| Course ID | nyc18_ds14 |
|---|---|
| Section | Winter 2018 - Cohort 14 |
| City | New York |
| Dates | 8-Jan-2018 to 30-Mar-2018 |
| Slack | ds14 team (use the app) |
i. Administrative 1. Class Work 2. Projects 3. Challenges 4. Blogs 5. Assessments 6. Investigations 7. Weekly Topics
- Class Lectures
- Resources
- Git Notes
- Blog Examples
- etc
- Presentation slides, in PDF format, should be submitted (via pull request) to the course repo, under student_submissions/projects folder
- A Markdown file with a link to your project repo should also be included. Do not submit code and data from projects to the course repo
| Project | Week | Topics | Due | |
|---|---|---|---|---|
| 1 | Benson | 1 | MTA turnstile | Fri, Jan 12 |
| 2 | Luther | 2, 3 | Movies, webscraping, regression | Fri, Jan 26 |
| 3 | McNulty | 4, 5, 6.5 | Supervised, D3 | Wed, Feb 14 |
| 4 | Fletcher | 6.5, 7, 8 | Unsupervised, NLP | Fri, Mar 02 |
| 5 | Kojak | 9, 10, 11, 12 | Passion Project | Thur Mar 29 |
Note:
- Review the Instructions before submitting!
- Students are permitted and encouraged to work with other students on the challenge sets
- Students should submit challenges individually (not by group); include names of people with whom you worked in header
- OK to submit partial work, full set is encouraged but not required
- Students should submit whatever they have completed by due date; can update and resubmit challenge sets at a later date
- No grades are assigned; focus is on learning the topics
| Challenge Set | Project Group | Topic | Note * | Date Assigned | Date Due |
|---|---|---|---|---|---|
| 1 | Benson | Explore MTA data | required | Mon, Jan 8 | Tue, Jan 16 |
| 2 | Benson | Math Primer | required | Mon, Jan 8 | Tue, Jan 16 |
| 3 | Benson | Pandas movies | required | Tue, Jan 16 | Mon, Jan 22 |
| 4 | Benson | Probability | required | Tue, Jan 16 | Mon, Jan 22 |
| 5 | Luther | Linear Splitting | required | Mon, Jan 22 | Mon, Jan 29 |
| 6 | Luther | Linear Learning | required | Mon, Jan 22 | Mon, Jan 29 |
| 7 | McNulty | Classification | required | Mon, Jan 29 | Mon, Feb 5 |
| 8 | McNulty | Classification Errors | required | Mon, Jan 29 | Mon, Feb 5 |
| 9 | McNulty | SQL | parts i, ii, iii required (part iv is optional) | Mon, Feb 5 | Mon, Feb 12 |
| 10 | McNulty | Decision Tree | recommended | Mon, Feb 5 | Mon, Feb 12 |
| 11 | McNulty | Poisson GLM | recommended | Mon, Feb 12 | Mon, Feb 19 |
| 12 | McNulty | D3 | recommended | Mon, Feb 12 | Mon, Feb 19 |
| 13 | Fletcher | Flask | recommended | Mon, Feb 19 | Mon, Feb 26 |
| 14 | Fletcher | Mongo Twitter | recommended | Mon, Feb 26 | Mon, Mar 5 |
| 15 | Fletcher | NLP Unsupervised | recommended | Mon, Mar 5 | Mon, Mar 12 |
| 16 | Kojak | Hadoop | recommended | Mon, Mar 12 | Mon, Mar 19 |
| 17 | Kojak | Hive | recommended | Mon, Mar 19 | Mon, Mar 26 |
*Full submission of challenge sets is encouraged, but partial submissions are ok.
- Required: 2 blogs
- Recommended: 5+ blogs
- Blog Tracking - link
| Blog | Topic | Note* | Due |
|---|---|---|---|
| 1 | ds / project 1 | required | Tues, Jan 16 |
| 2 | project 2 | required | Mon, Jan 29 |
| 3 | project 3 | recommended | Mon, Feb 19 |
| 4 | project 4 | recommended | Mon, Mar 5 |
| 5 | project 5 final | recommended* | Fri, Apr 2 |
*Blog 5 date is after the bootcamp graduation
- There will be 2 Quizzes given (dates below). The quiz will be given in class & will be multiple-choice format.
| Quiz | Topics | Note* | Date |
|---|---|---|---|
| 1 | Weeks 1-4 | required | Mon, Mar 5 |
| 2 | Weeks 5-7 | required | Mon, Mar 26 |
- Required: 2 presentations
- Investigation Signup Link
- After investigation presentation, a pdf copy should be submitted (via pull request) to the course repo, under student_submissions/investigations folder
| Wk | Date | Project | Topics |
|---|---|---|---|
| 1 | 1/8 | Benson | GitHub, MTA Turnstile, Pandas, Visualization with Matplotlib |
| 2 | 1/15 | Luther | Webscraping, Pickling, Linear Regression, Cross-validation,probability |
| 3 | 1/22 | Luther | Linear Regression, Regularization, Null Hypothesis, Bayes |
| 4 | 1/29 | McNulty | Supervised Learning, K-Nearest Neighbors, Logistic Regression, Amazon AWS, PostgreSQL, Support Vector Machine, Decision Trees and Random Forests |
| 5 | 2/5 | McNulty | Naive Bayes Classification, SGD, Deep Learning and Neural Networks, Javascript, Flask & D3 |
| 6 | 2/12 | McNulty | MLE, GLM, Poisson, NLP, APIs, Mongo |
| 7 | 2/19 | Fletcher | KMeans, PCA, LDA, Word2Vec, More Clustering |
| 8 | 2/26 | Fletcher | Review, Recommenders, More Deep Learning |
| 9 | 3/5 | Kojak | Docker, Hadoop, Hive, Spark |
| 10 | 3/12 | Kojak | Final Project Work |
| 11 | 3/19 | Kojak | Final Project Work |
| 12 | 3/26 | Kojak | Final Project Work |
*Metis is closed on 15-Jan-2018 (MLK holiday)