Midterm Exam Review

General Information

• Date: 3/13/2014• Time: 11-12.20• Location: 101 Davis• Closed book, closed notes

Topics

• Doing data science text: Ch.2 – 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

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

Topics: Lin & Dyer’s text

• Hadoop: HDFS as in Chapter 2• MapReduce: MR data-flow including

combiners and partitioners• 2 questions

Bloomberg Tech Talk on ML

• Building Intelligent solution• See the presentation• Up to slide#16 (No NLP or MT)• 1 question

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?

Seating for the exam

• Question, space for answer format• Designated seating: Will let you know the plan