S.#
Date
Day
Topics
Reading Material
1-2
13/02/14
Thursday
1. Course Overview
2. Chapter 9 (Data Mining with R)
3. ROC: Practical Consideration
4. HBR Article
5. Insight Fellowship Program Discussion
1.
3-4
20/2/2014
Thursday
1. Chapter 2 (Intelligent Data Analysis)
2. Chapter 5 (KNIME Cookbook)
3. Chapter 10 (Data Mining with R)
4. Hands-on Integration of KNIME with R

5-6
27/2/2014

1. Regression Analysis (Several youtube videos + slides)
2. Kaggle Challenges (Introduction)
3. KNIME Text Mining Hands-On

7-8
6/3/2014
Thursday
1. Logistic Regression (youtube videos + online material)
2. Kaggle Challenges (Presentation of initial findings)
3. Section 8.3 (Intelligent Data Analysis)




Midterm 1 Week

9-10
27/3/2014
Thursday
1. Discussion on KNIME Implementation of Text Aanlytics, Regression and Association Rule Mining
2. Introduction to Hadoop and MapReduce (Units 1-2 of UdaCity Course)

11-12
29/3/2014
Saturday
1. Modules 1 and 2 of Certification (Data Science & Big Data Analytics Fundamentals)

13-14
3/4/2014
Thursday
1. Time Series Analysis
2. Module 3 of EMC Certification (EDA using R)

15-16
5/4/2014
Saturday
1. Kaggle Challenge Discussion (Walmart Data)
2. Quick Overview of Python
3. Recap of the following topics for certification (Decision Trees, Naive Bayes, K-Means and Association Rules)

17-18
10/4/2014
Thursday
1. Recap of the following topics for certification (Regression Analysis, Logistic Regression, Time Series Analysis and Text Analytics)

19-20
12/4/2014
Saturday
1. Certification Lab

21-22
17/4/2014
Thursday
1. Kaggle Challenge Discussion (Walmart Data)
2. MapReduce programming (Units 3-4 of UdaCity Course)
3. EMC Practice Test


24/4/2014

Midterm 2 Week

23-24
15/5/2014

1. Data Streams Mining (Section 4.1, 4.2 and 4.3 of Mining of Massive Data Sets)
2. Link Analysis (Section 5.1 and 5.2 of Mining of Massive Data Sets)
3. Kaggle Challenge Discussion (AllState Data)

25-26
22/5/2014

1. Web Advertising (Section 8.1, 8.2 and 8.4 of Mining of Massive Data Sets)
2. Matrix/Vector + Database Operations in MapReduce (Chapter 2 of Mining of Massive Data Sets)

27-28
24/5/2014

1. Module 5 of EMC and Tutorials by Hortonworks (Pig, Hive, HBase)

29-30
29/5/2014

1. Recommendation Systems (Sections 9.1, 9.2 and 9.3 of Mining of Massive Data Sets)
2. Course Recap