Resources

Check out MadDSI, a data science initiative from our group.

Python

To learn Python, we recommend the following books:

• • Introducing Python, by Bill Lubanovic

  • Learning Python, by Mark Lutz

The Python tutorial at https://docs.python.org/2/tutorial/ is very useful.

Machine Learning

• • I will cover basic ML stuff in the class, will emphasize a bit the end-to-end process. That will be enough to get you going, but you should read more. I've posted some reading material for ML on the class's internal page.

  • There are a lot of ML books, but many of these are written from statistical perspectives and may not be easy for novice data scientists to read. The book Machine Learning by Tom Mitchell, while a bit outdated, is still the most accessible. You can probably buy a used copy on Amazon (far cheaper than a new one). Scan Chapter 1, skip Chapter 2, read Chapters 3 and 5, and read whichever remaining chapters you may like. Some other choices include:

  • Introduction to Machine Learning

  • Data Mining: Practical Machine Learning Tools and Techniques

  • This book also has a good introductory chapter on machine learning (Chapter 18).

  • You should also browse the slides on the homepage of CS 760: Machine Learning. Here's the course offering from David Page, and here's the course offering from Mark Craven.

  • If you want ML books from statistical perspectives, here are a few recommendations:

  • Pattern Recognition and Machine Learning

  • Pattern Recognition and Neural Networks

  • Machine Learning: A Probabilistic Perspective

Relational Database Systems, SQL

Any of the following textbooks will do.

• • Database Systems: The Complete Book

• Database System Concepts

• Database Management Systems

Tools in the Python Data Science Ecosystem

There are numerous tutorials/books/articles about these (just do a quick Google search). A few pointers:

• • A recent tutorial on scikit-learn, given at the PyData conference in Chicago.

  • A recent tutorial on pandas, given at the PyData conference in Chicago

  • This is a good book to learn more about pandas: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

  • You should also know GitHub and Jupyter Notebook.

  • Resources on Jupyter Notebook

  • https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/index.html

  • http://bebi103.caltech.edu/2015/tutorials/t0b_intro_to_jupyter_notebooks.htm

  • http://www.randalolson.com/2012/05/12/a-short-demo-on-how-to-use-ipython-notebook-as-a-research-notebook/

  • http://cs231n.github.io/ipython-tutorial/