HAD7001H-F2

Prerequisites

None

Course Description

Introduction to Data Science offers a comprehensive foundation in the principles, tools, and techniques of modern data analysis. Designed for beginners, the course guides students through the full data science pipeline—from ethical data sourcing and preparation to modeling and interpretation—using Python and its powerful data science libraries. Students will learn to retrieve and clean real-world datasets, perform exploratory data analysis, and apply machine learning methods including regression, classification, and clustering. Emphasis is placed on responsible, explainable, and locally relevant practices. The course also introduces natural language processing through topic modeling and sentiment analysis, equipping students with the practical skills to derive insights from structured and unstructured data. By the end, students will be able to build, evaluate, and communicate data-driven solutions across diverse domains.

Learning Outcomes

By the end of this course, students will be able to:

1. Explain and contextualize the role of data science, its ethical foundations, and its relevance across disciplines and real-world applications.
2. Demonstrate proficiency in using Python and associated libraries to acquire, clean, transform, and prepare diverse datasets for analysis.
3. Apply exploratory data analysis (EDA) to uncover patterns, assess data quality, and generate insights through visualization and statistical reasoning.
4. Develop and evaluate predictive models across regression, classification, and ensemble methods, applying best practices for feature engineering, hyperparameter tuning, and explainable AI.
5. Implement unsupervised learning approaches such as clustering and dimensionality reduction to discover structure in complex datasets.
6. Analyze textual data using natural language processing techniques, including topic modeling and sentiment analysis, to extract and interpret meaning from unstructured sources.
7. Integrate ethical, legal, and responsible practices in data handling, model development, and communication of results, with particular attention to fairness, accountability, and local relevance.
8. Communicate findings effectively to diverse audiences, using clear narratives, visualizations, and evidence-based interpretations that support decision-making.

Instructor

Contact Information

Office: 662
Email: Jude.kong@utoronto.ca
Office Hours: By Appointment
Course Time: Thursdays, 9am – noon
Course Type: In-person Lectures and hands-on labs

Course Evaluation

Item	Description	Weight
Participation	Active engagement in class discussions and preparation for sessions	10%
Assignment #1	In Assignment 1, students will be required to identify key variables from academic literature and credible media sources, locate corresponding public datasets, and construct well-documented datasets. 4	15%
Assignment #2	In Assignment 2, students will conduct Exploratory Data Analysis (EDA) on the dataset they developed in Assignment 1.	15%
Assignment #3	In Assignment 3, students will perform data modeling and evaluation using machine learning on the dataset they developed in Assignment 1.	15%
Assignment #4	In Assignment 4, students will perform and evaluate clustering analysis (unsupervised learning) on the dataset they developed in Assignment 1.	15%
Final Project	Final Project: For the Final Project, students will integrate the work from previous assignments—including the dataset from Assignment 1, Exploratory Data Analysis (EDA) from Assignment 2, data modeling and evaluation using machine learning from Assignment 3, and clustering analysis from Assignment 4—to produce a publication-ready manuscript.	30% (10% Presentation +20% Report)