- Your Team
- Course Overview
- Course Schedule
- Tech Requirements
- Classroom Tools: Slack
- Student Expectations
- Office Hours
- Student Feedback
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.
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: Vera Chernova
General Assembly's Data Science part time materials are organized into four units.
|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|
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|
This course will ask you to complete two sets of projects: three short unit projects and a longer final project.
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.
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 1: Python Technical Code Challenges
- Project 2: EDA + Chipotle
- Project 3: Linear Regression and KNN Practice
- Project 4: Final Project
- Part 1: Create Proposal
- Part 2: Identify Dataset
- Part 3: Perform EDA
- Part 4: Model Data
- Part 5: Present Findings
|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|
- Project 1: Friday, July 27
- Project 2: Friday, August 10
- Project 3: Friday, August 24
- Project 4:
- Part 1: Friday, August 10
- Part 2: Friday, August 17
- Part 3: Friday, August 24
- Part 4: Whenever you present!
- 8GB Ram (at least)
- 10GB Free Hard Drive Space (after installing Anaconda)
- Download and Install Anaconda with Python 3.6.
- Install Git Bash
- Google Chrome
- 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 DataScienceFundamentals.
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.
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.
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.