# DATA 301 Fall 2019 Schedule

# Table of contents

# Schedule

Below is our tentative schedule. It is subject to change, but those changes will be reflected here.

## Week 0 (Thursday 9/19)

Go over Syllabus

Icebreaker

Labs:

- General technical mayhem that comes with the first day

## Week 1 (9/24 and 9/26)

### Tuesday

Lecture Topics

- Introduction to data science
- Tabular data
- Summarizing data
- Visualizing variables
- Transforming variables

Lab 1:

- Chapter 1.1 and Exercises
- Chapter 1.2 and Exercises
- Chapter 1.3 and Exercises
- Chapter 1.4 and Exercises

### Thursday

Lecture Topics

- Filtering data
- Split-apply-combine

Lab 2:

- Chapter 2.1 and Exercises
- Chapter 2.2 and Exercises
- Lab Distribution of First Digits
- Lab Shark Tank

## Week 2 (10/1 and 10/3)

### Tuesday

Lecture Topics

- Office hour updated
- Questions/issues with lab?
- Tech Example
- OLAP
- Data cubes and pivot tables

Lab 3:

- Chapter 2.3 - We did this in class
- Chapter 2.4 and Exercises
- Lab Evidence of Discrimination

### Thursday

Before class:

Lecture Topics

- Relationships between categorical variables
- Independence
- Bayesian Classification

Lab 4:

- Chapter 3.1 and Exercises
- Chapter 3.2 and Exercises
- Bayesian Classification Worksheet

## Week 3 (10/8 and 10/10)

### Tuesday

Before class:

Lecture Topics

- Relationships between quantitative variables
- Beyond two variables

Lab 5:

- Chapter 3.3 and Exercises
- Chapter 3.4 and Exercises

### Thursday

Lecture Topics

Lab 6:

- PCA Worksheet
- Chapter 3.5 and Exercises

## Week 4 (10/15 and 10/17)

### Tuesday

Lecture Topics

- Distance metrics
- Distances between categorical variables
- Distance Matrix

Lab 7:

- Review
- ATTENTION: Exercises are due Tuesday of the following week and not the normal Thursday
- Chapter 4.1 and Exercises
- Chapter 4.2 and Exercises
- Chapter 4.3 and Exercises

### Thursday

Lecture Topics:

- Exam 1 - Part 1

Labs:

- Exam 1 - Part 2

## Week 5 (10/22 and 10/24)

### Tuesday

Lecture Topics

Lab 8 (Due the following Tuesday):

- Chapter 5.0 and Exercises

### Thursday

Lecture Topics

- Machine learning and regression

Lab 9:

- Chapter 5.1-5.4 and Exercises

## Week 6 (10/29 and 10/31)

### Tuesday

Lecture Topics

- Classification models and evaluation metrics

Lab 10:

- Chapter 5.5 and Exercises
- Chapter 6.1 and Exercises
- Chapter 6.2 and Exercises

### Thursday

Lecture Topics

- Textual Data
- SpaCy Example

Lab 11:

- Chapter 10.1 and Exercises
- Chapter 10.2 and Exercises

## Week 7 (11/5 and 11/7)

### Tuesday

Lecture Topics

- Hierarchical Data (JSON and RESTful APIs)

Lab 12:

- Chapter 11.1 and Exercises
- Chapter 11.2 and Exercises
- Chapter 11.3 and Exercises

### Thursday

Lecture Topics

- Hierarchical Data (XML and Web Scraping)

Labs:

- Project Time
- Team determination (Result: GitHub repo for each team joined)
- Project option selection
- Discussion of overall project

## Week 8 (11/12 and 11/14)

### Tuesday

Lecture Topics

- Review

Labs:

- Project Time

### Thursday

Lecture Topics

- Exam 2

Labs:

- Project Time

## Week 9 (11/19 and 11/21)

### Tuesday

DUE: Exploratory Data Analysis

Lecture Topics

- Unsupervised Learning and k-means
- Section 7.1 in the book

Labs:

- Project Time

### Thursday

DUE (now due Friday of this week):

- Preliminary results on main objective
- Identification of a second objective

Lecture Topics

- Hierarchical Clustering (Section 7.2 in the book)
- Data Science Dev Ops (Training)
- Data Science Dev Ops (Deployment)
- Example of platform

Labs:

- Project Time

Project Reflection

## Thanksgiving

## Week 10 (12/3 and 12/5)

### Tuesday

DUE: Preliminary results on second objective

Lecture Topics

- Data Science Dev Ops (Distributed computing)
- Project Sample Discussion

Labs:

- Project Time

### Thursday

Lecture Topics

- Deployment and serving models

Labs:

- Project Time

## Other

Last day of classes December 6th (also my birthday so you know it’s a good day)

Final exam period December 9-13

Final exam time period is Dec 10 from 10:10am – 1:00pm. This time will be used for exam 3.

Final project is due in full on Dec 10.