| 000 | 02711cam a2200337 i 4500 | ||
|---|---|---|---|
| 003 | CUTN | ||
| 005 | 20241001165220.0 | ||
| 008 | 200602s2021 enka b 001 0 eng | ||
| 020 | _a9780367897062 | ||
| 020 | _a9780367895860 | ||
| 020 | _z9781003020646 | ||
| 041 | _aEnglish | ||
| 042 | _apcc | ||
| 082 | 0 | 0 |
_a006.312 _223 _bZHA |
| 100 | 1 | _aZhang, Nailong, | |
| 100 | 1 | _eauthor. | |
| 245 | 1 | 2 |
_aA tour of data science : _blearn R and Python in parallel / _cNailong Zhang. |
| 250 | _aFirst edition. | ||
| 260 |
_aAbingdon, Oxon : _bCRC Press, _c2021. |
||
| 300 |
_ax, 206 pages : _billustrations ; _c26 cm |
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| 490 | 0 | _aChapman & Hall/CRC data science series | |
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | _tAssumptions about the readers 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 | _a"A 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"-- | ||
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aR (Computer program language) | |
| 650 | 0 | _aPython (Computer program language) | |
| 906 |
_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg |
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| 942 |
_2ddc _cBOOKS |
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| 999 |
_c43671 _d43671 |
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