A tour of data science: learn R and Python in parallel
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
2021
|
| Edition: | First edition |
| Series: | Chapman and Hall/CRC big data series
Data science series |
| Subjects: | |
| Online Access: | DE-863 DE-862 DE-91 DE-19 DE-739 Volltext |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (x, 206 Seiten) |
| ISBN: | 9781003020646 9781000215274 |
| DOI: | 10.1201/9781003020646 |
Staff View
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- 0.1 Assumptions About the Reader's Background -- 0.2 Book Overview -- Chapter 1 Introduction to R/Python Programming -- 1.1 Calculator -- 1.2 Variable and Type -- 1.3 Functions -- 1.4 Control Flows -- 1.5 Some Built-In Data Structures -- 1.6 Revisit of Variables -- 1.7 Object-Oriented Programming (OOP) In R/Python -- 1.8 Miscellaneous -- Chapter 2 More on R/Python Programming -- 2.1 Work with R/Python Scripts -- 2.2 Debugging in R/Python -- 2.3 Benchmarking -- 2.4 Vectorization -- 2.5 Embarrassingly Parallelism in R/Python -- 2.6 Evaluation Strategy -- 2.7 Speed Up With C/C++ in R/Python -- 2.8 A First Impression of Functional Programming -- 2.9 Miscellaneous -- Chapter 3 data. table and pandas -- 3.1 SQL -- 3.2 Get Started with data.table and Pandas -- 3.3 Indexing & -- Selecting Data -- 3.4 Add/Remove/Update -- 3.5 Group by -- 3.6 Join -- Chapter 4 Random Variables, Distributions & -- Linear Regression -- 4.1 A Refresher on Distributions -- 4.2 Inversion Sampling & -- Rejection Sampling -- 4.3 Joint Distribution & -- Copula -- 4.4 Fit a Distribution -- 4.5 Confidence Interval -- 4.6 Hypothesis Testing -- 4.7 Basics of Linear Regression -- 4.8 Ridge Regression -- Chapter 5 Optimization in Practice -- 5.1 Convexity -- 5.2 Gradient Descent -- 5.3 Root-Finding -- 5.4 General Purpose Minimization Tools in R/Python -- 5.5 Linear Programming -- 5.6 Miscellaneous -- Chapter 6 Machine Learning - A gentle introduction -- 6.1 Supervised Learning -- 6.2 Gradient Boosting Machine -- 6.3 Unsupervised Learning -- 6.4 Reinforcement Learning -- 6.5 Deep Q-Networks -- 6.6 Computational Differentiation -- 6.7 Miscellaneous -- Bibliography -- Index | |
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| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- 0.1 Assumptions About the Reader's Background -- 0.2 Book Overview -- Chapter 1 Introduction to R/Python Programming -- 1.1 Calculator -- 1.2 Variable and Type -- 1.3 Functions -- 1.4 Control Flows -- 1.5 Some Built-In Data Structures -- 1.6 Revisit of Variables -- 1.7 Object-Oriented Programming (OOP) In R/Python -- 1.8 Miscellaneous -- Chapter 2 More on R/Python Programming -- 2.1 Work with R/Python Scripts -- 2.2 Debugging in R/Python -- 2.3 Benchmarking -- 2.4 Vectorization -- 2.5 Embarrassingly Parallelism in R/Python -- 2.6 Evaluation Strategy -- 2.7 Speed Up With C/C++ in R/Python -- 2.8 A First Impression of Functional Programming -- 2.9 Miscellaneous -- Chapter 3 data. table and pandas -- 3.1 SQL -- 3.2 Get Started with data.table and Pandas -- 3.3 Indexing & -- Selecting Data -- 3.4 Add/Remove/Update -- 3.5 Group by -- 3.6 Join -- Chapter 4 Random Variables, Distributions & -- Linear Regression -- 4.1 A Refresher on Distributions -- 4.2 Inversion Sampling & -- Rejection Sampling -- 4.3 Joint Distribution & -- Copula -- 4.4 Fit a Distribution -- 4.5 Confidence Interval -- 4.6 Hypothesis Testing -- 4.7 Basics of Linear Regression -- 4.8 Ridge Regression -- Chapter 5 Optimization in Practice -- 5.1 Convexity -- 5.2 Gradient Descent -- 5.3 Root-Finding -- 5.4 General Purpose Minimization Tools in R/Python -- 5.5 Linear Programming -- 5.6 Miscellaneous -- Chapter 6 Machine Learning - A gentle introduction -- 6.1 Supervised Learning -- 6.2 Gradient Boosting Machine -- 6.3 Unsupervised Learning -- 6.4 Reinforcement Learning -- 6.5 Deep Q-Networks -- 6.6 Computational Differentiation -- 6.7 Miscellaneous -- Bibliography -- Index |
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| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.1201/9781003020646 |
| edition | First edition |
| format | Electronic eBook |
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| isbn | 9781003020646 9781000215274 |
| language | English |
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| spellingShingle | Zhang, Nailong A tour of data science learn R and Python in parallel Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- 0.1 Assumptions About the Reader's Background -- 0.2 Book Overview -- Chapter 1 Introduction to R/Python Programming -- 1.1 Calculator -- 1.2 Variable and Type -- 1.3 Functions -- 1.4 Control Flows -- 1.5 Some Built-In Data Structures -- 1.6 Revisit of Variables -- 1.7 Object-Oriented Programming (OOP) In R/Python -- 1.8 Miscellaneous -- Chapter 2 More on R/Python Programming -- 2.1 Work with R/Python Scripts -- 2.2 Debugging in R/Python -- 2.3 Benchmarking -- 2.4 Vectorization -- 2.5 Embarrassingly Parallelism in R/Python -- 2.6 Evaluation Strategy -- 2.7 Speed Up With C/C++ in R/Python -- 2.8 A First Impression of Functional Programming -- 2.9 Miscellaneous -- Chapter 3 data. table and pandas -- 3.1 SQL -- 3.2 Get Started with data.table and Pandas -- 3.3 Indexing & -- Selecting Data -- 3.4 Add/Remove/Update -- 3.5 Group by -- 3.6 Join -- Chapter 4 Random Variables, Distributions & -- Linear Regression -- 4.1 A Refresher on Distributions -- 4.2 Inversion Sampling & -- Rejection Sampling -- 4.3 Joint Distribution & -- Copula -- 4.4 Fit a Distribution -- 4.5 Confidence Interval -- 4.6 Hypothesis Testing -- 4.7 Basics of Linear Regression -- 4.8 Ridge Regression -- Chapter 5 Optimization in Practice -- 5.1 Convexity -- 5.2 Gradient Descent -- 5.3 Root-Finding -- 5.4 General Purpose Minimization Tools in R/Python -- 5.5 Linear Programming -- 5.6 Miscellaneous -- Chapter 6 Machine Learning - A gentle introduction -- 6.1 Supervised Learning -- 6.2 Gradient Boosting Machine -- 6.3 Unsupervised Learning -- 6.4 Reinforcement Learning -- 6.5 Deep Q-Networks -- 6.6 Computational Differentiation -- 6.7 Miscellaneous -- Bibliography -- Index Data mining.. R (Computer program language) Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd R Programm (DE-588)4705956-4 gnd Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4705956-4 (DE-588)1140936166 |
| title | A tour of data science learn R and Python in parallel |
| title_auth | A tour of data science learn R and Python in parallel |
| title_exact_search | A tour of data science learn R and Python in parallel |
| title_exact_search_txtP | A tour of data science learn R and Python in parallel |
| title_full | A tour of data science learn R and Python in parallel Nailong Zhang |
| title_fullStr | A tour of data science learn R and Python in parallel Nailong Zhang |
| title_full_unstemmed | A tour of data science learn R and Python in parallel Nailong Zhang |
| title_short | A tour of data science |
| title_sort | a tour of data science learn r and python in parallel |
| title_sub | learn R and Python in parallel |
| topic | Data mining.. R (Computer program language) Python (Computer program language) Python Programmiersprache (DE-588)4434275-5 gnd R Programm (DE-588)4705956-4 gnd Data Science (DE-588)1140936166 gnd |
| topic_facet | Data mining.. R (Computer program language) Python (Computer program language) Python Programmiersprache R Programm Data Science |
| url | https://doi.org/10.1201/9781003020646 |
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