MadDataScience/DAT4

Class Materials for General Assembly's Data Science course held in San Francisco

GA_DS_SF_2014_01

Class Materials for General Assembly's Data Science course held in San Francisco

IMPORTANT DATES

• 1/27: First Project Proposals
• 2/3: Formal Project Proposals (including data and methods chosen)
• 2/17: NO CLASS
• 2/19: Project live on Github
• 3/3: Peer Feedback

SYLLABUS

A. OVERVIEW

1. TOOLS OF THE TRADE
   • Introduction to Data Science; Lab on Python

B. DATA

2. BIG DATA
   • MAP-REDUCE AND HADOOP
   • DISTRIBUTED COMPUTING AND IPYTHON.PARALLEL
3. SEMI-STRUCTURED DATA: REST APIS, MONGO, ETC.
   • WORKING WITH MONGO, API REQUESTS, AND JSON
4. STRUCTURED DATA: RELATIONAL DATABASES AND DATAFRAMES
   • RDBMS AND PANDAS

C. SCIENCE

6. R
   • EXPLORATORY DATA ANALYSIS: R & GGPLOT
   • MACHINE LEARNING in R
7. INFORMATION RETRIEVAL (Guest Lecture)
8. MACHINE LEARNING IN R
   • KNN CLASSIFICATION
   • NAIVE BAYES CLASSIFICATION
   • LOGISTIC REGRESSION
   • DIMENSIONALITY REDUCTION
   • ARTIFICIAL NEURAL NETWORKS
   • CLUSTERING AND K-MEANS
9. MACHINE LEARNING in PYTHON
   • NATURAL LANGUAGE PROCESSING
   • DECISION TREES AND RANDOM FORESTS
   • SUPPORT VECTOR MACHINES
   • ENSEMBLE METHODS
   • RECOMMENDATION SYSTEMS