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March 30, 2021

Top skills for a Data Scientist


Edwin Lisowski

CSO & Co-Founder

Reading time:

13 minutes

Data science is a fantastic career opportunity to pursue, especially since there is still a significant workforce shortage in this discipline. But what is data science all about, really? What do you need to become a successful data scientist? And what are the most wanted skills for a data scientist? This is what we are going to talk about in this article.

Before we start analyzing data science skills, let’s talk about what data science really is. Such an analysis will help you understand better what are crucial data science skills. Let’s get right to it!

Data science: The essence

As Wikipedia tells us, “data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data”. In other words, data science studies and analyzes data. We’re not talking here, however, about any studies and any analysis. It has to be vital from the business perspective. In fact, the most significant purpose of data science is to support business managers in growing their companies and projects.

Now, in the business environment, data science has five fundamental applications and goals to achieve:

Identifying trends and patterns in big data

For starters, we ought to state that data science studies big data in order to find trends and patterns in it.

On our blog, we frequently mentioned that big data could not be analyzed without the tools provided by data science. That’s because companies are dealing with such vast data sets that manual analysis would be immensely time-consuming and unprofitable from an economic standpoint. Thanks to data science, organizations can work with big data effectively and analyze even large datasets.

The key purpose is to discover trends and patterns in data. What for? There are a couple of reasons, but the most important one is this: To make better, more informed decisions and facilitate the company’s growth. With data science, you can discover specific patterns in your marketing campaigns or customer behaviors and use this knowledge to your advantage.


Identifying anomalies in data

Generally speaking, anomalies in data are common across various industries and sectors. In some cases, they are perfectly harmless, and business owners don’t pay that much attention to them. However, in some sectors, like financing and banking, anomalies are particularly critical to spot as quickly as possible. Primarily because they usually mean some fraudulent activity that needs to be stopped.

Let’s take a simple example of a bank that detects unusual activity on a specific bank account. In order to prevent money laundering and other frauds, the bank can block further activities on this account, contact the owner, and ask them to clarify the facts.

anomaly detection

Predicting the likelihood of future events

Here’s an interesting application of data science. Thanks to the analysis of historical data, you can make specific assumptions regarding future events and scenarios. That’s why we need something called predictive analytics. We talked a lot about them in the previous blog post. Predictive analytics is a discipline that’s based on data science and machine learning alike.

ML algorithms analyze historical data and other variables (market conditions, economic situation, customer behavior, marketing activities, etc.) in order to try and predict the future. For instance, predictive analytics could be used to assess future sales levels in your company. If what you discover is not satisfying, you can take corrective actions ahead and prevent adverse scenarios from happening. Yes, with data science and machine learning, it’s possible.

future, pink, keyboard

Uncovering correlations in data

Why didn’t we mention this aspect in the “trends and patterns” section? Because it’s not the same thing. Here, we want to talk about causations and correlations. Although one shouldn’t mix these two aspects of data analysis, both of them are crucial for understanding what really happens with your big data.

Again, let’s take an example. In many situations, companies are aware of something happening; let’s say there is a decrease in website visits. Suddenly no one is entering your website. This situation instantly raises the question, why? And this is where data science, or rather causation and correlation, save the day. With big data (in this case related to marketing and website activity), you are on a straight course to discovering what caused the drop in the first place. Perhaps there is a bug on your website, or maybe it was attacked by hackers? Or the reason can be entirely external.

For example–Google has just changed some of their algorithms, and your website is not compliant with them. In many instances, looking for an answer can take some time. That’s why you need a quick and efficient tool. Yes, data science!

As you can see, data science is indispensable in a modern business environment. The analysis of big data helps organizations around the world in:

  • Making more accurate and informed business decisions
  • Working more efficiently
  • Understanding big data and drawing useful conclusions from it
  • Avoiding potential adverse scenarios and events

It seems that data scientists have a lot on their plates, right? To some extent, that’s correct. But it’s a vital and necessary profession that will help you find a satisfying job for many years to come. And this is what we are going to talk about now.

analysis, business meeting

Skills for a data scientist

You already know what data science is all about. It’s easy to draw a conclusion about what a data scientist does. In general, they analyze, process, and model data in their companies. They also play a critical role in interpreting the results and presenting actionable plans and insights to the managers and CEOs.

The next thing you have to know is that data science is a multi-role discipline that requires at least basic knowledge (but, naturally, the greater knowledge, the better) in the following fields:

  • Computer science
  • Math
  • Statistics
  • Software development
  • Machine learning
  • And, in fact, business too!

Of course, no one is omniscient, but you have to feel super comfortable with math and statistical analysis if you want to be a successful data scientist. The programming knowledge will also be immensely helpful.

You should especially gather knowledge regarding big data platforms, cloud computing tools and services, and data warehouses and data lakes. It would be helpful if you knew something about data visualization and reporting as well. In fact, data visualization is a vital aspect of data scientists’ work.

And, as you will discover further in the text, some soft skills are also necessary. After all, you have to communicate with other team members and know how to present your findings.

Skills for a data scientist

Business knowledge and cooperation

Business knowledge is also necessary. You see, data scientists work with and on data, but their actual role is to turn big data into useful business-wise knowledge. It would be extremely helpful if you understood how your company (and business in general) works.

Moreover, you have to realize that a data scientist is not some kind of lone island. They are usually a part of a larger team and work with other departments throughout the organization:

  • Marketing
  • Customer service
  • Project management
  • Operations

The list of data science skills can be intimidating, correct? Well, that’s because it’s a complex, multi-disciplinary field of expertise. It takes years to become an outstanding, experienced data scientist.

Let’s go further. You cannot forget that this job is closely tied with AI, machine learning, and business intelligence. This doesn’t mean that you have to be a machine learning specialist, no one expects that, but the thing is, you have to grasp the big picture. You have to understand deeply what’s the purpose and idea behind ML.

One of the significant challenges every data scientist faces every day is to turn messy, unstructured, disorganized data, originating from various sources into organized and neat datasets that can be used for business intelligence purposes and can be easily stored (that’s why you need to understand how data warehouses work).

Business knowledge among skills for a data scientist

Other data science skills

Now, let’s go back to the soft skills question. First of all, you have to be able to present the results of your work in plain, understandable language. You have to understand that many managers and business owners simply don’t have such a thorough understanding of data as you. But it goes further.

To some extent, you’ll also need… storytelling skills. That’s not a mistake! You are the “voice” of data in your company. It’s your job to make your “story” appealing and interesting for various groups of listeners. You should be able to draw a clear picture that originates with data for the staff members, the managers, and sometimes even clients. What can help you improve your storytelling skills? Data visualization! Find out what are available solutions and tools. When you have everything transparently presented on a chart or infographic, it’s half of your success!

Interestingly, one of the vital skills every data scientist needs is the problem-solving approach. In many instances, this trait will open you more doors than academic qualifications. Present yourself as a proactive and deeply curious team-player who doesn’t hesitate to share their point of view and always looks for the best solution. This will win your future employer’s attention. Also, remember that a problem-solving attitude also requires great work organization and the ability to manage many projects simultaneously.

problem-solving approach, puzzles

Skills for a data scientist: technical skills

We already told you that you need to be great with math and statistical analysis. You also need to know how various machine learning models (just to mention regression, classification, and clustering) work and be skillful with MS Excel, R, Tableau, SQL, and Python (this language is commonly used in data science and AI).

Also, bear in mind that the vast majority of employers in the data science field are interested in candidates who can use quantitative techniques to reach a solution based on available data. Quantitative research deals with numbers, logic, and an objective stance. These techniques are concentrated on numeric and unchanging data and detailed convergent reasoning. That’s what you should try to master.


Data science skills: education

First of all, a bachelor’s or, even better, master’s degree in math, computer science, statistics, engineering is a huge plus that will help you get ahead of other candidates. The fantastic way to distinguish yourself and present interesting added value is to obtain a degree in a field you’d like to work in, such as marketing or finance.

This way, your employer knows that you will easily find yourself in this job and thoroughly understand the industry your employer operates in. So, if you’re after a job at a financial institution, get some education in this field as well. It will surely help you stand out from the crowd of other candidates.

Also, there is a noticeable trend of requiring a specialized data science certification or degree. According to CIO, currently, the top 15 data science certifications that you can try to obtain are:

  • Certified Analytics Professional (CAP)
  • Cloudera Certified Associate (CCA) Data Analyst
  • Cloudera Certified Professional (CCP) Data Engineer
  • Data Science Council of America (DASCA) Senior Data Scientist (SDS)
  • Data Science Council of America (DASCA) Principle Data Scientist (PDS)
  • Dell EMC Data Science Track (EMCDS)
  • Google Professional Data Engineer Certification
  • IBM Data Science Professional Certificate
  • Microsoft Certified: Azure AI Fundamentals
  • Microsoft Certified: Azure Data Scientist Associate
  • Open Certified Data Scientist (Open CDS)
  • SAS Certified AI & Machine Learning Professional
  • SAS Certified Big Data Professional
  • SAS Certified Data Scientist
  • Tensorflow Developer Certificate

Of course, you don’t have to pursue all fifteen at once! Start with just one. Read about these certifications, see what requirements are, and try to meet them. The general rule says that all of these organizations (Tensorflow, Google, Microsoft, IBM, Dell, Cloudera) are world-famous and respected. Therefore, no matter which certificate you choose, it will open many doors for you!

data science certification

Is it a job for you?

Obviously, you have to answer this question yourself. Without a doubt, if you feel comfortable with numbers and programming, it’s a career worth pursuing. The demand for data scientists will only grow, and it’s a worldwide trend.

Also, as a data scientist, you will make some decent money. According to GlassDoor, the average base pay for a data scientist is slightly over 113,000 USD per year[1]. When we checked these numbers about a year ago, it was around 105,000 USD, so it’s a clear upward trend.

And what about work perspectives? For 4 years in a row, a data scientist has been named the number one job in the United States by Glassdoor. What’s more, the US Bureau of Labor Statistics reports that the demand for data science skills will drive almost a 30% rise in employment in the field through 2026[2]. The January 2019 report published by, showed a 29% increase in the demand for data scientists year over year and a whopping 344% increase since 2013[3]. Additionally, as LinkedIn indicated in their report from August 2018[4], at the time, there was a shortage of over 150,000 people with data science skills!

Today, things are not much better. According to The Data Scientist Shortage in 2020 infographic, there are 3x more job postings than job searches in the data science field! And, as QuantHub[5] estimates, the workforce shortage in 2020 was as high as 250,000 employees! These numbers really show the problem!

To sum up shortly, yes, we have to admit that a data scientist’s career is demanding, but this job is also rewarding and exciting (if you understand numbers). And you have a multitude of possibilities for many years to come. There are thousands of companies worldwide that are desperately looking for data scientists. And money is better and better every year.

Does it all sound interesting? If so, you have a clear path to follow. And if you already have some knowledge or statistical background, feel free to send your resume to Addepto. Just like others, we are also looking for promising candidates! 🙂


[1] Glassdoor. Data Scientist Salaries. URL:,14.htm. Accessed Mar 30, 2021.
[2] Kelsey Miller. 11 Data Science Careers Shaping Our Future. June 4, 2020. URL: Accessed Mar 30, 2021.
[3] Brian Holak. Demand for data scientists is booming and will only increase. 31 Jan 2019. URL: Accessed Mar 30, 2021.
[4] LinkedIn. August LinkedIn Workforce Report: Data Science Skills are in High Demand Across Industries. Aug 10, 2018. URL: Accessed Mar 30, 2021.
[5] Jen DuBois. The Data Scientist Shortage in 2020. Apr 7, 2020. URL: Accessed Mar 30, 2021.


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