Link Search Menu Expand Document

Data 8: Foundations of Data Science

InclusionBridge, Bridge to Data Fundamentals (Mercy University) 2024

Lecture Zoom Link

Let us start your Data Science Journey together

Acknowledgements

This course is based on Data 8, titled β€œThe Foundations of Data Science,” a course taught to first-year students at UC Berkeley. All course materials, including the textbook and assignments, are provided free of charge online under a Creative Commons license. The textbook, β€œComputational and Inferential Thinking: The Foundations of Data Science,” is an online resource featuring Jupyter notebooks and publicly accessible data sets used in all the examples. The course materials comprise Embedded Demo, Lab, and Homework Notebooks, as well as references, all sourced from the public Data8 repository. Students are encouraged to visit the official Data8 website for additional resources, including complete lecture videos and PowerPoint presentations.

Announcements

Jan 1 · 0 min read
  • Data Fundamentals starts January 24th! .

Week 1

Lecture
1 Introduction
Embedded Notebook
Reading: 1.1, 1.2, 1.3
Lab Lab 01: Expressions
Lecture
2 Cause and Effect
Embedded Notebook
Reading: 2
Homework Homework 01

Week 2

Lecture
3 Tables
Embedded Notebook
Reading: 3, 4
Lab Lab 02: Table Operations
Lecture
4 Data Types
Embedded Notebook
Reading: 5
Lecture
5 Building Tables
Embedded Notebook
Reading: 6.1, 6.2
Homework Homework 02

Week 3

Lecture
6 Census
Embedded Notebook
Reading: 6.3, 6.4
Lab Lab 03: Data Types, creating and Extending Tables
Lecture
7 Charts
Embedded Notebook
Reading: 7, 7.1
Lecture
8 Histograms
Embedded Notebook
Reading: 7.2, 7.3
Homework Homework 03

Week 4

Lecture
9 Functions
Embedded Notebook
Reading: 8.1
Lab Lab 04: Functions and Visualization
Lecture
10 Groups
Embedded Notebook
Reading: 8.2, 8.3
Lecture
11 Pivots and Joins
Embedded Notebook
Reading: 8.4
Homework Homework 04

Week 5

Lecture
12 Table Examples
Embedded Notebook
Reading: 8.5
Lecture
13 Conditionals and Iteration
Embedded Notebook
Reading: 9.2
Lab Lab 05: Conditional Statements, Iteration, Tables
Lecture
14 Chance
Embedded Notebook
Reading: 9.2, 9.3, 9.4
Homework Homework 05

Week 6

Lecture
15 Sampling
Embedded Notebook
Reading: 9.5, 10
Lecture
16 Models
Embedded Notebook
Reading: 10.2, 10.3, 10.4
Homework Homework 06
Lecture
17 Comparing Distributions
Embedded Notebook
Reading: 11.1, 11.2
Lab Lab 06: Assessing Models

Week 7

Lecture
18 Decisions and Uncertainty
Embedded Notebook
Reading: 11.3, 11.4
Lecture
19 A/B Testing
Embedded Notebook
Reading: 12.1
Lab Lab 07: A/B Testing
Lecture
20 Causality
Embedded Notebook
Reading: 12.2, 12.3
Homework Homework 07

Week 8

Lecture
23 Confidence Intervals
Embedded Notebook
Reading: 13, 13.1, 13.2
Lecture
24 Interpreting Confidence
Embedded Notebook
Reading: 13.3, 13.4
Homework Homework 08
Lecture
25 Center and Spread
Embedded Notebook
Reading: 14, 14.1, 14.2

Week 9

Assessment
Mid-Term Examination
Embedded Notebook

Week 10

Break
Spring Break

Week 11

Lecture
26 The Normal Distribution
Embedded Notebook
Reading: 14.3, 14.4
Lab Lab 08: Sample Mean
Lecture
27 Sample Means
Embedded Notebook
Reading: 14.5
Lecture
28 Designing Experiments
Embedded Notebook
Reading: 14.6
Homework Homework 09

Week 12

Lecture
29 Correlation
Embedded Notebook
Reading: 15, 15.1
Lecture
30 Linear Regression
Embedded Notebook
Reading: 15.2
Lecture
31 Least Squares
Embedded Notebook
Reading: 15.3, 15.4
Homework Homework 10

Week 13

Lecture
32 Residuals
Embedded Notebook
Reading: 15.5, 15.6
Lab Lab 09: Regression
Lecture
33 Regression Inference
Embedded Notebook
Reading: 16
Homework Homework 11

Week 14

Lecture
34 Classification
Embedded Notebook
Reading: 17, 17.1, 17.2, 17.3
Lecture
35 Classifiers
Embedded Notebook
Reading: 17.4
Homework Homework 12

Week 15

Project Work
Capstone Project
Project Notebook

<