1. Midterm Exam Review

2. General Information Date: 3/13/2014 Time: 11-12.20 Location: 101 Davis
Closed book, closed notes

3. Topics Doing data science text: Ch.2 One question
Statistical inference, exploratory data analysis, and data science process
Population and samples, sample sizes
Data model
Statistical model
Algorithms
Fitting a model
Probability distributions
EDA: plots, graphs and summaries
One question

4. Topics (contd.) Doing data science: Ch. 3
Comparison of algorithms and stat models
Three basic algorithms
Linear regression
K-NN (semi-supervised.. Classification)
K-means (unsupervised clustering)
Intuitive idea
Algorithmic steps for each of these algorithms
Representative examples
Why and when would you use each of these algorithms?
2 questions

5. Topics: Lin & Dyer’s text
Hadoop: HDFS as in Chapter 2
MapReduce: MR data-flow including combiners and partitioners
2 questions

6. Bloomberg Tech Talk on ML
Building Intelligent solution
See the presentation
Up to slide#16 (No NLP or MT)
1 question

7. Format 5 questions not equally weighed HDFS: direct Ch.2 dds: direct
MR and K-NN: little tricky
K-means: direct
Questions will test your understanding of the concepts
Example: what is the effect of large K vs smaller K in K-NN?

8. Seating for the exam Question, space for answer format
Designated seating: Will let you know the plan