Course Syllabus

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Please save/download a copy of the Course Syllabus: NEW_QC_ADA_Syllabus_Spring 2025 .docx

The syllabus is an essential tool for learning about the course and instructor, course goals/expectations, required readings and instructional materials, assignment and activity due dates, and other important information. Read it thoroughly. If you have questions regarding the syllabus, please contact your instructor.

Textbook PDF: Grus, J. (2019). Data science from scratch: First principles with Python (2nd ed.). O’Reilly Media

Textbook PDF: Damji, J. S., Wenig, B., Das, T., & Lee, D. (2020). Learning Spark: lightning-fast data analytics (2nd ed.). O’Reilly Media

Link: Learning Spark Ch. 1

 

Class Schedule

Week

Lecture Topics

Reading Assignments

1

Introduction

Data Science from Scratch, Chapter 1

2

Using Python and Jupiter

Data Science from Scratch, Chapter 2 Jupyter Notebook Documentation

3

Visualizing and Describing Data

Data Science from Scratch, Chapter 3

Matplotlib Tutorial

4

Hypothesis and Inference

Data Science from Scratch, Chapters 7

5

Data Acquisition

Data Science from Scratch, Chapter 9

6

Exploring, Cleaning and Manipulating Data

Data Science from Scratch, Chapter 10

7

Machine Learning

Data Science from Scratch, Chapter 11

8

k-th Nearest Neighbor and Naive Bayes

Data Science from Scratch, Chapter 12, 13

9

Linear and Logistic Regression

Data Science from Scratch, Chapter 14, 16

10

Decision Trees and Random Forests

Data Science from Scratch, Chapter 17

11

Introduction to Apache Spark

Learning Spark, Chapter 1

12

Getting Started with Apache Spark

Learning Spark, Chapter 2

13

Structured APIs

Learning Spark, Chapter 3

14

Spark SQL and Dataframes

Learning Spark, Chapter 4

15

Final Project Presentations

 

 

Course Summary:

Course Summary
Date Details Due