📊 Data Science Week 1 of 14 BSc · Y1 S2 ⏱ ~50 min

Week 1: Probability, Distributions & Hypothesis Testing

Build statistical intuition for data analysis: descriptive stats, probability theory, key distributions, and the hypothesis testing workflow.

University of America

STAT101 — Lecture 1 · BSc Y1 S2

🎬 CC Licensed Lecture

0:00 / —:—— 📺 MIT OpenCourseWare (CC BY-NC-SA)

🎯 Learning Objectives

Calculate descriptive statistics and interpret them correctly
Apply Bayes' theorem to real inference problems
Conduct t-tests and chi-square tests
Understand p-values, confidence intervals, and statistical power

Topics Covered This Lecture

Descriptive Statistics

Probability & Bayes

Normal, Binomial, Poisson Distributions

Hypothesis Testing & p-values

📖 Lecture Overview

This first lecture establishes the foundational framework for Statistics Fundamentals. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Build statistical intuition for data analysis: descriptive stats, probability theory, key distributions, and the hypothesis testing workflow. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Descriptive Statistics, Probability & Bayes, Normal, Binomial, Poisson Distributions, Hypothesis Testing & p-values. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for STAT101
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Descriptive Statistics and Probability & Bayes. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

STAT101 Project 1: A/B Test Analysis

Analyze a real A/B test dataset (marketing campaign or UI experiment). Apply t-tests, compute confidence intervals, and write a business recommendation based on statistical evidence.

Cleaned dataset with EDA summary
T-test and effect size calculation
Confidence interval visualization
1-page business recommendation with statistical justification

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

A coin is flipped 100 times and lands heads 61 times. Compute the p-value for the null hypothesis that the coin is fair.

Analysis Short Answer

What is the difference between Type I and Type II errors? Give a real-world example of each.

Applied Code / Proof

Explain what a 95% confidence interval means in plain language.