7 Skills Every Data Scientist Should Have

Data scientists use data to determine which questions teams should be asking and help teams answer those questions by creating algorithms and data models to forecast outcomes. The insights that data scientists uncover are used in business decisions to help drive profitability or innovation.

The most important skills data scientists need are technical skills, such as maneuvering and wrangling massive amounts of data to make sense of it all. But there is also a need for interpersonal skills, since data scientists work collaboratively with business analysts and data analysts to conduct analysis and communicate their findings with stakeholders.

This article will take you through the skills every data scientist should have—and some classes you can take to build them.

Build job-relevant data skills today

Looking to work toward a career in data? Build job-relevant data skills in these Professional Certificates from top industry leaders right here on Coursera:

In IBM's Data Science Professional Certificate, you'll prepare for a career as a data scientist as you master key data concepts, practices, tools, and programming languages used by data professionals like Python and SQL.

In Google's Advanced Data Analytics Professional Certificate, you'll learn in-demand skills like statistical analysis, Python, regression models, and machine learning in less than six months.

7 essential skills for a data scientist

As you embark on your career as a data scientist, these are skills you’ll definitely need to master.

1. Programming

Programming languages, such as Python or R, are necessary for data scientists to sort, analyze, and manage large amounts of data (commonly referred to as “big data”). As a data scientist just starting out, you should know the basic concepts of data science and begin familiarizing yourself with how to use Python. Popular programming languages include:

• Python

• R

• SAS

• SQL

Read more: What Is Python Used For? A Beginner’s Guide

Where to start: the University of Michigan's Python for Everybody Specialization teaches how to program and analyze data with Python.

2. Statistics and probability

In order to write high-quality machine learning models and algorithms, data scientists need to learn statistics and probability. For machine learning, it is essential to use statistical analysis concepts like linear regression. Data scientists need to be able to collect, interpret, organize, and present data, and to fully comprehend concepts like mean, median, mode, variance, and standard deviation. Here are different types of statistical techniques you should know:

• Probability distributions

• Over and undersampling

• Bayesian and frequentist statistics

• Dimension reduction

Where to start: Stanford University's Introduction to Statistics course covers statistical concepts that are essential for learning from data and communicating insights.

3. Data wrangling and database management

Data wrangling is the process of cleaning and organizing complex data sets to make them easier to access and analyze. Manipulating the data to categorize it by patterns and trends, and to correct any input data values can be time-consuming but necessary to make data-driven decisions. This is also related to understanding database management—you’re expected to extract data from different sources and transform it into a suitable format for query and analysis, and then load it into a data warehouse system. Useful tools for data wrangling include:

• Altair

• Talend

• Alteryx

• Trifacta

• Tamr

And database management tools include:

• MySQL

• MongoDB

• Oracle

Where to start: the University of Colorado Boulder's Data Warehousing for Business Intelligence Specialization teaches how to build a fully-optimized business data warehouse over five courses.

Read more: What Is Data Wrangling and Why Does It Matter?

4. Machine learning and deep learning

As a data scientist, you’ll want to immerse yourself in machine learning and deep learning. Incorporating these techniques helps you improve as a data scientist because you’ll be able to gather and synthesize data more efficiently, while also predicting the outcomes of future data sets. For example, you can forecast how many clients your company will have based on the previous month’s data using linear regression. Later on, you can boost your knowledge to include more sophisticated models like Random Forest. Some machine learning algorithms to know include:

• Linear regression

• Logistic regression

• Naive Bayes

• Decision tree

• Random forest algorithm

• K-nearest neighbor (KNN)

• K means algorithm

Where to start: in Stanford and DeepLearning.AI's Machine Learning Specialization, you'll master fundamental AI concepts and develop practical machine learning skills over three beginner-friendly courses taught by AI visionary Andrew Ng.

Read more: Is Machine Learning Hard? A Guide to Getting Started

5. Data visualization

Not only do you need to know how to analyze, organize, and categorize data, but you’ll also want to build your skills in data visualization. Being able to create charts and graphs is important to being a data scientist. With strong visualization skills, you can present your work to stakeholders so that the data tells a compelling story of the business insights. Familiarity with the following tools should prepare you well:

• Tableau

• Microsoft Excel

• PowerBI                         

Where to start: Tableau's Data Visualization with Tableau course offers insight into key data visualization concepts, methods, and tools used today.

Read more: 5 Data Visualization Jobs (+ Ways to Build Your Skills Now)

6. Cloud computing

As a data scientist, you'll most likely need to use cloud computing tools that help you analyze and visualize data that are stored in cloud platforms. Some certifications will specifically focus on cloud services such as:

• Amazon Web Service (AWS)

• Microsoft Azure

• Google Cloud

These tools provide data professionals access to cloud-based databases and frameworks that are key for advancing technology. They are used in many industries now, so it is important in data science to become familiar with the concepts behind cloud computing.

Where to start: Amazon Web Service's AWS Fundamentals Specialization offers an overview of the features, benefits, and capabilities of AWS.

7. Interpersonal skills

You’ll want to develop workplace skills such as communication in order to form strong working relationships with your team members and be able to present your findings to stakeholders. Just as data visualization is important for communicating the data insights you uncover as a data scientist, so is being able to collaborate with teams successfully. Here are interpersonal skills you can build upon:

• Active listening

• Effective communication skills

• Sharing feedback

• Attention to detail

• Leadership

• Empathy

• Public speaking

Where to start: the University of Pennsylvania's Improving Communication Skills course covers how to communicate more effectively at work to achieve your goals.

Read more: Why Is Workplace Communication Important? And How to Improve It

How to develop your data science skills

Whether you are a data science novice or a seasoned data scientist, here are some ways you can brush up on your skills.

1. Sign up for a course, certificate, or bootcamp.

Once you've decided to build your skills in programming, database management, or cloud computing, you may want to enroll in an online course or certificate program.

Another option is a data science bootcamp, which can be done either in person or online. These are intensive, often full-time and immersive, so you can learn quickly and efficiently over a few weeks or months. While this is a great way to advance your career or switch careers, it can be a privilege to be able to take time off work to do so.

The IBM Data Science Professional Certificate gave me a lot of confidence. I never saw myself as a computer person, but the program has you do all these complicated-seeming things like working in the Cloud and connecting to APIs, and it was so cool to me, to see how easy Watson Studio actually was to use, and how much you could do on it.

— Sam B.

Read more: How to Choose a Data Science Bootcamp (+ 5 to Consider)

2. Immerse yourself in media.

There's plenty of media out there that can help you learn the terminology and become familiar with trends in data science, such as:

• Blogs

• Books

• Podcasts

• YouTube videos

Read more: 17 Data Science Podcasts to Listen to in 2023

3. Get involved in the community.

Learning from others in the industry can help you gain a network of individuals who could become your future peers or mentors. These are some ways to get involved:

• Network: Find data science communities near you and attend networking events, panels, and happy hours. In a post-COVID era, some of these events are virtual so you are not limited to your town but can seek out online communities for such events on Slack, MeetUp, Discord, Facebook, and more.

• Attend a conference: These days, there are data science conferences for nearly any niche, so you can listen to talks and meet new people in the data science field.

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