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README.md

README.md

Welcome to Data Science

  1. Welcome
  2. Your Team
  3. Course Overview
  4. Course Schedule
  5. Projects
  6. Tech Requirements
  7. Classroom Tools: Slack
  8. Student Expectations
  9. Office Hours
  10. Student Feedback

Welcome to the part time Data Science course at General Assembly!

In our part-time course, we will use Python 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.

Python Version

Course materials provided for this curriculum use Python 3.6. However, Python 3.7 has been released only a few weeks prior to the start of our course. If you installed Python 3.7, this will also be fine. No 3.6 vs 3.7 differences will impact any of our class. The same goes if you have Python 3.5.


Your Instructional Team

Instructor: Tim Book

Instructor Associate #1: Adi Bronshtein

Instructor Associate #2: Harsha Goonawardana


Curriculum Structure

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, Common Models Lessons 15-19

Lesson Schedule

Here is the schedule we will be following for our part time data science course. The order of lessons taught is tentative as of this writing. The Unit 4 topics are also tentative, and are up for debate as the course goes on.

Lesson Unit Number Session Number
Welcome to Data Science Unit 1 Session 1
Your Development Environment Unit 1 Session 2
Python Foundations Unit 1 Session 3
Review + Project Workshop Unit 1 Session 4
--- --- ---
Exploratory Data Analysis Unit 2 Session 5
Experiments & Hypothesis Testing Unit 2 Session 6
Data Visualization in Python Unit 2 Session 7
Statistics Review Unit 2 Session 8
Review + Project Workshop Unit 2 Session 9
--- --- ---
Linear Regression Unit 3 Session 10
Train-Test Split & Bias-Variance Unit 3 Session 11
KNN / Classification Unit 3 Session 12
Logistic Regression Unit 3 Session 13
Review + Project Workshop Unit 3 Session 14
--- --- ---
Natural Language Processing Unit 4 Session 15
Decision Trees Unit 4 Session 16
Clustering Unit 4 Session 17
MINITOPIC: Advanced Python + Automation Unit 4 Session ??
MINITOPIC: Support Vector Machines Unit 4 Session ??
MINITOPIC: Principal Components Unit 4 Session ??
Review + Project Workshop Unit 4 Session 19
Project Presentations Unit 4 Session 20

Project Structure

This course will ask you to complete two sets of projects: three short unit projects and a longer final project.

Unit Projects

At the end of each unit, we'll ask you to complete a small project. These enrichment projects require you to synthesize the skills learned in that unit. There are three unit projects.

Final Project

You'll also complete a longer final project, which asks you to apply your skills to a real world problem. At the end of the course, you'll be asked to share your final project with peers and colleagues.

The final project is broken down into four smaller deliverables, which walks you through every step of the data science workflow as you tackle a real world project.

Project Breakdown

  1. Project 1: Python Technical Code Challenges
  2. Project 2: EDA + Chipotle
  3. Project 3: Linear Regression and KNN Practice
  4. Project 4: Final Project
    • Part 1: Create Proposal
    • Part 2: Identify Dataset
    • Part 3: Perform EDA
    • Part 4: Model Data
    • Part 5: Present Findings

Project Schedule

Lesson Deliverables
Unit 1 Unit Project 1
--- ---
Unit 2 Unit Project 2
Unit 2 Final Project Pt 1: Create Problem statement, get data
--- ---
Unit 3 Final Project Pt 2: Perform EDA on Data
Unit 3 Unit Project 3
--- ---
Unit 4 Final Project Pt 3: Model Data
Unit 4 Final Project Pt 4: Present Data

Due Dates

  • Project 1: Thursday, July 26
  • Project 2: Tuesday, August 14
  • Project 3: Thursday, August 30
  • Project 4:
    • Part 1: Thursday, August 16
    • Part 2: Tuesday, August 28
    • Part 3: Wednesday, September 12
      • If you elect to present Thursday, September 13th, we will push your due date back to Friday, Sept 14th. You may ask us for initial feedback while building your presentation.
    • Part 4: Whenever you present!

Tech Requirements

Hardware

  1. 8GB Ram (at least)
  2. 10GB Free Hard Drive Space (after installing Anaconda)

Software

  1. Download and Install Anaconda with Python 3.6.

PC only

Browser Check

  • Google Chrome

Additional Items

  • Text editor; we recommend Atom

Slack

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 DataScienceFundamentals.

Pro Tip: If you've never used Slack before, check out these resources:


Office Hours

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.

Office hours for your IAs will be pinned in our Slack channel!

The instructor (Tim) does not have office hours. However, he will always be available on Slack, where he lives. He is also often available before and after class. If you would like to meet with him outside these times, please Slack him to schedule an appointment.


Student Feedback

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.

The Exit Ticket will be administered 5 minutes before the end of each class. A link will be sent out on Slack.