Applied computational statistics for analytics

Homework schedule

Homework assignments will be posted at least one week before the due date. It is your responsibility to check the course assignments by logging into the course Google Classroom page.

You are expected to start working on the homework sets early (not the day they are due or right before). It is extremely difficult to answer last-minute homework questions; particularly if you have not been participating in the Campuswire discussion beforehand.

Lecture schedule

The following schedule is tentative; it will be updated each week in case we change pace during any topics. You are expected to cover (at least at a high level) the assigned readings before coming to the lecture. This will help you follow the course and organize your notes.

Homework problem sets are naturally related to the material covered in the course; hence, homework numbers are listed next to the corresponding topic.

Lecture topics

Topic [expected number of lecture hours spent]
Note: each module includes corresponding R and Python problems/labs

0. Framework for viewing data [3]
0.1. Introduction to concepts of a sample and a population
0.2. Interpretation of randomness and random variables
0.3. Overview of R and Python

1. Descriptive statistics [6]
1.1. Quantifying population attributes (discrete and continuous variables)
1.2. Examples of statistics and sampling distributions

2. Experiments, data, and inference [10]
2.1. What is inference? How does it relate to analytics?
2.2. Confidence intervals
2.3. Overview of hypothesis testing
2.4. One- and two-sample location problems
2.5. Analysis of variance [may be covered after topic 4]

3. Statistical learning [6]
3.1. What is statistical learning?
3.2. Estimation: how and why; tradeoff between accuracy and interpretability
3.3. Supervised vs. unsupervised learning
3.4. Assessing model accuracy
3.5. Resampling methods: cross-validation [may be covered after topic 4]

4. Regression [10]
4.1. Overview, simple and multiple linear regression
4.2. Estimating coefficients and assessing accuracy
4.3. Model diagnostics
4.4. Prediction, model extensions, and potential problems
4.5. Polynomial regression

Exams and overflow [3]

... "and how do I figure out when to do what??"