DSC 140: Fundamentals of Data Science

Course Materials

• Syllabus
• Introduction Survey
• Anonymous Feedback
• Course-Level Learning Objectives
• Course Schedules
  • MWF Section
  • TR Section
• Python Installation Instruction

Introduction to the Course

• Learning Objectives

Introduction to Data Science (MWF: August 26; TR: August 27)

• Slides
• Lecture Notes

Overview of Software (MWF: August 28; TR: August 27 - 29)

• Slides
• Lecture Notes

Homework

• Introduction and Excel (Due September 16th by 11:59 pm)
  • A set of example solutions can be found here. Note that these are only example solutions and not the only correct answers.

Excel

• Learning Objectives ### Introduction to Excel
• MWF: August 30; TR: August 29
• Slides
• Lecture Notes
• In-Class Excel Project
  • Excel Workbook Solutions

Statistics in Excel

• MWF: September 4; TR: September 3
• Slides
  • Data Set 1 (pizza dataset.xlsx)
    • Data Set 1 Solutions
  • Data Set 2 (Fires and Thefts.xlsx)
    • Data Set 2 Solutions
  • Data Set 3 (factbook.csv)
    • Data Set 3 Solutions
• Lecture Notes

Excel Cleaning and Pivot Tables

• MWF: September 6, TR: September 3 - September 5
• Slides
  • Data Set 1 (sales data.xlsx)
    • Data Set 1 Solutions
  • Data Set 2 (Book Data.xlsx)
    • Data Set 2 Solutions
• Lecture Notes

Projects (MWF: September 9 - September 11; TR: September 10)

• Excel Projects

Homework

• Introduction and Excel (Due September 16th by 11:59 pm)
  • A set of example solutions can be found here. Note that these are only example solutions and not the only correct answers.

Python

• Learning Objectives
• Creating a GitHub
• Coding Guidelines

Introduction to Python (

• MWF: September 13 - September 16; TR: September 12
• Notebooks
  • Python Basics (PDF)
    • This is a PDF version for quick viewing.
  • Python Basics (Notebook)
    • This is a Jupyter notebook version that will let you run the Python code.
  • MWF In-Class Notebook (Jupyter)
  • TR In-Class Notebook (Colab)
• Lecture Notes

Reading Data in Python

• MWF: September 18; TR: September 17
• Notebooks
  • MWF Section
  • TR Section
• Lecture Notes

Loops in Python

• MWF: September 20; TR: September 17 - September 19
• Notebooks
  • MWF Section
  • TR Section

Statistics in Python and Other Python Libraries

• MWF: September 23 - September 25; TR: September 24
• Notebook
  • MWF Section
  • TR Section
• Data Set
  • Example Voter Data

Data Cleaning in Python

• MWF: September 30 - October 4; TR: October 1 - October 3
• MWF Section
• TR Section

Machine Learning in Python

• MWF: October 21 - October 25; TR: October 22 - October 24
• Introduction to Machine Learning
• Classification with Machine Learning
  • MWF Section
  • TR Section
  • Wine Data
  • Titanic Training Set
  • Titanic Test Set
• Machine Learning Examples
  • Instructions
  • Seeds Data
  • Traveler’s Reviews Data
  • Housing Data
  • Credit Card Data
  • Voter Data

Python Projects

• Python Projects Part 1 (MWF: September 25 - September 27; TR: September 26)
• Python Projects Part 2 (MWF: October 7 - October 11; TR: October 8 - October 10)

Homework

• Python Homework 1 (Due September 30th by 11:59 pm)
  • Sample Solutions. Note that there is more than one correct way to solve most of the problems.
• Python Homework 2 (Due October 21st by 11:59 pm)
  • Sample Solutions. Note that there is more than one correct way to solve most of the problems.
• Python Homework 3 (Due October 30th by 11:59 pm)
  • Sample Solutions. Note that there is more than one correct way to solve most of the problems.

R

• Learning Objectives

Introduction to R

• MWF: October 28 - November 1; TR: October 29 - October 31
• Lecture Notes as R File
• Data File: IQ Size

Data Visualization and Linear Regression in R

• MWF: October 28 - November 1; TR: October 29 - October 31
• Lecture Notes as R File
• Data File: IQ Size
• Data File: Cancer Probability
• Data File: Rainfall

Machine Learning in R

• MWF: November 4 - November 6; TR: November 5 - 7
• Lecture Notes
• Data File: Wine
• Data File: Iris
• Data File: Beaver

Projects

• R Projects (MWF: November 6 - November 8; TR: November 7)

Homework

• R Homework (Due November 13th by 11:59 pm)
  • Example Solutions

MySQL

• Learning Objectives

Introduction to MySQL

• MWF: November 11 - November 13; TR: November 12
• Slides
• Connection Code

MySQL and Python

• MWF: November 13 - November 15; TR: November 14
• Lecture Notes (Google Colab Link)

Projects

• MySQL Projects (MWF: November 18 - November 22; TR: November 19 - November 21)

Homework

• MySQL Homework (Due November 25th by 11:59 pm)
  • Sample Solutions. Note that these are example solutions, other solutions can still recieve full credit.

Additional Topics

• Learning Objectives
• Gathering Data from the Internet (MWF: November 20-25; TR: November 19-26)
  • Slides
  • Lecture Notes

Projects

• API Projects (MWF: November 22 - November 25, TR: November 21 - November 26)
  • Housing Vacancies Data Explanation

Midterm Exam

• MWF: October 16; TR: October 15
• List of Topics on the Midterm
• Practice Midterm #1
  • Excel Solutions
  • Python Example Solutions
• Practice Midterm #2
  • Excel Solutions
  • Python Solutions
• Notebook from Midterm Exam Review

Final Exam

• MWF: December 9th at 1:00 pm; TR: December 11th at 8:00 am
• List of Topics
• Sample Final Exam (Fall 2023)
  • R Example Solutions
  • MySQL Example Solutions

Ethics Report

• Due October 7th by 11:59 pm More details can be found here.

Project Report #1

• Due November 1st by 11:59 pm More details can be found here.

Project Report ##2

• Due November 26th by 11:59 pm More details can be found here.

Final Project

• Due dates from December 1st at 11:59 pm to December 6th at 11:59pm More details can be found here.
  
• Editing Groups and Presentation