Practical Data Analysis

Instructor: Alice Paul
Textbook: [1] Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition). Springer New York, NY. ISBN 978-0-387-84858-7. [2] James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer New York, NY. ISBN 978-1-4614-7138-7.

Description: This course was designed for graduate students who seek to analyze data in practical scientific settings and want to gain experience in distilling complex statistical information into formats understandable to colleagues. Topics covered included data collection, exploratory data analysis, missing data, fitting and checking models, simulation, predictive models, and presentation of reproducible results. These were developed through a series of case studies based on different types of data requiring a variety of statistical methods, which were often carried out and reproduced using the R programming environment.

Analyzing data is a core skill for many scientists. In addition to familiarity with probability and statistics, good data analysis requires skill in computing and effective presentation of results and communication with scientific colleagues. The data analyst must be able to translate the scientific question and hypothesis into a testable form, advise about data to be collected and manipulate it into a computable format to carry out appropriate analysis.

Assignments:

• Homework 1
• Homework 2
• Homework 3
• Homework 4

 

Fall ‘22