/sf-dat-21

Course materials for General Assembly's Data Science course in San Francisco (2/25/16 - 5/3/16)

Primary LanguageJupyter Notebook

SF-DAT-21

Course materials for General Assembly's Data Science course in San Francisco (2/25/16 - 5/3/16)

Exit Ticket

Fill me out at the end of each class!

Schedule

Week Date Class Dues and assigned
0 Pre-Work
Unit 1 - Research Design and Exploratory Data Analysis
1 2/25 What is Data Science Unit Project 1 and Final Project 1 assigned
1 3/1 Research Design and pandas
2 3/3 Statistics Fundamentals Unit Project 1 due; Unit Project 2 assigned
2 3/8 Statistics Fundamentals, Part 2
3 3/10 Flexible Class Session Unit Project 3 assigned
Unit 2 - Foundations of Data Modeling
3 3/15 Introduction to Regression and Model Fit Unit Project 2 due (extended)
4 3/17 Introduction to Regression and Model Fit, Part 2
4 3/22 Introduction to Classification Final Project 1 due; Final Project 2 assigned
5 3/24 Introduction to Logistic Regression Unit Project 4 assigned
5 3/29 Flexible Class Session Unit Project 3 due (extended)
6 3/31 Advanced Metrics and Communicating Results Unit Project 4 due (extended)
Unit 3 - Data Science in the Real World
6 4/5 Decision Trees and Random Forests
7 4/7 Natural Language Processing and Text Classification
7 4/12 Latent Variables and Natural Language Processing Final Project 2 due; Final Projects 3, 4, and 5 assigned
8 4/14 Time Series Data
8 4/19 Time Series Data, Part 2 Final Project 3 due
9 4/21 Introduction to Databases
9 4/26 Wrapping Up and Next Steps Final Project 4 due
10 4/28 Final Project Presentations Final Project 5 due
10 5/3 Final Project Presentations, Part 2

(Syllabus last updated on 4/26/2016)

(Flexible class sessions will be finalized after student goals are defined)

Your Team

Instructor: Ivan Corneillet

Experts-in-Residence: Azi Hussain and Jeremiah Gaw

Course Producer: Vanessa Ohta

Office Hours

  • Jeremiah and Azi: Check their weekly announcements on Slack
  • Ivan on Tuesdays and Thursdays, 5:30PM to 6:30PM at GA (one hour before class)

Slack

You've all been invited to use Slack for chat during class and the day. Please consider this the primary way to contact other students. Jeremiah and Azi will be in Slack during class to handle questions. All instructors will be available on Slack during office hours (listed above).

Graduation Requirements

  1. Missing no more than 2 class sessions over the duration the course
  2. Completing 80% of assigned unit project (4 unit projects)
  3. Completing the final project (one final project subdivided into 5 deliverables)

Unit Projects

Unit Project Description Goal Assigned Due
1 Research Design Write-Up Create a problem statement, analysis plan, and data dictionary in iPython 2/25 3/3 6:30PM Pacific
2 Exploratory Data Analysis Explore data with visualizations and statistical analysis in an iPython notebook 3/3 3/10 3/15 6:30PM Pacific (extended)
3 Basic Modeling Assignment Perform logistic regressions, creating dummy variables, and calculating probabilities 3/10 3/24 3/29 6:30PM Pacific (extended)
4 Notebook with Executive Summary Present your findings in an iPython notebook with executive summary, visuals, and recommendations 3/24 3/29 3/31 6:30PM Pacific

Final Project

Final Project, Part Description Goal Assigned Due
1 Lightning Presentation Prepare a one-minute lightning talk that covers 3 potential project topics 2/25 3/22 12:00PM Pacific
2 Experiment Write-Up Create an outline of your research design approach, including hypothesis, assumptions, goals, and success metrics 3/22 4/12 6:30PM Pacific
3 Exploratory Analysis Confirm your data and create an exploratory analysis notebook with stat analysis and visualization 4/12 4/19 6:30PM Pacific
4 Notebook Draft Detailed iPython technical notebook with a summary of your statistical analysis, model, and evaluation metrics 4/12 4/26 6:30PM Pacific
5 Presentation Detailed presentation deck that relates your data, model, and findings to a non-technical audience 4/12 4/28 6:00PM Pacific