Kara Karpman is an Adjunct Assistant Professor of Data Science and Statistics at Cornell University, as well as an Assistant Professor of Mathematics at Middlebury College in Middlebury, VT. Her research focuses on statistical modeling techniques for studying biological and financial data. Professor Karpman holds a B.S. in Mathematics from Duke University and an M.S. and Ph.D. in Applied Mathematics from Cornell University.
When you think of what data scientists do on a daily basis, you probably think of working with data. Yet data scientists typically derive insights on behalf of their organization or for a client, so any insights they obtain from their data need to be accessible to a broader audience. Often, that audience is less familiar with — or even uncomfortable with — data analysis or statistics. For this reason, communication is just as important to a data scientist's success as working with data.
In this course, you will explore the process of working with a client to understand their data science needs and provide them with a summary of results. You will examine how to understand their questions and perform exploratory data analysis to begin answering their questions. You will practice using your detailed data analysis to write a report then translate that data analysis for a presentation to a data science client. This course can serve as a template for working with a client from start to finish.
The following courses are required to be completed before taking this course:
- Exploring Data Sets With R
- Summarizing and Visualizing Data
- Measuring Relationships and Uncertainty
- Data Cleaning With the Tidyverse
- Classifying Data With Logistic Regression
Key Course Takeaways
- Discover the process of working with a data science client
- Distill results from a data analysis into a clear, complete, and concise story
- Use results from a data analysis to compile a report and presentation
How It Works
Who Should Enroll
- Current and aspiring data scientists and analysts
- Business decision makers
- Marketing analysts
- Anyone seeking to gain deeper exposure to data science