# Data Science 3 with Python (303-3-22)

## Instructors

Lizhen Shi

## Meeting Info

University Hall 122: Tues, Thurs 5:00PM - 6:20PM

## Overview of class

Only Statistics majors, Data Science minors, and Statistics Masters students assigned to take 303-3 in this quarter are able to register for this course.

The course introduces non-linear statistical models such as splines, support vector machines, and tree-based classification methods such as random forests, and boosting.

## Registration Requirements

STAT 303-2 or consent of the instructor

## Learning Objectives

1) Translate a problem described in layman terms to a statistical modeling problem.

2) Identify the appropriate statistical modeling method for a given problem.

3) Developing and tuning model parameters of the statistical model.

4) Integrate statistical modeling as a component of the larger data science project.

5) Demonstrate proficiency with coding in the Python programming language, in the context of statistical modeling.

6) Collaborate in a team to develop a complete statistical modeling-based data science solution that answers a question of interest.

## Teaching Method

Most of the lecture will be focused on explaining the course material, where conceptual content will be explained with power point presentations, and application of the concepts in solving real data science problems will be demostrated with code on Jupyter notebook. If time permits, there will be a lab session after the lecture, where students may ask questions on assignments to the instructor and the TAs.

## Evaluation Method

Evaluation will consist of weekly or bi-weekly assignments, a mid-term exam, a final exam, prediction problems, and a course project.

## Class Materials (Required)

A laptop that is able to run Anaconda Navigator for Python programming

An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani, Second edition, with Python codes https://github.com/JWarmenhoven/ISLR-python, ISBN-13: 978-1461471370 (free e-book)

## Class Materials (Suggested)

The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Second edition, ISBN-13: 978-0387848570 (free e-book)

## Class Attributes

Formal Studies Distro Area

## Enrollment Requirements

Enrollment Requirements: Prerequisite: STAT 303-2 or consent of the instructor.

Add Consent: Department Consent Required