Our Philosophy. Authentic Contexts. Provide relevant and meaningful courseware to promote deeper understanding

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1 AcademyR Revolution Analytics partners with leading minds and industry experts to offer professional training courses designed to give your organization a quick start in building high performance analytical applications that leverage R for the enterprise. Experienced instructors work with you to customize courses with data and examples specific to your industry. AcademyR courses are available in classroom, virtual, self-paced and blended learning setups. One must learn by doing the thing, for though you think you know it you have no certainty until you try. -Sophocles, 450 BC Our Philosophy Impact Assessment Active Learning Fun Authentic Contexts Prior Knowledge Cooperation Goal-based Assess individual performance and overall training effectiveness Engage learners in the learning process Encourage participation in interesting and challenging activities Provide relevant and meaningful courseware to promote deeper understanding Recognize and build on prior experience and knowledge Encourage group learning Clear identification of learning goals Connect with us today at academyr@revolutionanalytics.com. We would love to hear from you!

2 Course Catalog Introduction to R Introductory Basics of R programming, data manipulation, graphics, and data analysis. For Data Analysts, Data scientists, Statisticians, Programmers etc. who require foundational knowledge in R. Course: T-IN-01 2 days Introduction to R as a Second Language Foundation for programming in R specifically for those familiar with statistical software such as SAS, SPSS or Stata. Knowledge of basic data analysis concepts is assumed. For Data Analysts, Data scientists, Statisticians, Programmers etc who are already familiar with another analytic platform. Course: T-IN-02 2 days Introduction to R Commander Learn to transition to R through a cross platform GUI. No need to remembering names and arguments of commands: avoid syntax and typing errors! For Statisticians and Data Analysts who usually work with a graphical user interface. Course: T-IN-03 Custom Course Delivery Formats Classroom Live Instructor led class at high tech facilities across the world or at your place of business. Learn from the renowned R experts. Private trainings can be tailored to your unique business needs Virtual Instructor Led Classroom Live virtual class delivered to your desktop over the Web using VOIP and hands-on labs. Live instruction with the same course content, class exercises and hands-on labs as classroom training without the need to travel. Ask questions and get answers real-time. Recordings are available for six weeks for participants to review. Blended Self-paced Web-based classes with assignments and content support. Learn at your own pace with content support (readings, homework) from leading R experts. Participants can ask questions and exchange comments with the instructor via a private discussion board during the course. Managing Data in R Most commonly used data management tasks in R using built in functions and latest packages. Course: T-IN-04 1/2 day Advanced Data Management in R Advanced methods for data import and export, data munging, and data manipulation. The course would go into more detail around accessing data stored in text files, web pages, and database management systems. Data manipulation, including string processing, date processing, methods for handling missing data, and methods of restructuring data (e.g., plyr, reshape2) are covered. Course: T-IN-05 2

3 Course Catalog Data Mining Data Mining with Rattle and R Transition from basic data mining, to sophisticated data analyses using a powerful statistical language, through the Rattle GUI. For Data Miners and Data Scientists. Course: T-DM-01 Custom Data Mining with Revolution R Enterprise Focus on data mining as an application area with R. Run predictive models on big data using Revolution R Enterprise. For Data Miners and Data Scientists. Course: T-DM-02 2 days Predictive Modeling and Data Mining with R Survey of algorithms and techniques for using R for predictive modeling and data mining, including linear regression and GLMs, decisions trees, neural nets, random forests, SVM, k-mean cluster analysis, association rules, and text mining. For Data Miners and Data Scientists. Course: T-DM-03 Advanced Data Mining with R Advanced data mining methods such as cluster analysis, tree based methods, support vector machines, neural networks. The course would also cover ROC curves and other methods for assessing the prediction accuracy. For Data Miners and Data Scientists. Course: T-DM-04 Predictive Modeling and Data Mining - Model Development A Tactical Drill-Down Of Process, Methods, Tools And Techniques. Covers data preparation, modeling and evaluation. Ideal for companies starting their analytics journey. For IT Professionals, Decision Support System Architects, Business Analysts. Course: T-DM-05 2 days Data Mining Predictive Modeling and Data Mining - Strategic Implementation A Case-Driven Workshop on to Discover What Really Works in data mining. Focuses on business understanding, data understanding and deployment. Ideal for companies starting their analytics journey. For Senior Management, Functional Managers, Project Managers. Course: T-DM-06 3 days Visualization and Reporting Data Visualization in R Learn how to build graphics in R using a range of packages - base R, lattice, ggplot2, and interactive graphics with iplots, googlevis, and RGGobi. For Data Miners, Data Scientists, Statisticians, BI developers Course: T-VZ-01 2 days Dynamic Reporting with R Methods for creating reports - web pages, interactive web pages, Microsoft Office documents (Word, Excel, PowerPoint), and publication quality (LaTex) reports with R. Covers knitr, markdown, R2wd, R2ppt, googlevis, shiny. For Data Miners, Data Scientists, Statisticians, BI developers Course: T-VZ-02 2 days Advanced Data Visualization in R Customizing graphs in R, with an emphasis on ggplot2 and lattice. For Data Miners, Data Scientists, Statisticians, BI developers Course: T-VZ-03 3

4 Course Catalog Data Science Statistical Hypothesis Testing Traverse the plethora of statistical tests and know when to use what. Covers T-tests, F-tests, Z-test, ANOVA, Chisquare tests, parametric tests, nonparametric tests. For Statisticians, Data Analysts. Course: T-DS-01 1/2 day Machine Learning Learn about state-of-the-art machine learning algorithms available directly in R, including hidden markov models, naive bayes, decision trees and random forests, support vector machines and more. For Data Scientists. Course: T-DS-02 Optimization and Mathematical Programming Different methods of optimization such as Linear Programming, Convex optimization - gradient descent, conjugate gradient; Constrained optimization - Lagrange multipliers. For Data Scientists, Operations Researchers. Course: T-DS-03 Linear Models, GLMs and GAMs Modeling and interpreting GLMs and GAMs with R. Topics include recent approaches and extensions. For Statisticians, Data Analysts. Course: T-DS-04 Multivariate Statistics Topics in multivariate statistics - MANOVA, factor analysis, principal components analysis, and discriminant analysis. For Statisticians, Data Analysts. Course: T-DS-05 State Space Models Analysis and forecasting of time series by state space methods using R with a focus on dlm package in R. For Forecasters, Planners, Econometricians. Course: T-DS-06 Custom Data Science Time Series Analysis Learn how to prepare, structure, analyze and model time series data as we discuss autocorrelation and autoregressive models, exponential smoothing and state-space models, as well as seasonality and trends. For Forecasters, Planners, Econometricians. Course: T-DS-07 2 days Survival Analysis Time to event models to predict when an event will occur or trying to infer why events occur. For Marketers, Clinical Researchers. Course: T-DS-08 Spatial Statistics Demystify the tools for spatial analysis in R, discussing data management and loading, transformation and reprojecting, as well as analysis, using spatial correlations and kriging models. For GIS Analysts/Researchers Course: T-DS-09 Ensemble Models Improve accuracy by combining prediction models. For Data Scientists, Data Miners. Course: T-DS-10 Social Network Analysis Quantitative and qualitative methods for describing, measuring and analyzing social networks. Learn to identify influential individuals, track the spread of information through networks, and solve real problems. For Researchers, Data Scientists. Course: T-DS-11 Monte Carlo Methods Basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis Hastings and Gibbs algorithms, and adaptive algorithms. For Researchers, Data Scientists. Course: T-DS-12 4

5 Course Catalog Business Applications Marketing Analytics with Revolution R Enterprise Key techniques used for direct marketing to analyze customer data. The course covers new customer acquisition, managing existing customers, and retention and churn. For Marketers, Product Managers. Course: T-BA-01 5 days Customer Analytics for Marketers Analyze and Model Customer Behavior to Improve Marketing Efficiency. No R knowledge required. For Marketers, Product Managers. Course: T-BA-02 2 days Consumer Choice Forecasting Application of discrete choice models (Logit, Probit, and McFadden s Logit) to forecast consumer behavior and choices. For Market Researchers. Course: T-BA hours Marketing Optimization Learn how to plan, prioritize and optimize marketing results to maximize profits. Avoid common problems like over- or under-contacting customers, budget overspending, etc. Explore what-if scenarios like impact of budget on revenue, impact of contact frequency on LTV etc. For Marketers, Product Managers. Course: T-BA-04 Business Applications Quantitative Risk Analysis with R Core principles of quantitative risk analysis and the most important risk modeling principles, methods and techniques. For Risk Management professionals. Course: T-BA-06 4 days Advanced Analytics for Insurance Advanced analytics for the Insurance industry, with examples drawn from non-life (property and casualty) insurance pricing. Covers both the technical analysis as well as business change and approaches to deployment. For Actuaries. Course: T-BA-07 5 days Financial Risk Modeling and Computation in R Introduction to financial risk modeling in R and using many-core and multi-core accelerator platforms for high performance computations. Covers parallel programming, Monte-Carlo simulation, time-series analysis and is directed at financial risk practitioners especially in the investment banking sector. For Quantitative Modelers. Course: T-BA-08 2 days Statistical Forecasting: Principles and Practice Methods and models of statistical forecasting of timeseries data. Covers seasonality and trends, exponential smoothing, ARIMA modelling, dynamic regression and state space models, as well as forecast accuracy methods and forecast evaluation techniques such as cross-validation. For Forecasters, Planners. Course: T-BA hours 5

6 Big Data Big Data Analytics with Revolution R Enterprise How Revolution R Enterprise addresses the biggest soft spot of R. For Data Analysts, Data scientists, Data Miners, Statisticians, R-Programmers. Course: T-BD-01 Parallel Computing with Revolution R Enterprise Techniques for parallel computing with R on computer clusters, multicore systems or grid computing and how Revolution R Enterprise makes parallel computing easy. For Data Analysts, Data scientists, Data Miners, Statisticians, R-Programmers. Course: T-BD-02 Introduction to Revolution R Enterprise in Hadoop Empower Hadoop analysts/developers with Revolution R Enterprise in Hadoop. For Data Analysts, Data scientists using Hadoop environment. Course: T-BD-03 Modeling in Revolution R Enterprise in Hadoop Advanced modeling techniques in Hadoop using Revolution R Enterprise. Covers decision trees and forests, general linear models and clustering. For Data scientists using Hadoop environment. Course: T-BD-04 Maximizing R and Hadoop Advanced use of mapreduce jobs in Hadoop using the RHadoop packages. For Data scientists using Hadoop environment. Course: T-BD-05 Text Analytics in R Text analytics and NLP in R. Structuring text and topic modeling algorithms. Course: T-BD-06 Programming Object Oriented Programming in R R as an object oriented programming language, emphasizing good programming practices and the design and development of clear, concise and efficient code. Course: T-PG-01 Advanced R Programming Advanced R programming topics such as efficient programming and memory management, advanced function writing topics, simulation, environments, and object oriented programming. Course: T-PG-02 Implementing Webservices using RevoDeployR Deploy R applications on a server for access by client applications through a web services API. Course: T-PG-03 Other Train the Trainer Program An add-on module to selected courses. Recommended for organizations with a large pool of analysts who need training on any of the above topics. Course: T-OT-01 Custom System Administration Training Hands on training for System administrators and others responsible for managing Revolution R Enterprise in production or development environments. For System Administrators. Course: T-OT-02 6

7 Courses in Partnership with statistics.com Bayesian Statistics in R Using the R-packages R-INLA and JAGS, specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data. Procedures covered from a Bayesian perspective include linear regression, poisson, logit and negative binomial regression, and ordinal regression. For Statisticians, Data Scientists, Data Miners. Course: T-ST-01 Bootstrap Methods Use the bootstrap procedure to assess bias and variance, test hypotheses, and produce confidence intervals. The bootstrap is illustrated also for regression and time series procedures. Basic and improved bootstrap procedures are covered. For Statisticians, Data Scientists, Data Miners. Course: T-ST-02 Biostatistics in R: Clinical Trial Applications Learn how to use R to compare treatments, incorporate covariates into the analysis, analyze survival (time-to-event) trials, model longitudinal data, and analysis of bioequivalence trials. Clinical Trial Statistician Course: T-ST-03 Data Mining in R - Learning with Case Studies Learn by doing data mining with a series of real world data mining case studies.. For Data Miners. Course: T-ST-04 Wrangling and Munging Data with SQL and R Learn how to extract data from a relational database using SQL, and then merge the data into a single file in R, so that you can perform statistical operations. Course: T-ST-05 Courses in Partnership with statistics.com Logistic Regression Covers the functional form of the logistic model and how to interpret model coefficients. Learn the concepts of odds and odds ratio, risk ratio etc. Course: T-ST-06 Mapping in R Manipulate, visualize and analyze spatial information and integrate with the data analysis process. For Data Analysts, Statisticians. Course: T-ST-07 Statistical Analysis of Microarray Data Learn the biology, statistical tools required for the analysis of microarray data, how to apply them using R software and how to interpret the results meaningfully. For Statisticians. Course: T-ST-08 Graphics in R Covers the core R graphics functions and the lattice package for producing plots and also looks at lower-level tools for customizing plots. For Statisticians, Data Scientists, Data Miners. Course: T-ST-09 Visualization in R with ggplot2 Use the ggplot R Project to make, format, label and adjust graphs using R. For Statisticians. Course: T-ST-10 7

8 Courses in Partnership with statistics.com Introduction to Smoothing and P-spline Techniques using R Learn how to use R software for data smoothing via P-splines - a combination of regression on a B-spline basis (basis splines) and difference penalties (on the B-spline coefficients). For Statisticians. Course: T-ST-11 Survey Analysis in R Design simple and complex surveys, produce descriptive statistics and graphs and conduct statistical analysis. For Statisticians. Course: T-ST-12 Spatial Analysis Techniques in R Introduce the use of R for geographic information analysis. Point Pattern Analysis, Area (lattice) objects and basics of Geostatistics. For GIS researchers. Course: T-ST-13 Introduction to R - Data Handling Enter, save, retrieve, manipulate, and summarize data using R. Build foundation to build your programming skills in R. For Data Analysts, Data Miners, Data Scientists, Statisticians. Course: T-ST-14 Introduction to R - Statistical Analysis Use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression. For Data Miners, Data Scientists, Statisticians. Course: T-ST-15 Courses in Partnership with statistics.com Modeling in R Use R to build statistical models and use them to analyze data. Covers multiple regression, logistic regression, GLM, poisson regression. Analyze longitudinal data using graphics and simple inferential approaches. Describe mixedeffects models and the generalized estimating approach for such data. For Data Miners, Data Scientists, Statisticians. Course: T-ST-16 R Programming - Introduction Covers simple arithmetic, vector operations, writing functions, the role of user-created packages, logical operations, working with text data, categorical data, time/ date data, and data frames. Course: T-ST-17 R Programming - Intermediate Learn R as a programming language, emphasizing good programming practices and the development of clear, concise code. Course: T-ST-18 R-Programming - Advanced Key concepts for writing advanced R code, emphasizing the design of functional and efficient code. It will set students down the road to mastering the intricacies of R. Course: T-ST-19 To register for our public courses, go to Contact us to arrange for a private training at your work location at academyr@revolutionanalytics.com 8