000			02922nam a22002177a 4500
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008			230113b ||||| |||| 00| 0 eng d
020			_a9780367895860
082			_a006.312 _bZHA
100			_a Zhang, Nailong _910484
245			_aA tour of data science: _blearn R and Python in parallel
260			_bCRC Press _aBoco Raton _c2021
300			_ax, 206 p.
365			_aGBP _b44.99
504			_aTable of Contents 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
520			_aA Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.
650			_aPython (Computer program language) _911337
650			_aR (Computer program language) _91512
650			_aData mining _9365
942			_2ddc _cBK