# Module 05: Statistical Inference

1. Prepare (due M 2/7)
1. Content below
2. Sakai quizzes
2. Peer Instructions – See on the class forum
3. Homework (due Su 2/13)
4. Worked Example

## Content

5.A – Confidence Intervals and Bootstrapping

1. Intro Confidence Intervals (17 min.)
2. Confidence Intervals in Python (17 min.)

5.B – Hypothesis Testing

1. Intro Hypothesis Testing and Proportions (14 min.)
2. Hypothesis Testing Means and More (33 min.)

## Optional Supplements

You can access an excellent free online textbook on OpenIntro Statistics here, co-authored by Duke faculty. You can pay a suggested but adjustable price for a tablet-friendly pdf, but you can also just get the regular pdf for free. For Module 3B, the following optional readings may be particularly helpful supplements:

• Chapter 5.2 Confidence intervals for a proportion. This provides introductory material on confidence intervals elaborating on 3B.A.1.
• Chapter 5.3 Hypothesis testing for a proportion. This elaborates on the introduction to hypothesis testing from 3B.B.1.
• Chapters 7.1, 7.3, and 7.5 cover material from 3B.B.2 on using t-tests for a single mean, the difference of two means, and many pairwise means respectively.
• Chapter 6.3 discusses the chi-square test for categorical data introduced in 3B.B.2.

In addition, here is the documentation for the scipy.stats library that implements most of the functionality described here as well as many other useful statistical functions.

# Module 04: Data Wrangling

1. Prepare (due M 1/31)
1. Content below
2. Sakai quizzes
2. Peer Instructions – See on the class forum
3. Homework (due Su 2/6)
4. Worked Example

## Content (Slides in the Box folder)

4.A – What is Wrangling

1. Data sources, formats, and importing (26 min.)
2. Common data cleaning problems (16 min.)
3. Read Section 3.4 Handling Missing Data from Python Data Science Handbook

4.B – Wrangling Text

1. Python string operations (16 min.)
2. Introduction to regular expressions (18 min.)
3. Read Section 3.10 Vectorized String Operations from Python Data Science Handbook

# Module 03: Probability

1. Prepare (due M 1/24)
1. Content below
2. Sakai quizzes
2. Peer Instructions – See on the class forum
3. Homework (due Su 1/30)
4. Worked Examples

## Content (Slides in the Box folder)

3.A – Foundations of Probability (52 min.)

1. Outcomes, Events, Probabilities (15 min.)
2. Joint and Conditional Probability (11 min.)
3. Marginalization and Bayes’ Theorem (15 min.)
4. Random Variables and Expectations (11 min.)

3.B – Distributions of Random Variables (46 min.)

1. Distributions, Means, Variance (19 min.)
2. Monte Carlo Simulation (15 min.)
3. Central Limit Theorem (12 min.)
1. Slide 26 in the video has a typo that is fixed in the pdf version of the slides on Box. In the video, it says the probability is <= 0.95, but it should say < 0.05.

## Optional Supplements

You can access an excellent free online textbook on OpenIntro Statistics here, co-authored by Duke faculty. You can pay a suggested but adjustable price for a tablet-friendly pdf, but you can also just get the regular pdf for free. For this module, the following optional readings may be particularly helpful supplements:

• Chapter 3: Probability. This provides more information on many of the topics from the above videos in Foundations of Probability.
• Chapter 4: Distributions of random variables. This provides much more information about particular classic distributions than is provided in 2B.B.1.
• Chapter 5.1: Point estimates and sampling variability. This provides more information on some of the topics from 2B.B.2-3.

In addition, you can find documentation for the two pseudorandom number generating / sampling libraries in python that we mentioned here:

# Module 02: Numpy & Pandas

1. Prepare (due M 1/17)
1. Content below
2. Sakai quizzes
2. Peer Instructions
1. DataFrame Indexing: Round 1, Round 2
2. Series Adding: Round 1, Round 2
3. hstack/vstack: Round 1, Round 2
4. Slicing: Round 1, Round 2
3. Homework (Su 1/23)
4. Worked Example

## Content (Slides in the Box folder)

2.A – Numpy (1 hour)

1. Why Numpy (8 min.)
2. Numpy Array Basics (15 min.)
3. Numpy Universal Functions (20 min.)
4. Numpy Axis (14 min.)

2.B – Pandas (45 min.)

1. Why Pandas (7 min.)
2. Pandas Series (19 min.)
3. Pandas Dataframe (21 min.)

# Module 01: What is Data Science, Anaconda, Python, & Jupyter

1. Prepare (due M 1/10 )
1. Content below
2. Quiz is on Sakai
3. Install Anaconda
2. Peer Instructions (these will open when we use them)
1. lambda with min/max: Round 1, Round 2
2. Sorting: Round 1, Round 2
3. Notebooks I: Round 1, Round 2
4. Notebooks II: Round 1, Round 2
3. Homework (due Su 1/16)

## Content (Slides in the Box folder)

1.A – What is Data Science? (in-class on 1/7 or see recording)

1.B – Python3 (12 min.)

1. Python vs. Java (3 min.)
2. Data Types (2 min.)
3. Iteration, Functions, Classes (7 min.)

1.C – Python for Data Science

1. Anaconda and Jupyter (10 min.)
2. Jupyter Notebook Demo (11 min.)