robinkalia/datamining290

Data Mining and Analytics in Intelligent Business Services, UC Berkeley School of Information

Data Mining 290

Description

Learn how to obtain, clean, visualize, understand, model, and predict the world around you using data. Grading will consist of homework (30%), a midterm (30%), and a project (40%).

Instructor

Jimmy Retzlaff <jretz@ischool>

GSI

Shreyas <shreyas@ischool>

Textbook

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques, Third Edition (3rd ed.). Morgan Kaufmann.

Course Discussion

Info 290T: Data Mining on Piazza

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Syllabus

DM[0-9]+ indicates chapters from the text, Data Mining.

Date	Readings	Slides	Homework / Project
Jan 23	Try Github ; A Taxonomy of Data Science	Class Intro ; Tools Intro by GUEST: Shreyas	Git Intro
Jan 30	DM1 ; The Yelp Factor: Are Consumer Reviews Good for Business?	Case Studies ; Obtaining Data	Obtain & Explore Data
Feb 6	DM2, DM3	Probability ; Preprocessing	Data Stats
Feb 13	DM4, Apache Hadoop: Petabytes and Terawatts (slides); mrjob docs (for homework)	Data Warehouse ; MapReduce	Project Details ; mrjob
Feb 20	DM8	Decision Trees; Naive Bayes	Gini Index
Feb 27	DM[9.1-9.3], 9.5 ; Understanding the Bias-Variance Tradeoff	SVM ; Neural Networks	Neural Network Back Propagation
Mar 6	DM10	Clustering - Partitioning ; Clustering - Hierarchical & Density	K-Means
Mar 13	DM11.1	Review	prepare 1 cheat sheet
Mar 20	1 cheat sheet	Midterm
Mar 27	HOLIDAY
Apr 3	DM6	Advanced Clustering ; Frequent Patterns	AWS ; Project Proposal due April 9
Apr 10	DM11.3; PageRank; Uncovering Social Network Sybils in the Wild	Graphs; PageRank	Adjacency Representations
Apr 17	DM12; Shazam Audio Search	Outliers; Images & Audio	Midterm Review
Apr 24	Embedded Plots ; Data-Driven Documents	Visualization ; Yelp's Visualizations	D3 Intro; D3 Lab
May 1	A Few Useful Things to Know about Machine Learning ; Top 10 Algorithms in Data Mining	In Real Life	Project Data and Presentation due May 8th
May 8		Final Presentation	Project Code & Papers due May 14th
May 15			Bye!