Repo for Seattle Part-time Data Science 10-2019
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
lessons add session on working with APIs Dec 11, 2019
projects/required add solutions to linear regression module. Dec 1, 2019
.gitignore add linear regression lesson (session 10) Nov 8, 2019 add lesson 4 Oct 22, 2019

Welcome to Data Science

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

Course Overview

Welcome to the part time Data Science course at General Assembly! We are building a global community of lifelong learners who are excited about using data to solve real world problems.

In this program, we will use Python to explore datasets, build predictive models, and communicate data driven insights. Specifically, you will learn how to:

  • Define many of the approaches and considerations that data scientists use to solve real world problems.
  • Perform exploratory data analysis with powerful programmatic tools in Python.
  • 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.

What You Will Learn

  • Statistical Analysis with Python:
  • Perform visual and statistical analysis on data using Python and its associated libraries and tools.
  • Data-Driven Decision-Making:
  • Define and determine the trade-offs involving feature selection, model accuracy, and data quality.
  • Machine Learning & Modeling Techniques:
  • Explore supervised learning techniques, inlcuding classification, regression, and decision trees.
  • Visualizations & Presentations:
  • Create visualizations and interactive notebooks to present to industry stakeholders.

Python Version

The curriculum materials for this course are written in Python 3.6. The material is compatible with Python 3.7.

Your Instructional Team

Instructor: Naumaan Nayyar (

Curriculum Structure

General Assembly's Data Science part time materials are organized into four units.

Unit Title Topics Covered Length
Unit 1 Data Foundations Python Syntax, Development Environment Lessons 1-4
Unit 2 Working with Data Stats Review, Visualization, & EDA Lessons 5-9
Unit 3 Data Science Modeling Regression, Classification, & KNN Lessons 10-14
Unit 4 Data Science Applications Decision Trees, NLP, & Flex Topics Lessons 15-19

Course Schedule

  • Class Dates: Tuesdays and Thursdays from 6-9pm
  • Course Duration: (October 8th - December 17th, 2019)
  • Holiday(s): November 28th

Lesson Schedule

Here is the schedule we will be following for our part time data science course:

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
FLEX: Project Workshop + Presentations Unit 1 Session 4
--- --- ---
Exploratory Data Analysis in Pandas Unit 2 Session 5
Data Visualization in Python Unit 2 Session 6
Statistics in Python Unit 2 Session 7
Experiments & Hypothesis Testing Unit 2 Session 8
FLEX: Project Workshop + Presentations 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
FLEX: Project Workshop + Presentations Unit 3 Session 14
--- --- ---
Working With Data: APIs Unit 4 Session 15
Intro to Natural Language Processing Unit 4 Session 16
Intro to Time Series Unit 4 Session 17
FLEX: Instructor Choice Unit 4 Session 18
FLEX: Review + Project Workshop Unit 4 Session 19
Final Project Presentations Unit 4 Session 20

Flex Topics

Flex sessions are designed to provide the class with time to catch up, review materials, ask questions, work on projects, or go deeper into specific topic areas.

Depending on class interest, we may optionally cover additional topics, such as:

Decision Trees
Robust Regression
Deploying Models with Flask

Project Structure

This course will ask you to complete a series of projects in order to practice and apply the skills covered in-class.

Unit Projects

At the end of each Unit, you'll work on short structured projects. These activities will test your understanding of that unit’s most important concepts with in-class practice and instructor support.

For those of you who want to go above and beyond, we’ve also included stretch options, bonus activities, and other opportunities for further reading and practice.

Final Project

In our final project, we'll ask you to apply your skills to a real-world or business problem of your choice.

This is an opportunity for you to demonstrate your new skills and tackle a pressing issue relevant to your life, industry, or organization. You’ll create a hypothesis, analyze internal data, and generate a working model, prototype, solution, or recommendation.

You will get structured guidance and designated time to work throughout the course. Final project deliverables include:

  • Proposal: Describe your chosen problem and identify relevant data sets (confirming access, as needed).
  • Brief: Share a summary of your initial analysis and your next steps with your instructional team.
  • Report: Submit a cleanly formatted Jupyter notebook (or other files) documenting your code and process for technical/peer stakeholders.
  • Presentation: Present a summary of your business problem, approach, and recommendation to an audience of non-technical executive stakeholders.

Project Breakdown

  1. Project 1: Python Technical Code Challenges
  2. Project 2: Exploratory Data Analysis
  3. Project 3: Modeling Practice
  4. Project 4: Final Project
    • Part 1: Proposal + Dataset
    • Part 2: Initial EDA Brief
    • Part 3: Technical Report
    • Part 4: Presentation

Project Schedule

  • Project 1: Due @ End of Unit 1
  • Project 2: Due @ End of Unit 2
  • Project 3: Due @ End of Unit 3
  • Project 4 (Final):
    • Proposal + Dataset: Due @ End of Unit 2
    • Initial EDA Brief: Due @ End of Unit 3
    • Technical Report: Due @ End of Unit 4
    • Presentation: Due @ End of Unit 4

Technology Requirements


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


  1. Download and Install Anaconda with Python 3.7.

Note: Anaconda provides support for two different versions of Python. Make sure to install the "Python 3.7" version.

PC only


  • Google Chrome
  • Mozilla Firefox



We'll use Slack for our class communications platform. 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 and the course channel is dat-oct-19.

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


Students are expected to:

  • Miss no more than 2 class sessions over the duration of the course
  • Complete at least 80% of assigned homework
  • Complete the final project
  • Participate in feedback surveys

All assignments must be submitted by the final day of the course in order to receive credit.

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.

  • Instructor's Office Hours - Wednesdays 5:30-7:30pm (or by Appointment - slack Naumaan N)

Slack me at Naumaan N 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.

Data Science Seattle - Exit Ticket

Contribution Guidelines

Contributions are welcome! To contribute to any course materials in this organization, please reach out the course instructor.