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.