Data Scientist Skills (With Examples and Tips to Improve)

By Indeed Editorial Team

Updated 13 August 2022

Published 6 October 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data science is a constantly growing sector, with applications in different industries. Skilled data scientists can choose from a wide variety of information technology (IT) jobs. Before you apply to those jobs, it is important to understand the skills and qualifications that can make you a great candidate. In this article, we outline some of the most in-demand data scientist skills and discuss how to develop them.


Related: These Highest Paying Jobs Are Waiting For You


What Are Data Scientist Skills?

Data scientist skills include technical and mathematical skills, research skills and communication and teamwork skills. You may need to develop hard skills, such as mathematical knowledge and technical skills, as this career field can deal with the operation, processes and applications of technical data. Data scientists often also have a variety of soft skills that can help in their career performance. Written communication, analytical skills and the ability to work on or direct a team may all be skills that employers look for when hiring a data scientist.

Related: 50 Informatica Interview Questions (With Example Answers)

Hard Skills For Data Scientists

Here are a few technical or hard skills a data scientist uses to do their job well:

Mathematical skills

Data scientists focus on applied mathematics, especially algorithms, logarithms, calculus and statistical mathematics. Key concepts here include descriptive statistics concepts like mean, median, mode, variance and standard deviation. It is also important to study probability distribution. In this area, you can study topics like samples, skewness and kurtosis, hypothesis testing and confidence intervals.

Statistical skills are a crucial part of data science, which relies heavily on algorithms, statistical data and mathematical formulae to process and quantify data.

Programming Skills

You need to communicate with machines to take advantage of technology like machine learning, which relies on heavy computing power. This is where you need programming knowledge. You need not become an expert programmer, but it helps to have a working knowledge of languages like:

  • Python: This is a general-purpose programming language. It includes multiple data science libraries and rapid prototyping capabilities.

  • R: This language serves you well in data visualisation and analysis and statistical and mathematical computation.

  • Julia: Julia combines the functionality offered by Python and R with greater speed. It features programmer-friendly syntax like Python, with a performance that can compete with C.

  • Java: Most software engineers are familiar with this high-level language, which is one of the most popular general-purpose languages. Big data applications like Hadoop and Hive are coded using Java, which also features machine learning libraries like Weka.

  • C/C++: These are low-level languages but come with incredible computational speeds.

Related: Similarities and Differences Between C++ and Java


Data manipulation and analysis

Also known as wrangling, data manipulation is the process of ‘cleaning' the available data. To manipulate data, you rearrange and restructure it into an easier-to-analyse format. Techniques include:

  • Row and column filtering

  • Join and concatenation

  • Missing value imputation

  • Scaling

  • Outlier treatment

  • Regression

  • Classification

  • Using mathematical formulae


Related: 10 Valuable Data Analysis Skills


Data visualisation

This is the art-inspired side of data science. Using various tools, a data visualisation expert builds a story that explains the data. Most languages like Python, R, Julia, Java and C help you visualise data using extensive libraries for advanced charts. This includes data plotting methods like pie charts, histograms and bar graphs. MS Excel also offers advanced charts like Pareto, Waterfall and Funnel Charts. To improve your skills further, study the basics of a tool like Tableau that helps you build impactful data visualisation. Business intelligence, analysis and data experts usually prefer Tableau for this purpose.

Machine learning

In data science, machine learning helps you build predictive models that inform businesses of patterns. For example, you can use machine learning to predict a company's lead conversion rates for the next month based on historical lead conversion data from the same company.

Simple models include linear and logistic regression models. To cut down your chances of error, turn to ensemble models like Random Forest, XGBoost, and CatBoost. These can help you with hyper-parameter tuning, ultimately enabling you to build a low-error model.

Big data

The internet and social media have grown exponentially in popularity. Technology like IoT (the Internet of Things) is also becoming more commonplace. For a data scientist, this means you get to work with an abundance of data. Big data technology helps organisations make sense of all this otherwise overwhelming information. To process big data, you need to get familiar with tools like Hadoop, Hive, Spark and Apache Storm.

Technical Skills With Accompanying Certification

In addition to giving you the required skill, certificate courses highlight your expertise and professional development to a potential client or employer. The following data scientist skills are available as certification courses:

  1. Certified Analytics Professional (CAP): This certification is an industry standard awarded by INFORMS after you take a three-hour exam. It is platform and vendor-neutral and is valid for three years.

  2. Cloudera Certified Associate Data Analyst: Valid for two years, this certification shows that you can effectively use SQL developer skills to pull and generate reports in Cloudera's CDH environment using Impala and Hive.

  3. Cloudera Certified Professional (CCP) Data Engineer: This requires data engineering experience and a high level of common data science skills. The challenging four-hour exam requires a 70% score for a 'pass' result.

  4. Data Science Council of America (DASCA) Senior Data Scientist (SDS): This is recommended for people with an undergraduate degree and five or more years of experience. To prepare, build your knowledge in subjects like databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, object-oriented programming and RDBMS.

  5. Data Science Council of America (DASCA) Principal Data Scientist (PDS): This permanent certification has four tracks and is for data science professionals with over ten years of experience. Three tracks require you to have a master's degree.

  6. Google Professional Data Engineer Certification: You are an ideal candidate for this if you understand the Google Cloud Platform (GCP), and have designed and managed solutions using it. Credentials do not expire.

  7. SAS Certified AI & Machine Learning Professional: To earn this, you take the SAS Certified Specialist exams in Machine Learning, Forecasting and Optimisation and Natural Language Processing and Computer Vision. Your certification indicates that you can use open-source tools to drive insights from data using AI and analytics skills.

Soft Skills For Data Scientists

Here are a few soft skills a data scientist often uses to order to succeed in this role:

Communication and storytelling

Data scientists may often provide written accounts of the projects they work on. Storytelling comes in when you need to communicate your insights from data analysis to someone who does not understand complex statistics. A bar chart might not be engaging enough for the average business stakeholder. You can help explain its significance and the impact of the data when you have worked on your storytelling ability.

Curiosity, and a commitment to learning

Asking questions is an extremely useful habit in the data science field. As a data scientist, curiosity needs to be a part of your lifestyle and the way you work. Train yourself to always ask questions like:

  • Why did something happen?

  • What caused it to happen that way?

  • What can I do to change the way it happened and improve the outcome?

  • What is the business impact of this change?

You can choose to follow the machine learning lifecycle, but cultivating a curious approach helps you achieve effective results consistently as a data scientist. Data science is constantly evolving and changing, and this attitude of constant learning is a great way to keep up.

Disciplined thinking

You are likely to be responsible for several different tasks each day. You might have to complete a report on a project from the previous week, give a presentation of your findings from another project and then get started on a third different project. A methodological approach can help you maintain order even as you work on these three projects.

As a data scientist, you often need to look at problems from different perspectives. Disciplined thinking helps you keep focused, spend your time effectively and not get lost within a single task.

How To Improve Your Data Science Skills

Here are a few steps you can take to develop your current skills or to learn new skill sets:

1. Always be learning

Earn a postgraduate degree, such as an MS in data science. Attend events and sessions on data science-related topics, like machine learning, big data and AI. Enrol in courses that focus on specific roles within data science. Attend software training sessions that focus on a particular application like Hadoop.

2. Gain work experience

Working in different industry verticals introduces you to the broad scope of data science and can open up opportunities. This is especially useful if you are starting out in this field. Look at entry-level positions as free opportunities to learn. You can even request an experienced supervisor to mentor you and help shape your learning and career path.

3. Network through peer groups and open-source projects

Join communities of peers to discuss trends and developments in the field, share ideas and collaborate on projects. Networks like these lead you to job openings as well. In open-source projects, communities of data scientists meet (online) to analyse, manipulate and visualise data. Simply watching experienced data analysis professionals at work can help you understand how to process and evaluate data better than any college course can explain.


Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.

Explore more articles