CMSC 491/691, Introduction to Data Science [Fall 2017]

CMSC 491/691, Introduction to Data Science. Fall 2017
Computer Science & Electrical Engineering Department
University of Maryland Baltimore County

Course References

Books
- Data Mining: Concepts and Techniques by J. Han, M. Kamber, and J. Pei (3rd edition, 2011). Alias: DMCT11
- Mining of Massive Data Sets Jure Leskovec, Anand Rajaraman, and Jeff Ullman (2014). Alias: MMDS14
- Introduction to Statistical Learning with Applications in R by G. James, D. Witten, T. Hastie and R. Tibshirani (2013). Alias: ISLA13
  Slides and videos are also available.
- The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman (2009). Alias: ESL09
- A First Course in Design and Analysis of Experiment
- Doing Data Science by Cathy O'Neil and Rachel Schutt (2013).
- Data Science from Scratch First Principles with Python. by Joel Grus (2015). Code samples
- Python for Data Analysis (Data Wrangling with Pandas, NumPy, and IPython) by Wes McKinney (2012).
- Interactive Data Visualization for the Web by S. Murray (2013).
Python and Software resources
- Virtual Box VM and Ubuntu Linux
- python
- Anaconda
- Jupyter
- scikit-learn (Machine Learning in Python)
- Statsmodels Statistics Python Library
- seaborn: statistical data visualization (Python Library)
- pandas Python Data Analysis Library
- matplotlib 2-d
- Installing Jupyter (IPython Notebook) on HDP 2.4
- Spark
  - HortonWorks Apache Hadoop Sandbox Virtual Machine
  - Training Apache Spark Essentials
Datasets

Readings Assignments

Please refer to the Schedule of Lectures

© Copyright 2017- Dr. K. Kalpakis