7 Skills Every Data Scientist Should Have

Key takeaways

Data scientist skills span programming, statistics, machine learning, and communication.

• Data scientists rely on programming languages such as Python, R, SAS, and SQL to sort, analyze, and manage massive datasets.

• Interpersonal skills, such as communication and active listening, are equally important for working with analysts and presenting insights to stakeholders.

• You can build data science skills through online courses, certificate and certification programs, and community involvement

Discover skills every data scientist should have and some classes you can take to build them. If you're ready to start building data science skills, consider enrolling in the IBM Data Science Professional Certificate. You'll explore tools, languages, and libraries that data scientists use while working with data sets. In as little as four months, you can earn a shareable certificate.

What skills are needed to be a data scientist?

Data scientists use their skills to determine which questions teams should be asking and help answer those questions by creating algorithms and data models to forecast outcomes. The insights they uncover using those skills inform business decisions and 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, you'll also need strong interpersonal skills, since data scientists work collaboratively with business analysts and data analysts to conduct analysis and communicate their findings with stakeholders.

7 essential data scientist skills

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

Where to start: The University of Michigan's Python for Everybody Specialization explores 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: In the IBM Data Management Professional Certificate, you can build the foundational knowledge and skills needed for a career in data management.

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 explore fundamental AI concepts and work with practical machine learning skills over three beginner-friendly courses taught by AI visionary Andrew Ng.

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

Where to start: Amazon Web Services' 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.

How to develop your data science skill set

Whether you're just entering the field or are 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 boot camp, 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.

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

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. Today, 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.

Have career questions? We have answers.

Subscribe to Coursera Career Chat on LinkedIn to receive our weekly, bite-sized newsletter for more work insights, tips, and updates from our in-house team.

Explore our free data science resources

If you’re interested in expert guidance and no-fluff tips to help you build your data science skills, subscribe to our YouTube channel. You can also explore free career resources to learn new topics, explore emerging industries, and discover new opportunities:

• Plan your learning path: Data Science Learning Roadmap: Beginner to Expert

• Hear from a learner: Meet the Statistics Teacher Who's Going Back to School for Data Science

• Bookmark for later: Data Science Terminology and Definitions

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial