Learn R for Applied Statistics (eBook)
XV, 243 Seiten
Apress (Verlag)
978-1-4842-4200-1 (ISBN)
- Discover R, statistics, data science, data mining, and big data
- Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions
- Work with descriptive statistics
- Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots
- Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions
Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R's syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will LearnDiscover R, statistics, data science, data mining, and big dataMaster the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functionsWork with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplotsUse inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressionsWho This Book Is ForThose who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.
Eric Goh is a data scientist, software engineer, adjunct faculty and entrepreneur with years of experiences in multiple industries. His varied career includes data science, data and text mining, natural language processing, machine learning, intelligent system development, and engineering product design.Eric Goh has been leading his teams for various industrial projects, including the advanced product code classification system project which automates Singapore Custom’s trade facilitation process, and Nanyang Technological University's data science projects where he develop his own DSTK data science software. He has years of experience in C#, Java, C/C++, SPSS Statistics and Modeller, SAS Enterprise Miner, R, Python, Excel, Excel VBA and etc. He won Tan Kah Kee Young Inventors' Merit Award and Shortlisted Entry for TelR Data Mining Challenge. Eric Goh founded the SVBook website to offer affordable books, courses and software in data science and programming. He holds a Masters of Technology degree from the National University of Singapore, an Executive MBA degree from U21Global (currently GlobalNxt) and IGNOU, a Graduate Diploma in Mechatronics from A*STAR SIMTech (a national research institute located in Nanyang Technological University), and Coursera Specialization Certificate in Business Statistics and Analysis from Rice University. He possessed a Bachelor of Science degree in Computing from the University of Portsmouth after National Service. He is also a AIIM Certified Business Process Management Master (BPMM), GSTF certified Big Data Science Analyst (CBDSA), and IES Certified Lecturer.
Chapter 1: Introduction Chapter Goal: To understand what is R, why use R, statistics in data mining and data scienceNo of pages15Sub -Topics1.What is R?2.High Level and Low Level Language3.What is Statistics?4.What is Data Science?5.What is Data Mining?6.What is Text Mining?7.Three Types of Analytics8.Big Data9.Why R?10.ConclusionChapter 2: Getting StartedChapter Goal: To set up the computer for R ProgrammingNo of pages: 15Sub - Topics1. What is R and RStudio?2.Installation of R and RStudio3.Integrated Development Environment4. RStudio – The IDE for R. 5. ConclusionChapter 3: Basic SyntaxChapter Goal: To learn R programming basicsNo of pages : 30Sub - Topics: 1.Writing in R Console2.Using Code Editor3. Variables and Data Types4. Vectors5. Lists6. Data Frame7. Logical Statements8. Loops9. Functions10. ConclusionChapter 4: Descriptive StatisticsChapter Goal: To learn Descriptive Statistics in RNo of pages: 20Sub - Topics: 1. Reading Data Files2. Mean, Median, Min, Max, …3. Percentile, Standard Deviations4. The Summary() and Str() functions5. Distributions6.ConclusionChapter 5: Data VisualizationsChapter Goal: To learn Data Visualizations in R No of pages: 20Sub - Topics: 1.What is Data Visualizations?2.Bar Chart, Histogram3.Line Chart, Pie Chart4.Scatterplot and Box Plot5.Scatterplot Matrix6.Decision Trees7.ConclusionChapter 6: Inferential Statistics and RegressionsChapter Goal: To learn inferential statistics and regressions in RNo of pages: 20Sub - Topics: 1.Correlations2.T Test, Chi Square, ANOVA3.Non Parametric Test4.Linear Regressions5.Multiple Linear Regressions
| Erscheint lt. Verlag | 30.11.2018 |
|---|---|
| Zusatzinfo | XV, 243 p. 111 illus. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Theorie / Studium ► Compilerbau | |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| Schlagworte | data analytics • Data Exploration • Data Mining • Data Science • Data Vizualisation • machine learning • Natural Language Processing • R • Statistics |
| ISBN-10 | 1-4842-4200-9 / 1484242009 |
| ISBN-13 | 978-1-4842-4200-1 / 9781484242001 |
| Haben Sie eine Frage zum Produkt? |
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