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General Assembly's Data Science course in Boston, MA (Winter-Spring 2015)

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DAT5 Course Repository

Course materials for General Assembly's Data Science course in Boston, MA (20 January 2015 - 07 April 2015). View student work in the student repository.

Instructor: Bryan Balin. Teaching Assistant: Harish Krishnamurthy.

Office hours: Wednesday 5-6pm; Friday 5-6:30pm, @Boston Public Library; as needed Tuesdays and Thursdas @GA.

Course Project information

Tuesday Thursday
1/20: Introduction 1/22: Python & Pandas
1/27: Git and GitHub 1/29: Getting Data
2/3: Advanced Pandas
Milestone: Question and Data Set
2/5: Numpy, Machine Learning, KNN
2/10: scikit-learn, Model Evaluation Procedures 2/12: Linear Regression
2/17: Logistic Regression,
Preview of Other Models
2/19: Model Evaluation Metrics
Milestone: Data Exploration and Analysis Plan
2/24: Working a Data Problem 2/26: Clustering and Visualization
Milestone: Deadline for Topic Changes
3/3: Naive Bayes 3/5: Natural Language Processing
3/10: Decision Trees and Ensembles
Milestone: First Draft
3/12: Advanced scikit-learn
3/17: No Class 3/19: Databases and MapReduce
3/24: Recommenders 3/26: Course Review, Companion Tools
Milestone: Second Draft (Optional)
3/31: TBD 4/2: Project Presentations
4/7: Project Presentations

Installation and Setup

  • Install the Anaconda distribution of Python 2.7x.
  • Install Git and create a GitHub account.
  • Once you receive an email invitation from Slack, join our "datbos05 team" and add your photo!

Class 1: Introduction

  • Introduction to General Assembly
  • Course overview: our philosophy and expectations (slides)
  • Data science overview (slides)
  • Tools: check for proper setup of Anaconda, overview of Slack

Homework:

  • Resolve any installation issues before next class.

Optional:

Class 2: Python & Pandas

slides

  • Brief overview of Python
  • Brief overview of Python environments: Python scripting, IPython interpreter, Spyder
  • Working with data in Pandas
    • Loading and viewing data
    • Indexing and selecting data
    • Assigning, reassigning, and splitting data
    • Describing and summarizing data
    • Plotting data

Homework:

Optional:

Resources:

Class 3: Git and GitHub

  • Check for proper setup of Git by running git clone https://github.com/bbalin12/DAT-project-examples.git

Class 4: Getting Data

Class 5: Advanced Pandas

Class 6: Numpy, Machine Learning, KNN

Class 7: scikit-learn, Model Evaluation Procedures

Class 8: Linear Regression

Class 9: Logistic Regression, Preview of Other Models

Class 10: Model Evaluation Metrics

Class 11: Working a Data Problem

Class 12: Clustering and Visualization

Class 13: Naive Bayes

Class 14: Natural Language Processing

Class 15: Decision Trees and Ensembles

Class 16: Advanced scikit-learn

Class 17: Databases and MapReduce

Class 18: Recommenders

Class 19: Course Review, Companion Tools

Class 20: TBD

Class 21: Project Presentations

Class 22: Project Presentations

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General Assembly's Data Science course in Boston, MA (Winter-Spring 2015)

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