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Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).
RSS public training courses - Royal Statistical Society
Basic Statistics: Understanding and Analysing Data (F) Introduction to R & Statistical Modelling in R (F). Programming in R (I). Advanced Programming in R (P). Introduction to Machine Learning in R (P) Interactive Visualisation (P). LEARNING PATH: R: Advanced Data Analysis with R | Udemy
Master R's advanced techniques to build data analysis systems from scratch. With its popularity as a statistical programming language rapidly increasing with Data visualization: This allows analyzing data from angles which are not clear in Machine learning: At some point in data science, a programmer may need to 1 Introduction - Introduction to Data Science
The book is divided into eight parts: R Basics, Data Visualization, Probability, Statistical Inference and Modeling, Data Wrangling, Regression, Machine Learning Python vs R for Artificial Intelligence, Machine Learning, and Data
This article covers Python vs R vs other languages for data science, machine learning, When I say 'learn', I mean learn fundamental programming concepts and control aggregating, visualizing, and performing exploratory data analysis (EDA). . analysis (EDA), statistical analysis, predictive analytics, machine learning, Advanced R Programming [Video] - O'Reilly Media
Selection from Advanced R Programming [Video] He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing Lesson 5: Network Analysis. Data Science (DATA_SCI) - University of Missouri
data analytics such as accessing, cleansing, modeling, visualizing, and interpreting data. Analysis methods include machine learning, network analysis, topic DATA_SCI 7640: R Statistical Programming Boot Camp. This course DATA_SCI 8001: Advanced Topics in Data Science and Analytics. Topics and credit may Advanced R Statistical Programming and Data Models: Analysis
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Carry out regression modeling using R data visualization, linear and Handle machine learning using R including parallel processing, dimension Advanced R Statistical Programming and Data Models by Matt Wiley
Advanced R Statistical Programming and Data Models: Analysis, Machine Carry out regression modeling using R data visualization, linear and advanced Handle machine learning using R including parallel processing, dimension Dataquest Content And Course Directory | Dataquest
Learn Python and R for data science. Learn by coding and working with data in your browser. Build your portfolio with projects and become a data scientist. Data Science Courses: R & Python Analysis Tutorials | DataCamp
Learn to train and assess models performing common machine learning tasks such as This course covers some advanced topics including strategies for handling large Apply statistical modeling in a real-life setting using logistic regression and Learn how to analyze and visualize network data in the R programming What Is Data Analysis and How Can You Start Learning It Today?
And how can you start a career in data analysis if you have no background in it? data science is typically more advanced (a lot more programming, . for developing statistical software, data analysis, machine learning and so on. Where to learn it: Applied Statistical Modeling for Data Analysis in R.
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