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After the completion of ‘Business Analytics with R’ at Edureka, you should be able to:
Business Analytics with R at Edureka will prepare you to:
edureka! has done an excellent job in teaching us R! I thoroughly enjoyed the classes, these classes are completely different than routine classroom sessions in a good way. After attending these classes, you will realize the big difference between learning directly from an industry expert and learning from an Instructor with no or negligible industry experience. I am looking forward to do my project and certification exam post my project submission. Many thanks to R instructor and edureka!
Very good and well presented R course. Trainer has a personable style reassuring online participants to learn well which is unique. This helps to learn quickly. Keep it up. Support staff, thanks for your unstinting 24*7 support. Overall very well executed classes.
Program Manager, SAP Supply Chain, Functional Expert at Tech Mahindra
Instructor very kindly replied to our questions with real time scenarios. This actually helped us to understand the concepts quickly.
Detailed discussion on the topic in the virtual classes, which helped in developing the basics correctly. All this with awsome 24*7 support. Great work edureka!
Senior Consultant with Bristlecone, New Delhi Area, India Connect at linkedIn
Great Session. The class went very smoothly as the instructor was very knowledgeable and insightful of the topic.
Abhishek Swarup ,
The instructor is very good, has good command on the subject, he interacted with all the attendees very well. I liked the session and I think it will be very helpful. Awsome Support!
Partha Borah ,
Class was good.
Ajit Sovani ,
Learning Objectives - At the end of this chapter, you will be able to install R and packages which are going to be used for the course. You will also learn how 'R' is being used in the industry (by Oracle, SAP, Google, Facebook, IBM and Revolution Analytics) and also get exposed to various GUI (like Rattle, Deducer, R Commander). You will learn how to use the IDE R Studio and various ways to use the R help (including calling up existing R Documentation, CRAN Views, and R Help Email lists). You will also be introduced to how the worldwide R community collaborates including the #rstats hashtag on Twitter, Stack Overflow, RBloggers, Conferences and using Github.
Topics - Business Problems, Data used by Various Business Domains (Telecom, Finance, Pharma, Retail, Online), Decision Management based on data, Spreadsheet vs Analytics, Type of Business Analytics including descriptive and predictive analytics, Installation of R, CRAN and Views, updating R, packages and dependencies, Github, assigning objects, using R as a calculator, functions, using help from within R, Email Groups, #rstats, Stack Overflow, Introduction to Deducer, Rattle, R Commander, Deducer Plugins, R Commander Extensions, R Studio, Revolution Analytics, Oracle R Enterprise, SAP Hana with R, IBM Netezza with R, R Community
Learning Objectives -At the end of this module, you will be able to import data from multiple formats into R and check it for accuracy. You will come across various steps for checking data quality and refining it. You will also learn how to import data from existing statistical formats like SPSS and SAS7BDAT. This is important as data quality can be a critical and time consuming part of an analytical project. You will also compare and contrast data import for command line versus Graphical User Interfaces.
Topics -Data import using text files, spreadsheets, databases, GUIs, APIs, web data, SAS and SPSS data formats using various R packages including str, names, plot, head, tail, sample, read, table functions and foreign, sas7bdat, RODBC packages, Introduction to using XML, RCurl and rjsonio packages.
Learning Objectives - At the end of this chapter, you will be able to create new datasets, new variables and create desired data shapes. You will come across the tremendous flexibility with which R can deal with data formats (lists, matrix, data frames) and variety (numeric, character, date time), and how to convert one data object to another.
Topics -Data manipulation to achieve desired quality and shape of data for analysis, apply functions, aggregate, reshape, is.NA, missing value treatment, creating new variables, subset, using square brackets for selection, conditional selection using AND and OR Conditions, using substr, gsub, difftime, cut functions, paste and as operator, Introduction to various R packages lubridate, stringr and plyr.
Learning Objectives -At the end of this chapter, you will be able to use R for basic analysis. The exploratory data analysis will look at functions and packages for numerical summary and analysis, and how to slice and dice data according to requirements. You will learn data analytics techniques including distribution analysis and understanding correlation between variables.
Topics -Structure of data object, aggregation and summary, exploring outliers, understanding the analytical approach, using summary, describe, mean, std, median, min, max, quartile, boxplot and hist functions, and Hmisc package.
Topics -Basics of Data Visualization and Visual Aesthetics, Different kinds of graphs (scatterplot, hexbin, lineplot, sunflowerplot, table plot, barplot, pie chart, heatmap, histogram with density, violin plots, adding rug to plots), customizing graphics including color palettes and R Color Brewer, using facets to slice and dice data, using basic and advanced R packages and GUI Deducer, Spatial Analysis, making a 3D plot in R Commander, Shiny Package and D3 Examples.
Learning Objectives -At the end of this module, you will be able to use R for data mining using various techniques including clustering, decision trees, ensemble models, association analysis using the GUI RATTLE. You will be exposed to examples from various data mining methodologies. The learner will also be introduced to SEMMA, CRISPDM and KDD concepts. Various clustering techniques including k means and hierarchical clustering will be learnt.
The learner will also be exposed briefly to neural networks and ensemble models.
Topics -Conceptual introduction to SEMMA, CRISPDM and KDD, Introduction to clustering (hierarchical, kmeans) and iterating for clusters, Introduction to data mining methods including SVM, decision trees, ensemble models, association analysis, neural nets, random forests using GUI Rattle.
Learning Objectives -At the end of this module, you will be able to use R for building regression models. The learner will learn how to check for heteroscedasticity and multicollinearity and treat the same. In addition, you will be exposed to splitting the modelling dataset into test, control and validation parts. You will learn how to build a model for predictive analytics and apply it for accurate results. This will also include testing the model for stability and statistical rigor including out of time validation.
Topics -Regression models using GUI Rattle, R Commander, car, gvlma, ROCR packages, p values, parameter estimates, Confusion matrix, Sensitivity, Specificity, Information Complexity, MultiCollinearity, Heteroscedasticity, Model Output, Lift Charts, Model Curves.
Learning Objectives -At the end of this module, you will be able to use R for text mining, time series forecasting, web analytics using Google Analytics, or explore Twitter or Facebook for analysis. This will expose the learners to techniques like web analytics for websites, predictive analytics for sales (or any time series data), social network analysis for relationships between entities, text mining for unstructured text data, and social media analytics for publicly available consumer data.
Topics -Forecasting, web analytics, social media analytics, text mining using various packages in R, epack plugin in R Commander, tm package, wordcloud package twitteR package, introduction to social network analysis and social media analytics.
US 1800 275 9730 (Toll Free)
India +91 88808 62004