well. However, I’d encourage you to go through an article at KDnuggets.

What do you think is the number one skill required for Analytics? I’m looking forward to read your thoughts via comments below.

Often, we might hear the words – Analysis and Analytics – being interchanged in our usage. Are they quite similar? The answer is no. There’s a fine line of difference between these two terms. Here are my two cents 🙂

Analysis is a way to interpret the data and derive meaningful insights from the data. Essentially, you may use the analytical tools such as Microsoft Excel to plot the graph, pivot, chart to delve into the subject of interest. Let’s take a very simple example: Your executive wants to know, “Who are the top 10 salesforce folks who exceeded the targets this year in U.S. region?”. Well, you can extract the U.S. sales data from the tool and sort it by descending order to arrive at the top 10. Your leadership team might think of a surprise gift vouchers to them as a token of hard-work and determination!

Analytics: This also holds true in deriving meaningful insights from the data. The difference is, analytics involves statistical tools & techniques with business acumen to bring out the hidden patterns, stories from the data. I would say analysis is a sub-set of analytics whereas the latter involves some complex techniques to solve the problem. Ex: Google recommends you search ideas when you start typing your keywords. Let’s say, you want to know “how to make a website”. Google has the search data from your country’s demographics who had already searched about the similar keywords. Using machine learning algorithm in real-time, your search query is suggested by the search engine before you complete the keywords!

In a nutshell, Analytics = Business + Statistics (Applied Maths) + Computer Programming. Using the statistical tools & techniques, a business problem is solved and that’s analytics for you guys!

Next time, if somebody switches these two terms, ensure you get it clarified! It doesn’t change the context drastically but technically you should be well informed about the discussion points.

If you have any questions or anything to be shared, please write it in the comments section below. I look forward to hear from you.