Quantitative Reasoning

This course introduces foundational concepts in discrete mathematics, probability, and predictive modeling, along with computational tools for analyzing data.

Instructor: Dipto Das

Term: Spring

Location: Lucile Berkeley Buchanan Building 155

Time: Tuesdays and Thursdays, 2:00–3:15 PM

Course Overview

This course introduces quantitative reasoning methods for analyzing data. Students will:

  • Learn foundational concepts in logic, sets, and linear algebra
  • Apply probability and statistical reasoning
  • Use combinatorics and distributions to model uncertainty
  • Develop computational approaches to data analysis
  • Build and evaluate predictive models

Prerequisites

  • Introductory programming experience
  • Basic algebra

Textbooks

  • No required textbook
  • Course materials provided throughout the semester

Grading

  • Attendance: 10%
  • Quizzes: 10%
  • Homework: 20%
  • Exam 1: 20%
  • Exam 2: 20%
  • Final Exam: 20%

Schedule

Week Date Topic Materials
1 Course Overview and Motivation

Introduction to quantitative reasoning and course tools.
2 Logic and Reasoning

Foundations of logical thinking and computational reasoning.
3 Sets and Functions

Set theory and functions for structuring data.
4 Linear Algebra Foundations

Scalars, vectors, and matrices.
5 Exam 1

Covers fundamentals of logic and mathematical structures.
6 Statistics and Probability

Introduction to probability distributions and statistics.
7 Conditional Probability

Reasoning about dependent events.
8 Expected Value

Understanding expectation and decision-making.
9 Bayes’ Rule

Bayesian reasoning and updating beliefs.
10 Exam 2

Covers probability and statistical reasoning.
11 Combinatorics

Permutations and combinations.
12 Binomial Distribution

Modeling discrete outcomes.
13 Normal Distribution

Continuous distributions and inference.
14 Regression and Correlation

Modeling relationships between variables.
15 Final Exam

Comprehensive final exam.