
Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics
Eric Goh Ming Hui
Résumé
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 Learn
- 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
Who This Book Is For
Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.
Chapter 2: Getting StartedChapter Goal: To set up the computer for R ProgrammingNo of pages: 15Sub - Topics 1. 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. Conclusion
Chapter 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. Conclusion
Chapter 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. Conclusion
Chapter 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
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.
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Apress |
Auteur(s) | Eric Goh Ming Hui |
Parution | 30/11/2018 |
Nb. de pages | 243 |
EAN13 | 9781484241998 |
Avantages Eyrolles.com
Consultez aussi
- Les meilleures ventes en Graphisme & Photo
- Les meilleures ventes en Informatique
- Les meilleures ventes en Construction
- Les meilleures ventes en Entreprise & Droit
- Les meilleures ventes en Sciences
- Les meilleures ventes en Littérature
- Les meilleures ventes en Arts & Loisirs
- Les meilleures ventes en Vie pratique
- Les meilleures ventes en Voyage et Tourisme
- Les meilleures ventes en BD et Jeunesse