This book covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single and short book. It does not cover everything, but instead, teaches the key concepts and topics. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source.
This book covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single and short book. It does not cover everything, but instead, teaches the key concepts and topics. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source.
Nailong Zhang is lead Data Scientist at Mass Mutual Life Insurance Company.
Inhaltsangabe
Assumptions about the reader's background Book overview Introduction to R/Python Programming Calculator Variable and Type Functions Control flows Some built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategy Speed up with C/C++ in R/Python A first impression of functional programming Miscellaneous data.table and pandas SQL Get started with data.table and pandas Indexing & selecting data Add/Remove/Update Group by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence interval Hypothesis testing Basics of linear regression Ridge regression Optimization in Practice Convexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous
Assumptions about the reader's background Book overview Introduction to R/Python Programming Calculator Variable and Type Functions Control flows Some built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategy Speed up with C/C++ in R/Python A first impression of functional programming Miscellaneous data.table and pandas SQL Get started with data.table and pandas Indexing & selecting data Add/Remove/Update Group by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence interval Hypothesis testing Basics of linear regression Ridge regression Optimization in Practice Convexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous
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