- Welcome
- Your Team
- Course Overview
- Course Schedule
- Projects
- Tech Requirements
- Classroom Tools: Slack
- Student Expectations
- Office Hours
- Student Feedback
Welcome to the part time Data Science course at General Assembly!
In our part-time course, we will use Python (currently v2.7) to explore datasets, build predictive models, and communicate data driven insights.
Specifically, you will learn to:
- Define the language and approaches used by data scientists to solve real world problems.
- Perform exploratory data analysis with powerful programmatic tools, including the command line, python, and pandas.
- Build and refine basic machine learning models to predict patterns from data sets.
- Communicate data driven insights to peers and stakeholders in order to inform business decisions.
Please reach out to the instructional team via Slack!
Instructor:
- Stefan Jansen: @Stefan
Assistant:
- Beth Tenorio: @beth, booking link is https://bethtenorio.youcanbook.me/
- Pauline Chow: @pauline, booking link is https://paulinechow.youcanbook.me
Contact: online@generalassemb.ly
- 11/28 to 02/08
- Tuesdays and Thursdays, 7-10pm EST
- Holiday Schedule (No Class): Christmas Week (12/26, 12/28)
General Assembly's Data Science part time materials are organized into four units.
| Unit | Title | Topics Covered | Length |
|---|---|---|---|
| Unit 1 | Foundations | Python Syntax, Development Environment | Lessons 1-4 |
| Unit 2 | Working with Data | Stats Review, Visualization, & EDA | Lessons 5-9 |
| Unit 3 | Data Modeling | Regression, Classification, & KNN | Lessons 10-14 |
| Unit 4 | Applications | Decision Trees, NLP, Trends | Lessons 15-19 |
Here is the schedule we will be following for our part time data science curriculum
| Lesson | Unit Number | Session Number | Date |
|---|---|---|---|
| What is Data Science? | Unit 1 | Session 1 | Nov. 28 |
| Your Development Environment | Unit 1 | Session 2 | Nov. 30 |
| Python Foundations | Unit 1 | Session 3 | Dec. 5 |
| Review + Project Workshop | Unit 1 | Session 4 | Dec. 7 |
| Statistics Review | Unit 2 | Session 5 | Dec. 12 |
| Stats & Visualizations in Python | Unit 2 | Session 6 | Dec. 14 |
| Exploratory Data Analysis | Unit 2 | Session 7 | Dec. 19 |
| Data Visualization in Python | Unit 2 | Session 8 | Dec. 21 |
| Review + Project Workshop | Unit 2 | Session 9 | Jan. 2 |
| Linear Regression | Unit 3 | Session 10 | Jan. 4 |
| Train-Test Split & Bias-Variance | Unit 3 | Session 11 | Jan. 9 |
| KNN / Classification | Unit 3 | Session 12 | Jan. 11 |
| Logistic Regression | Unit 3 | Session 13 | Jan. 16 |
| Decision Trees | Unit 3 | Session 14 | Jan. 18 |
| Review + Project Workshop | Unit 3 | Session 15 | Jan. 23 |
| Clustering | Unit 4 | Session 16 | Jan. 25 |
| Natural Language Processing | Unit 4 | Session 17 | Jan. 30 |
| Getting Data from API's | Unit 4 | Session 18 | Feb. 1 |
| Review + Project Workshop | Unit 4 | Session 19 | Feb. 6 |
| Project Presentations | Unit 4 | Session 20 | Feb. 8 |
| Tuesday | Topic | Thursday | Topic |
|---|---|---|---|
| 11/28 | What is Data Science? | 11/30 | Your Development Environment |
| 12/05 | Python Foundations | 12/07 | Review + Project Workshop |
| 12/12 | Statistics Review | 12/14 | Stats & Visualizations in Python |
| 12/19 | Exploratory Data Analysis | 12/21 | Data Visualization in Python |
| 01/02 | Review + Project Workshop | 01/04 | Linear Regression |
| 01/09 | Train-Test Split & Bias-Variance | 01/11 | KNN / Classification |
| 01/16 | Logistic Regression | 01/18 | Decision Trees |
| 01/23 | Review + Project Workshop | 01/25 | Clustering |
| 01/30 | Natural Language Processing | 02/01 | Getting Data from API's |
| 02/06 | Review + Project Workshop | 02/08 | Project Presentations |
This course provides two types of projects: unit projects and a final project.
Our data science course contains three unit projects, to be completed at the end of each unit. These enrichment projects ask you to synthesize the skills learned in that unit. You will be required to complete projects for Units 1 and 2.
Note: Our Unit 3 project is optional, but strongly encouraged!
The final project asks you to apply your skills to a real world problem. This final project is broken down into five smaller deliverables, which helps you to perform each step of our data science workflow while tackling a real world projet.
- Project 1: Python Technical Code Challenges
- Project 2: EDA + Chipotle
- Project 3: Linear Regression and KNN Practice (Optional)
- Project 4: Final Project
- Part 1: Create Proposal
- Part 2: Identify Dataset
- Part 3: Perform EDA
- Part 4: Model Data
- Part 5: Present Findings
| Date | Deliverable |
|---|---|
| 12/07 | Unit Project 1 - Python Code Challenges |
| 01/02 | Unit Project 2 - Exploratory Data Analysis (EDA) |
| 01/04 | Final Pt 1: Create Problem statement |
| 01/07 | Final Pt 2: Define Data sources |
| 01/18 | Optional Project 3: Regression & KNN Practice |
| 01/23 | Final Pt 3: Perform EDA on Data |
| 02/01 | Final Pt 4: Model Data |
| 02/06 | Project Workshop |
| 02/08 | Final Project Presentations |
- 8GB Ram (at least)
- 10GB Free Hard Drive Space (after installing Anaconda)
- Download and Install Anaconda with Python 2.7.
- Note: If you have already downloaded Anaconda for Python 3.6, that is not a major issue. We will just need to add a modified configuration to your development environment.
MAC only
- Install HomeBrew
PC only
- Install Git Bash
- Google Chome
- Firefox (optional)
- Most students may wish to install a text editor; we recommend Sublime or Atom
We'll be using Slack for our in-class communications. Slack is a messaging platform where you can chat with your peers and instructors. We will use Slack to share information about the course, discuss lessons, and submit projects. Our Slack homepage is datr1128.
Pro Tip: If you've never used Slack before, check out these resources:
Every week, your instructional team will hold office hours where you can get in touch to ask questions about anything relating to the course. This is a great opportunity to follow up on questions or ask for more details about any topics covered so far.
- IA Office Hours - By appointment using this link: (TBD)
Slack us or post in our #datr1128-office-hours channel to reserve a time-slot!
Throughout the course, you'll be asked to provide feedback about your experience. This feedback is extremely important, as it helps us provide you with a better learning experience.