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Wave 4 Remote Data Fundamentals for Pacific Timezone
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Kevin McCullough
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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
  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.


Your Instructional Team

Instructor: Kevin McCullough (kevmccullough1@gmail.com)

Assistants: Kelvin Sumlin (ksumlin@gmail.com), Ed Salinas (edrian.salinas@gmail.com)


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

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
Experiments & Hypothesis Testing Unit 2 Session 7
Statistics in Python 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
Clustering Unit 4 Session 18
FLEX: Review + Project Workshop Unit 4 Session 19
Final Project Presentations Unit 4 Session 20

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

You'll also complete a final project, asking you to apply your skills to a real-world or business problem of your choice.

The capstone 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 (EOD Oct 7)
  • Project 2: Due @ End of Unit 2 (EOD Oct 26)
  • Project 3: Due @ End of Unit 3 (EOD Nov 18)
  • Project 4 (Final):
    • Proposal + Dataset: Due @ End of Unit 2 (EOD Oct 28)
    • Initial EDA Brief: Due @ End of Unit 3 (EOD Nov 16)
    • Technical Report: Due @ End of Unit 4 (EOD Dec 3)
    • Presentation: Due @ End of Unit 4 (EOD Dec 3)

Technology 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.

Note: Anaconda provides support for two different versions of Python. Make sure to install the "Python 3.6" version.

PC only

  • Install Git Bash
  • Two things to reduce confusion:
    • Many first time Git users find Vim to be confusing. If you do not wish to learn Vim commands, select an alternative text editor such as Nano.
    • If you select "Use Git and optional Unix tools from the Windows Command Prompt" you will be able to use the the command prompt for all Git related tasks in this course.

Browser

  • Google Chrome

Miscellaneous

  • Text editor (we recommend Atom)

Slack

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 pst-remote-mw-wave4.

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


Expectations

  • Participate in classroom discussions & complete class feedback forms
  • Be respectful to others
  • Miss no more than 2 classes (Booz Allen Hamilton requirement)
  • Complete all unit assignments
  • Complete the final project

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.

Slack us 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.