Are you looking forward to get well versed with classifying and clustering data with R? Then this is the perfect course for you! Theres 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. Youll 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. Youll 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, youll 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. Youll 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 youll have mastered obtaining and visualizing data with R. Youll also be confident with data cleaning, preparation, and sentiment analysis with R.By the end of the course, youll 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.
