R Data Analysis With R - Step-By-Step Tutorial! 3-In-1

What you'll learn
Get to know a set of techniques for importing data, manipulating data, performing statistical analysis, and producing useful data synthesis.
Build decision tree model for classification and prediction
Understand time-series decomposition, forecasting, clustering, and classification.
Master essential text data visualization with R.
Carry out cluster analysis using visualization methods such as Dendrogram and Silhouette plots.
Delve into network analysis of tweets with R.
Perform density-based clustering and clustering of tweets.
Requirements
Prior basic understanding R programming language will be useful.
Description
Are you looking forward to get well versed with classifying and clustering data with R? Then this is the perfect course for you!There's an increase in the number of data being produced every day which has led to the demand for skilled professionals who can analyze these data and make decisions. R is a programming language and environment used in statistical computing, data analytics and scientific research. Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years. This comprehensive 3-in-1 course takes a practical and incremental approach. Analyze and manage large volumes of data using advanced techniques. Attain a greater understanding of the fundamentals of applied statistics. Load, manipulate, and analyze data from different sources! Develop decision tree model for classification and prediction. Know how to use hierarchical cluster analysis using visualization methods such as Dendrogram and Silhouette plots!Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learn R programming, covers R programming to create data structures and perform extensive statistical data analysis and synthesis. You'll work with powerful R tools and techniques. Boost your productivity with the most popular R packages and tackle data structures such as matrices, lists, and factors. Create vectors, handle variables, and perform other core functions. You'll be able to tackle issues with data input/output and will learn to work with strings and dates. Explore more advanced concepts such as metaprogramming with R and functional programming. Finally, you'll learn to tackle issues while working with databases and data manipulation.The second course, Classifying and Clustering Data with R, covers classifying and clustering Data with R. This video course provides the steps you need to carry out classification and clustering with R/RStudio software. You'll understand hierarchical clustering, non-hierarchical clustering, density-based clustering, and clustering of tweets. It also provides steps to carry out classification using discriminant analysis and decision tree methods.In addition, we cover time-series decomposition, forecasting, clustering, and classification.By the end the course, you will be well-versed with clustering and classification using Cluster Analysis, Discriminant Analysis, Time-series Analysis, and decision trees.The third course, Bringing Order to Unstructured Data with R, covers obtaining, cleansing, and visualizing data with R. This video course will demonstrate the steps for analyzing unstructured data with the R/R Studio software. At the end the video course you'll have mastered obtaining and visualizing data with R. You'll also be confident with data cleaning, preparation, and sentiment analysis with R.By the end of the course, you'll be able to classify as well as cluster data and bring order to unstructured data with R.About the AuthorsDr. David Wilkins has been writing R for over a decade. He is the author of a number of popular open-source R packages, two previous Packt Publishing courses on the R language, and over a dozen scientific publications involving R analyses. He holds a Bachelor's degree in Science and a PhD in molecular genetics. David has a particular passion for creating beautiful and informative statistical graphics, and enjoys teaching people to use R to find and express insights in their own datasets.Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit. His two master's degrees include specializations in quality, reliability, and OR from Indian Statistical Institute and another in statistics from Meerut University, India. He teaches courses on topics such as Analyzing Big Data, Business Analytics and Data Mining, Twitter and Text Analytics, Applied Decision Techniques, Operations Management, and Data Science for Business. He has over twenty years' consulting and training experience, including industries such as automotive, cutting tool, electronics, food, software, chemical, defense, and so on, in the areas of SPC, design of experiments, quality engineering, problem solving tools, Six-Sigma, and QMS. His work experience includes extensive research experience over five years at Ford in the areas of quality, reliability, and six-sigma. His research publications include journals such as IEEE Transactions on Reliability, Reliability Engineering & System Safety, Quality Engineering, International Journal of Product Development, International Journal of Business Excellence, and JSSSE. He has been keynote speaker at conferences and presented his research work at conferences such as SAE World Conference, INFORMS Annual Meetings, Industrial Engineering Research Conference, ASQs Annual Quality Congress, Taguchi's Robust Engineering Symposium, and Canadian RAMS. Dr. Rai has won awards for Excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics. He also received an Employee Recognition Award by FAIA for his Ph.D. dissertation in support of Ford Motor Company. He is certified as ISO 9000 lead assessor from British Standards Institute, ISO 14000 lead assessor from Marsden Environmental International, and Six Sigma Black Belt from ASQ.
Overview
Section 1: Learn R programming
Lecture 1 The Course Overview
Lecture 2 Setting Up RStudio
Lecture 3 Writing, Running, and Saving R Scripts
Lecture 4 Exploring Numbers and Arithmetic Operators
Lecture 5 Working with Variables and Vectors
Lecture 6 Using Functions and Reading Function Documentation
Lecture 7 Exploring Vectors in Depth and Understanding Data Types
Lecture 8 Working with Matrices and Arrays
Lecture 9 Discovering Lists
Lecture 10 Discovering Data Frames
Lecture 11 Exploring Factors
Lecture 12 Reading Data from a File
Lecture 13 Subsetting Data Frames
Lecture 14 Statistical Summaries of Data
Lecture 15 Statistical Tests on Data
Lecture 16 Manipulating Data
Lecture 17 Writing Data to File
Section 2: Classifying and Clustering Data with R
Lecture 18 The Course Overview
Lecture 19 Iris Data
Lecture 20 Hierarchical Clustering Using Dendrogram with R
Lecture 21 Nonhierarchical K-means Clustering with R
Lecture 22 Preparing Data and Packages for Density-based Clustering
Lecture 23 Density-based Clustering with R
Lecture 24 Text Data Preparation for Clustering
Lecture 25 Clustering Words or Tweets with R
Lecture 26 Discriminant Analysis with R
Lecture 27 Model Interpretation
Lecture 28 Visualization
Lecture 29 Model Assessment
Lecture 30 Time Series Decomposition with R
Lecture 31 Time Series Forecasting with R
Lecture 32 Time Series Clustering with R
Lecture 33 Time Series Classification with R
Lecture 34 Decision Tree with R
Lecture 35 Visualization of Decision Trees
Lecture 36 Prediction and Misclassification Errors
Section 3: Bringing Order to Unstructured Data with R
Lecture 37 The Course Overview
Lecture 38 Obtaining Twitter Data Using R
Lecture 39 Data Cleaning and Preparation with R
Lecture 40 Visualization of Text Data with R
Lecture 41 Sentiment Analysis with R
Lecture 42 Network Analysis of Tweets with R
Lecture 43 Visualization and Interpretation – One
Lecture 44 Visualization and Interpretation – Two
Data scientist or a data analyst who want to master the art of Data Analysis and Statistics using the R programming language.


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