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# 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, you can still access legacy Python 2.7 versions by checking out the python2 branch for any given PT DS repository.

Your Instructional Team

Instructor: David Yakobovitch

Assistant: Chris Messier

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:

Lesson Unit Number Session Number
What is 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
--- --- ---
Statistics Review Unit 2 Session 5
Experiments & Hypothesis Testing Unit 2 Session 6
Exploratory Data Analysis Unit 2 Session 7
Data Visualization in Python 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
--- --- ---
Decision Trees Unit 4 Session 15
Natural Language Processing Unit 4 Session 16
Clustering Unit 4 Session 17
Getting Data from APIs Unit 4 Session 18
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: 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.

Note: Unit projects 1 and 2 are required, whereas Unit 3 project is optional... but strongly encouraged!

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 five 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 (Optional)
  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 Status
Unit 1, Lesson 1 Review All Projects and Deliverables Discussion
Unit 1, Lesson 4 Unit Project 1 Complete in-Class or as HW
--- --- ---
Unit 2, Lesson 2 Review Final Project Datasets Discussion or as HW
Unit 2, Lesson 5 Unit Project 2 Complete in-Class or as HW
Unit 2, Lesson 5 Final Project Pt 1: Create Problem statement Assigned
--- --- ---
Unit 3, Lesson 1 Final Project Pt 1: Create Problem statement Due
Unit 3, Lesson 1 Final Project Pt 2: Define Data sources Assigned
Unit 3, Lesson 3 Final Project Pt 2: Define Data sources Due
Unit 3, Lesson 3 Final Project Pt 3: Perform EDA on Data Assigned
Unit 3, Lesson 5 Unit Project 3 Optional: Complete In-Class or as HW
--- --- ---
Unit 4, Lesson 1 Final Project Pt 3: Perform EDA on Data Due
Unit 4, Lesson 1 Final Project Pt 4: Model Data Assigned
Unit 4, Lesson 4 Final Project Pt 4: Model Data Due
Unit 4, Lesson 4 Final Project Pt 5: Present Data Assigned
Unit 4, Lesson 6 Final Project Pt 5: Present Data Due

Recommended Technology Requirements


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


  1. Download and Install Anaconda with Python 3.6.

Note: Anaconda provides support for two different versions of Python. We'll primarily use Python 3.6 in this course.

PC only

Browser Check

  • Google Chome

Additional Items

  • Text editor; we recommend 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 dsfundamentals.slack.com .

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


[Add specific local market attendance, student policy, and parking expectations here]

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.

In Person

  • Tuesdays and Thursdays, before class (or by Appointment)


  • Monday and Wednesday evenings by Slack

Slack us or post in our #officehours channel to reserve a time-slot!

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.

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