python (I used python tutorial, details can check the book learning python, and then query some of the documents such as numpy, matplotlib official documents)

java (I first read the head first java, and then thinking in java read part)

linux shell (the more familiar the better, I just brush the bird the first part of the introductory book)

linux shell (the more familiar the better, I just brush the bird the first part of the introductory book).

java (I first read head first java, then thinking in java read part)

linux shell (the more familiar the better, I just brush the bird brother that the first half of the introductory book)

hadoop (need to be able to toss, in the Win computer is not good configuration, if the lab has an environment or someone to help bring with the introduction of the best.

2 Introduction to machine learning (ordered)

Collective programming wisdom (to brush up the examples, on the one hand, is to understand the introductory data mining, on the one hand, more familiar with python)

Introduction to Data Mining, Machine Learning (tom mitchell), Andrew Ng's Machine Learning course, Machine Learning Practical (mainly reference to the book) The code in the book is not very perfect, and is mainly used to get started).

These materials are recommended to choose one or two of the core coherent learning, the other can be referred to. For example, you first use the introduction to data mining to understand some of the basic concepts, with Andrew Ng's machine learning course for a more detailed study, in which to practice some of the algorithms can be referred to machine learning in action, some algorithms can not understand when you can refer to other books

Kaggle to find a few of the simplest questions for the introductory practice. (For example, Titanic that question)

Can be appropriate to understand some specific applications of machine learning, such as: recommender systems, image processing, voice or search. (Combined with their own interests and specialties to choose a certain in-depth study)

Pattern Recognition And Machine Learning,The Elements of Statistical Learning two very detailed theory of the tome, if you have the energy to be sure to look at. It is recommended to start from the first book (because I can only barely understand the first book, the second book, if other people say it is very classic, you can read it is certainly to see).

3 Data Structure Fundamentals

Introduction to Algorithms + leetcode online topics

Summary:

Read the book to more hands-on, more summarized, for example, looked at a simple Bayesian algorithm, it is best to summarize the method, and then write code to achieve a simple example. Refer to more books, refer to baidu

Additionally if you want to find a job must be more internships, as long as there is a good internship experience to find a good job the probability of greatly increased. From another perspective, don't put all the pressure on the school recruitment.