Data Science Principles
Are you prepared for our data-driven world?
Data Science Principles is a Harvard Online course that gives you an overview of data science with a code- and math-free introduction to prediction, causality, data wrangling, privacy, and ethics.
What You'll Learn
What is data science, and how can it help you make sense of the infinite data, metrics, and tools that are available today?
Data science is at the core of any growing modern business, from health care to government to advertising and more. Insights gathered from data science collection and analysis practices have the potential to increase quality, effectiveness, and efficiency of work output in professional and personal situations.
Data Science Principles makes the foundational topics in data science approachable and relevant by using real-world examples that prompt you to think critically about applying these understandings to your workplace. Get an overview of data science with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, and ethics.
Data Science Principles is an introduction to data science course for anyone who wants to positively impact outcomes and understand insights from their company’s data collection and analysis efforts. This online certificate course will prepare you to speak the language of data science and contribute to data-oriented discussions within your company and daily life. This is a course for beginners and managers to better understand what data science is and how to work with data scientists.
Data Science Principles is part of our Harvard on Digital Learning Path.
The Harvard on Digital course series provides the frameworks and methodologies to turn data into insight, technologies into strategy, and opportunities into value and responsibility to lead with data-driven decision making.
- Understand the modern data science landscape and technical terminology for a data-driven world
- Recognize major concepts and tools in the field of data science and determine where they can be appropriately applied
- Appreciate the importance of curating, organizing, and wrangling data
- Explain uncertainty, causality, and data quality—and the ways they relate to each other
- Predict the consequences of data use and misuse and know when more data may be needed or when to change approaches
Dustin Tingley is a data scientist at Harvard University. He is Professor of Government and Deputy Vice Provost for Advances in Learning and helps to direct Harvard's education focused data science and technology team. Professor Tingley has helped a variety of organizations use the tools of data science and he has helped to develop machine learning algorithms and accompanying software for the social sciences. He has written on a variety of topics using data science techniques, including education, politics, and economics.
Real World Case Studies
Affiliations are listed for identification purposes only.
Listen to Harvard Professor and faculty member at Boston Children’s Hospital analyze Google Flu, its failures, and lessons learned.
Explore the difficulties faced in keeping data anonymous and private with Harvard Professor and Director of the Data Privacy Lab in IQSS at Harvard.
Learn how Burning Glass Technologies uses text analysis to recommend job openings, skill development, and labor market trends.
Explore and connect to our courses via articles, webinars, and more.