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.DS_Store

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
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build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
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nosetests.xml
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*,cover
.hypothesis/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py

# Flask stuff:
instance/
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# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
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# IPython Notebook
.ipynb_checkpoints

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule

# dotenv
.env

# virtualenv
venv/
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# Spyder project settings
.spyderproject

# Rope project settings
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# ![](https://ga-dash.s3.amazonaws.com/production/assets/logo-9f88ae6c9c3871690e33280fcf557f33.png) Welcome to Data Science


1. [Welcome](#welcome)
2. [Your Team](#team)
3. [Course Overview](#course)
4. [Course Schedule](#schedule)
5. [Projects](#projects)
6. [Tech Requirements](#tech)
7. [Classroom Tools: Slack](#slack)
8. [Student Expectations](#expectations)
9. [Office Hours](#hours)
10. [Student Feedback](#feedback)

---

<a id='welcome'></a>
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.

---
<a id='team'></a>
## Your Instructional Team

**Instructor**: [X](X)

**Assistant**: [X](X)

---

<a id='course'></a>
## 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 |

---


<a id='schedule'></a>
## 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?][1-1A] | Unit 1 | Session 1 |
[Your Development Environment][1-1B] | Unit 1 | Session 2 |
[Python Foundations][1-1C] | Unit 1 | Session 3 |
[Review + Project Workshop][1-1D] | Unit 1 | Session 4 |
--- | --- | --- |
[Statistics Review][1-1E] | Unit 2 | Session 5 |
[Experiments & Hypothesis Testing][1-1F] | Unit 2 | Session 6 |
[Exploratory Data Analysis][1-1G] | Unit 2 | Session 7 |
[Data Visualization in Python][1-1H] | Unit 2 | Session 8 |
[Review + Project Workshop][1-1I] | Unit 2 | Session 9 |
--- | --- | --- |
[Linear Regression][1-1J] | Unit 3 | Session 10 |
[Train-Test Split & Bias-Variance][1-1K] | Unit 3 | Session 11 |
[KNN / Classification][1-1L] | Unit 3 | Session 12 |
[Logistic Regression][1-1M] | Unit 3 | Session 13 |
[Review + Project Workshop][1-1N] | Unit 3 | Session 14 |
--- | --- | --- |
[Getting Data from API's][1-1O] | Unit 4 | Session 15 |
[Flex: Natural Language Processing][1-1P] | Unit 4 | Session 16 |
[Flex: Decision Trees][1-1Q] | Unit 4 | Session 17 |
[Flex: Clustering][1-1R] | Unit 4 | Session 18 |
[Review + Project Workshop][1-1T] | Unit 4 | Session 19 |
[Project Presentations][1-1U] | Unit 4 | Session 20 |


[1-1A]: https://git.generalassemb.ly/data-part-time/what-is-data-science
[1-1B]: https://git.generalassemb.ly/data-part-time/your-development-environment
[1-1C]: https://git.generalassemb.ly/data-part-time/python-foundations
[1-1D]: https://git.generalassemb.ly/data-part-time/fundamentals-review
[1-1E]: https://git.generalassemb.ly/data-part-time/statistics-review
[1-1F]: https://git.generalassemb.ly/data-part-time/experiments-hypothesis-tests
[1-1G]: https://git.generalassemb.ly/data-part-time/exploratory-data-analysis
[1-1H]: https://git.generalassemb.ly/data-part-time/visualizations
[1-1I]: https://git.generalassemb.ly/data-part-time/working-with-data-review
[1-1J]: https://git.generalassemb.ly/data-part-time/linear-regression
[1-1K]: https://git.generalassemb.ly/data-part-time/train-test-split-and-bias-variance
[1-1L]: https://git.generalassemb.ly/data-part-time/knn-classification
[1-1M]: https://git.generalassemb.ly/data-part-time/logistic-regression
[1-1N]: https://git.generalassemb.ly/data-part-time/data-modeling-review
[1-1O]: https://git.generalassemb.ly/data-part-time/getting-data-APIs
[1-1P]: https://git.generalassemb.ly/data-part-time/natural-language-processing
[1-1Q]: https://git.generalassemb.ly/data-part-time/decision-trees
[1-1R]: https://git.generalassemb.ly/data-part-time/flex_clustering
[1-1T]: https://git.generalassemb.ly/data-part-time/applications-review
[1-1U]: https://git.generalassemb.ly/data-part-time/unit-4_project

---

<a id='projects'></a>
## 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][2-1A]
2. [Project 2: EDA + Chipotle][2-1B]
3. [Project 3: Linear Regression and KNN Practice (*Optional*)][2-1C]
4. [Project 4: Final Project][2-1D]
- Part 1: Create Proposal
- Part 2: Identify Dataset
- Part 3: Perform EDA
- Part 4: Model Data
- Part 5: Present Findings

[2-1A]: https://git.generalassemb.ly/data-part-time/unit-1_project
[2-1B]: https://git.generalassemb.ly/data-part-time/unit-2_project
[2-1C]: https://git.generalassemb.ly/data-part-time/unit-3_project
[2-1D]: https://git.generalassemb.ly/data-part-time/unit-4_project

#### 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** |

---

<a id='tech'></a>
## Recommended 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](https://www.continuum.io/downloads).

> Note: Anaconda provides support for two different versions of Python. We'll primarily use Python 3.6 in this course.
**PC only**
- Install [Git Bash](https://git-for-windows.github.io/)

#### Browser Check
- Google Chome

#### Additional Items
- Text editor; we recommend [Atom](https://atom.io)
---

<a id='slack'></a>
## <img src="https://lh3.googleusercontent.com/CzlsZP3xUHeX3HAGdZ2rL9mK6_C-6T1-YWeBeM8nB3ilmfPSBHCFx4-UbQr8MnQms3d9=w300" width="25px"> 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 [X](x).

**Pro Tip**: If you've never used Slack before, check out these resources:
- [Intro to Slack](https://www.youtube.com/watch?v=9RJZMSsH7-g)
- [Slack Basics and Shortcuts](https://get.slack.help/hc/en-us/articles/217626358-Cheat-sheet-for-basics-and-shortcuts)
- [The Ultimate Slack Cheatsheet](https://chartmogul.attach.io/EyoxcOGL)

---

<a id='expectations'></a>
## Expectations

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

---

<a id='hours'></a>
## 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 - Day, Time (or by Appointment)
* Assistant's Office Hours - Day, Time (or by Appointment)

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

---

<a id='feedback'></a>
## 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.

[Insert specific VTS/Exit Ticket details here]

---

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