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R Data Analysis Projects

  • Course Code: Data Analysisi / BI - R Data Analysis Projects
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Get valuable insights from your data by building data analysis systems from scratch with R.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level R Data Analysis Projects skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Get valuable insights from your data by building data analysis systems from scratch with R..  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This course will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The course covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this course, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle. 

Working in a hands-on learning environment, led by our R Data Analysis Projects expert instructor, students will learn about and explore: 

  • A handy guide to take your understanding of data analysis with R to the next level 
  • Real-world projects that focus on problems in finance, network analysis, social media, and more 
  • From data manipulation to analysis to visualization in R, this course will teach you everything you need to know about building end-to-end data analysis pipelines using R 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Build end-to-end predictive analytics systems in R 
  • Build an experimental design to gather your own data and conduct analysis 
  • Build a recommender system from scratch using different approaches 
  • Use and leverage RShiny to build reactive programming applications 
  • Build systems for varied domains including market research, network analysis, social media analysis, and more 
  • Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively 
  • Communicate modeling results using Shiny Dashboards 
  • Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Get valuable insights from your data by building data analysis systems from scratch with R. 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  

Course Agenda / Topics 

  1. Association Rule Mining 
  • Association Rule Mining 
  • Understanding the recommender systems 
  • Retailer use case and data 
  • Association rule mining 
  • The cross-selling campaign 
  • Weighted association rule mining 
  • Hyperlink-induced topic search (HITS) 
  • Negative association rules 
  • Rules visualization 
  • Wrapping up 
  1. Fuzzy Logic Induced Content-Based Recommendation 
  • Fuzzy Logic Induced Content-Based Recommendation 
  • Introducing content-based recommendation 
  • News aggregator use case and data 
  • Designing the content-based recommendation engine 
  • Complete R Code 
  1. Collaborative Filtering 
  • Collaborative Filtering 
  • Collaborative filtering 
  • Recommenderlab package 
  • Use case and data 
  • Designing and implementing collaborative filtering 
  • Complete R Code 
  1. Taming Time Series Data Using Deep Neural Networks 
  • Taming Time Series Data Using Deep Neural Networks 
  • Time series data 
  • Deep neural networks 
  • Introduction to the MXNet R package 
  • Symbolic programming in MXNet 
  • Training test split 
  • Complete R code 
  1. Twitter Text Sentiment Classification Using Kernel Density Estimates 
  • Twitter Text Sentiment Classification Using Kernel Density Estimates 
  • Kernel density estimation 
  • Twitter text 
  • Sentiment classification 
  • Dictionary based scoring 
  • Text pre-processing 
  • Building a sentiment classifier 
  • Assembling an RShiny application 
  • Complete R code 
  1. Record Linkage – Stochastic and Machine Learning Approaches 
  • Record Linkage – Stochastic and Machine Learning Approaches 
  • Introducing our use case 
  • Demonstrating the use of RecordLinkage package 
  • Stochastic record linkage 
  • Machine learning-based record linkage 
  • Building an RShiny application 
  • Complete R code 
  1. Streaming Data Clustering Analysis in R 
  • Streaming Data Clustering Analysis in R 
  • Streaming data and its challenges 
  • Introducing stream clustering 
  • Introducing the stream package 
  • Use case and data 
  • Complete R code 
  1. Analyze and Understand Networks Using R 
  • Analyze and Understand Networks Using R 
  • Graphs in R 
  • Use case and data 
  • Data preparation 
  • Product network analysis 
  • Building a RShiny application 
  • The complete R script 
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