Data Science for Business
Move beyond the spreadsheet
Designed for managers, this Harvard Online course provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information.
4-5 hours per week
4-5 hours per week
What You'll Learn
There is more data available to businesses and organizations today than ever before, offering the potential to quickly and efficiently identify and reach desired business goals and outcomes. What is the best way to access and use this data to help develop business solutions and make decisions? What is data-driven decision making, and how can a better understanding and use of data impact your business or organization?
Data is only as useful as the insights you can collect from it. As a business professional, you can help realize the full potential of your data by building the skills that will help you effectively understand, visualize, and analyze the data available to you.
Data Science for Business will help you appreciate the full benefits of data-driven decision making and teach you the business analytics tools and techniques you need to effectively build better business solutions and become a stronger manager.
Through real-life case studies and hands-on exercises, you will understand how experts from across industries used data to answer some of their biggest business questions, and:
- Build a framework that will support data-driven decision making in your organization.
- Identify trends in data that lead to hypotheses and insights.
- Catch data mistakes or missing components.
- Communicate and work with data analysts and scientists to better lead your team to long-term success.
By the end of this course, you will be prepared to harness data to help you reach your business goals.
Start making decisions with data in Data Science for Business.
The course is part of the Harvard on Digital Learning Path and will be delivered via HBS Online’s course platform. Learners will be immersed in real-world examples from experts at industry-leading organizations. By the end of the course, participants will be able to:
- Break away from the spreadsheet by developing a foundational understanding of data science tools, processes, and models.
- Use business analytics and data science to make better decisions that lead to organizational success.
- Identify and avoid common mistakes while interpreting datasets, metrics, and visualizations.
- Create a data-driven framework for your organization and yourself; develop hypotheses and insights; and identify data and missing components.
- Speak a common language with data scientist teams to uncover actionable recommendations and findings.
- Put key techniques into practice such as data curation, regression models, prediction and analyses, and visualization.
- Read basic code in order to comprehend the syntax that informs data requests.
- Assess applicable methodologies in statistics, data analytics, and data science by hearing from real-world examples across industries, topics, and business challenges.
Your Instructor
Yael Grushka-Cockayne is the Altec Styslinger Foundation Bicentennial Chair in Business Administration and Senior Associate Dean for Professional Degree Programs at the University of Virginia Darden School of Business and was formerly a Visiting Professor of Business Administration at Harvard Business School and Professor of Business Administration. Her research and teaching activities focus on data science, forecasting, project management, and behavioral decision-making. Her research is published in numerous academic and professional journals, and she is a regular speaker at international conferences in the areas of decision analysis, project management, and management science. In 2014, Grushka-Cockayne was named one of "21 Thought-Leader Professors" in Data Science.
Real World Case Studies
Affiliations are listed for identification purposes only.
Temple Fennell
Temple Fennell is the CEO and founder of ATO Pictures, LLC, a motion pictures finance, production and distribution company that provides U.S. distribution and production funding. Step into Hollywood and explore how this company used data to identify box office success in the entertainment industry.
Paul Matherne, MD
Paul Matherne, MD is a pediatric cardiologist and the associate chief medical officer for UVA Children's. He will demonstrate how even preemies can benefit from data science in the case of a children’s hospital’s bid to expand the NICU.
Susanna Gallani
Susanna Gallani is an Assistant Professor Of Business Administration at Harvard Business School. She used data to determine if employees were fully engaged at work and will analyze how data can be used to fine-tune incentive strategies.
Who Will Benefit
Aspiring Managers
Get a headstart on your next career by gaining new technical skills and a data-driven mindset.
Managers and Rising Leaders
Dig into your organization’s datasets to create compelling business plans that increase revenue while mitigating risk.
Product Managers
Develop market-driven products by using data to identify and predict market size, competition, and buyer trends.
Learner Experience
Syllabus
Data Science for Business moves beyond the spreadsheet and provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information. Starting with the questions you need to ask when using data for decision-making, this course will help you know when to trust your data and how to interpret the results.
Learning requirements: There are no prerequisites required to enroll in this course. In order to earn a Certificate of Completion from Harvard Online and Harvard Business School Online, participants must thoughtfully complete all 5 modules, including satisfactory completion of the associated assignments, by stated deadlines.
- Study good data and bad buys in a case study about Carvana
- Translate business problems into data hypotheses
- Explore and describe datasets
- Use visualizations to generate hypotheses
- Relate the quality of data with the the quality of the conclusions by studying the Fannie Mae case on investment identification
- Prepare and clean data for analysis
- Examine data dictionaries
- Design table joins
- Identify solutions for managing missing data
- Critique existing charts and identify methods of improvement through the exploration of the StockX case on demand.
- Generate insight with graphs
- Design visualizations to express data clearly
- Connect yesterday’s data with tomorrow’s prediction
- Evaluate temporal patterns in data
- Match the time scale with the business problem
- Select appropriate smoothing techniques for time series forecasting
- Study the Bark Gift Shop case on motivating managers and the ATO Pictures case on marketing movies
- Identify relationships between variables
- Write hypotheses
- Explain the parts of a linear model, including interactions and dummy variables
- Interpret linear regression results
- Revisit the Carvana and Fannie Mae cases from earlier modules
- Complete a confusion matrix
- Interpret results from logistic regression, CART, random forest, lasso, and neural networks
- Select a model to guide decisions
