Data scientists collect, analyze, and decipher data to identify insights that help organizations improve their decision making. Learn how to get started in this high-paying, in-demand profession* with these entry-level data science resources.
Data scientists use a variety of techniques – from straightforward statistics to cutting-edge machine learning – to answer novel questions and equip organizations with actionable insights that improve their decision-making. As a data scientist, you’ll use your knowledge of data concepts, tools, and structures to pose original research questions, build high-quality data sets, uncover trends and patterns, and communicate your insights to key stakeholders.
Learn more about Coursera and discover additional resources you can use to build foundational data science skills and expand your understanding of this fast-growing, in-demand* field:
Data science is an interdisciplinary field focused on the study of data, particularly the methods of collecting, processing, and analyzing it for insights. An increasingly important component of practically every modern industry, including health care, logistics, and finance, data science is an in-demand field that is projected to grow by 35-percent between 2022 and 2032, according to the U.S. Bureau of Labor Statistics (BLS).*
Data scientists are well compensated for their in-demand skill set. According to the U.S. Bureau of Labor Statistics (BLS), the median annual salary for a data scientist was $103,500 as of May 2022. By comparison, the BLS noted the median annual wage for all workers in the country was just $46,310 during the same period.*
Data scientists must have a strong mix of data-specific technical skills and interpersonal workplace abilities. At a glance, some of these skills include:
Knowledge of programming languages, such as Python, R, and SQL
Strong grasp of statistics and probability
Data wrangling and database management
Machine learning
Facility with data visualization tools, such as Tableau and Power BI
Grasp of cloud computing platforms like Microsoft Azure and Amazon Web Services (AWS)