ECE4710J Introduction to Data Science

Number of Credits

4

Teaching Hours

64

Offering School

UM-SJTU Joint Institute

Course Teacher

Ailin Zhang

Course Level

Undergraduate Level

Language of Instruction

English

First Day of Class

(TBD) May, 2024

Last Day of Class

(TBD) August, 2024

Course Component

Lecture

Mode of Teaching

Synchronous

Meeting Time

(TBD)

Every Monday ,Wednesday and Friday at 10:00 - 11:40 AM

Click here to view World Clock Meeting Planner

Time Zone

Beijing Time（UTC+8）

Course-specific Restrictions (e.g. Prerequisites / Major / Year of Study)

For senior undergraduates and graduates.

• Foundations of Math and Statistics

Linear algebra, probability and statistics are essential. We will need some basic concepts like linear operators, eigenvectors, derivatives, and integrals to enable statistical inference and derive algorithms.

• Computing

We will use python as the computing language for teaching and homework. You need to be familiar with python programming (e.g., for loops, lambdas, debugging, and complexity) You can use the following tutorial to pick up your python skill.

General Python: https://docs.python.org/3.9/tutorial/index.html

Numpy and Pandas: https://cs231n.github.io/python-numpy-tutorial/

Course Description

The course will cover concepts and skills to tackle real-world data science problems. We will follow the data science life cycle to discuss data collection, data cleaning, data visualization, modeling, and data informed decision making. We will introduce concepts in probability, statistical inference, and machine learning. By working on real datasets, you will develop skills in programing and scientific computation (R and Python). In the course, you will learn how to build data-driven models from scratch to inform decision making.

Assessment Format

1. Homework (5-7 submissions): 30%
2. Project: 20%
3. Midterm: 20%
4. Final: 30%
* Extra Credit: 3%

Syllabus

English