Permalink
Browse files

updated course info and link locations

  • Loading branch information...
messiest committed Apr 16, 2018
1 parent f50173a commit aa1b009ee9fba00d75ce57730fd46bf22ceb7b1c
Showing with 53 additions and 51 deletions.
  1. +53 −51 README.md
104 README.md
@@ -17,7 +17,7 @@
<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.
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:

@@ -34,9 +34,11 @@ Course materials provided for this curriculum use Python 3.6; however, you can s
<a id='team'></a>
## Your Instructional Team

**Instructor**: [X](X)
**Instructor**: [Tim Book](https://www.linkedin.com/in/timothykbook/)

**Assistant**: [X](X)
**Assistant**: [Chris Messier](https://www.linkedin.com/in/messiest/)

**Assistant**: [Matt Speck](https://www.linkedin.com/in/mjspeck/)

---

@@ -45,12 +47,12 @@ Course materials provided for this curriculum use Python 3.6; however, you can s

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

| Unit | Title | Topics Covered | Length |
| 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 |
| 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 |

---

@@ -63,50 +65,50 @@ 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 |
[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 |
[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 |
[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 |
[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
[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/wave2-dc-mw/what-is-data-science
[1-1B]: https://git.generalassemb.ly/wave2-dc-mw/your-development-environment
[1-1C]: https://git.generalassemb.ly/wave2-dc-mw/python-foundations
[1-1D]: https://git.generalassemb.ly/wave2-dc-mw/fundamentals-review
[1-1E]: https://git.generalassemb.ly/wave2-dc-mw/statistics-review
[1-1F]: https://git.generalassemb.ly/wave2-dc-mw/experiments-hypothesis-tests
[1-1G]: https://git.generalassemb.ly/wave2-dc-mw/exploratory-data-analysis
[1-1H]: https://git.generalassemb.ly/wave2-dc-mw/visualizations
[1-1I]: https://git.generalassemb.ly/wave2-dc-mw/working-with-data-review
[1-1J]: https://git.generalassemb.ly/wave2-dc-mw/linear-regression
[1-1K]: https://git.generalassemb.ly/wave2-dc-mw/train-test-split-and-bias-variance
[1-1L]: https://git.generalassemb.ly/wave2-dc-mw/knn-classification
[1-1M]: https://git.generalassemb.ly/wave2-dc-mw/logistic-regression
[1-1N]: https://git.generalassemb.ly/wave2-dc-mw/data-modeling-review
[1-1O]: https://git.generalassemb.ly/wave2-dc-mw/getting-data-APIs
[1-1P]: https://git.generalassemb.ly/wave2-dc-mw/natural-language-processing
[1-1Q]: https://git.generalassemb.ly/wave2-dc-mw/decision-trees
[1-1R]: https://git.generalassemb.ly/wave2-dc-mw/flex_clustering
[1-1T]: https://git.generalassemb.ly/wave2-dc-mw/applications-review
[1-1U]: https://git.generalassemb.ly/wave2-dc-mw/unit-4_project

---

@@ -169,7 +171,7 @@ The final project is broken down into five smaller deliverables, which walks you
---

<a id='tech'></a>
## Recommended Technology Requirements
## Tech Requirements

#### Hardware

@@ -180,7 +182,7 @@ The final project is broken down into five smaller deliverables, which walks you

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.
> 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/)
@@ -215,8 +217,8 @@ We'll be using Slack for our in-class communications. Slack is a messaging platf
## 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)
* Chris Messier's Office Hours - Day, Time (or by Appointment)
* Matt Speck's Office Hours - Day, Time (or by Appointment)

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

0 comments on commit aa1b009

Please sign in to comment.