## To-do-list

1. SQL Introduction to database

2. Book to read:

- Mixed effect model:
*Data analysis using regression and multilevel models* - Business questions:
*Data Science for Business: What you need to know about data mining and data-analytic thinking* - Brain teaser:
*A practical guide to quantitative finance interviews, Chapter two* - Machine Learning / Data Mining:
*Elements of Statistical Learning*

3. Learn Python

- Berkeley CS61A: option 1 (Fall 13) or option 2 (Summer 14)
- Online learning website: http://pythontutor.com/
- Python analytic packages: Ipython notebook, pandas, numpy, Problem Solving with Algorithms and Data Structures
- Book:
*Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython* - Twitters(#tobeadded): Continuum Analytics, Data Scientist at @tailwindapp

4. Optional

Big data and hoop: https://class.coursera.org/datasci-002/assignment

- map reduce abstraction
- Distributed File System
- map reduce process

Resume preparation

http://www.1point3acres.com/%E7%BE%8E%E5%9B%BD%E6%B1%82%E8%81%8C%E7%AE%80%E5%8E%86/

## Data Analysis

Here is a very good article talking about how to do modeling generally.

http://www.theanalysisfactor.com/13-steps-regression-anova/

Part 1 and 2 are of especially valuable, telling you how to do data analysis before modeling. I’ll write down my own thoughts about it later.

## Good Materials for Java Study

1. Berkeley CS 61B Data Structures

http://www.cs.berkeley.edu/~jrs/61b/

2. Introduction to Computer Science using Java, Bradley Kjell, Central Connecticut State University

http://chortle.ccsu.edu/java5/index.html#03

very introductory course

3. Udeny: Java for Complete Beginners

https://www.udemy.com/java-tutorial/#/

4. Columbia University: Data Structure