6 Skills Every Data Scientist Should Have

Written by Coursera • Updated on

The essential skills that you should have if you’re contemplating a career as a data scientist include programming, statistical analysis, and more.

[Featured image] Data scientist displays charts and graphs that visually represent data on a computer screen

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.

6 essential skills for a data scientist

As you embark on your career as a data scientist, these are six 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



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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 under sampling

  • Bayesian (or frequency) statistics

  • Dimension reduction



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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 and 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

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



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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

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



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5. Data visualization

Not only do you need to know how to analyze, organize, and categorize data, 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                         

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



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6. Communication

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. You’ll want to develop soft skills such as communication in order to form strong working relationships with your team members and be able to present your findings to stakeholders. Skills within communication you can build upon:

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



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Written by Coursera • Updated on

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