A Tour of Data Science: Learn R and Python in Parallel

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Table of Contents......Page 8
0.2 Book Overview......Page 10
1.1 Calculator......Page 12
1.2 Variable and Type......Page 14
1.3 Functions......Page 15
1.4 Control Flows......Page 17
1.5 Some Built-In Data Structures......Page 20
1.6 Revisit of Variables......Page 32
1.7 Object-Oriented Programming (OOP) In R/Python......Page 41
1.8 Miscellaneous......Page 46
2.1 Work with R/Python Scripts......Page 48
2.2 Debugging in R/Python......Page 51
2.3 Benchmarking......Page 58
2.4 Vectorization......Page 60
2.5 Embarrassingly Parallelism in R/Python......Page 68
2.6 Evaluation Strategy......Page 72
2.7 Speed Up With C/C++ in R/Python......Page 74
2.8 A First Impression of Functional Programming......Page 76
2.9 Miscellaneous......Page 80
3.1 SQL......Page 82
3.2 Get Started with data.table and Pandas......Page 85
3.3 Indexing & Selecting Data......Page 87
3.4 Add/Remove/Update......Page 97
3.5 Group by......Page 102
3.6 Join......Page 104
4.1 A Refresher on Distributions......Page 110
4.2 Inversion Sampling & Rejection Sampling......Page 113
4.3 Joint Distribution & Copula......Page 117
4.4 Fit a Distribution......Page 121
4.5 Confidence Interval......Page 122
4.6 Hypothesis Testing......Page 125
4.7 Basics of Linear Regression......Page 133
4.8 Ridge Regression......Page 138
5.1 Convexity......Page 144
5.2 Gradient Descent......Page 145
5.3 Root-Finding......Page 152
5.4 General Purpose Minimization Tools in R/Python......Page 154
5.5 Linear Programming......Page 160
5.6 Miscellaneous......Page 164
6.1 Supervised Learning......Page 172
6.2 Gradient Boosting Machine......Page 180
6.3 Unsupervised Learning......Page 199
6.4 Reinforcement Learning......Page 206
6.5 Deep Q-Networks......Page 208
6.6 Computational Differentiation......Page 211
6.7 Miscellaneous......Page 213
Bibliography......Page 214
Index......Page 216