Programming for Analytics - R
Objective:
The course will begin with an introductory course in R language and go on to cover the principles of programming and software development for analytics with the important attributes like scalability, reliability, accuracy and efficiency. Different essential libraries/ packages will be covered that will help participants to integrate multiple technologies, use and build models for analytics, and adapts to changing requirements.
Learning Outcomes:
1. Learn the basic as well as advanced features and applications of R
2. Get a deeper understanding of systematic and stepwise process of complex problem solving
3. Develop an ability to write effective and efficient algorithms and heuristics for problem solving
4. Learn to solve complex analytical and business problems using R
Day-wise Content (5 days):
Day 1:
- Introduction to Computer Science and algorithms; problem definition, determination, analysis, diagnosis, repair and de-duplication
- Introduction to R
- Introduction to IDEs (RStudio)
- Working with data types and data structures in R
- File processing in R
- Working with vectors, dataframes, arrays and lists
- Data manipulation in R
- Introduction to R packages
Day 2:
- Learning to work with dplyr
- Advanced functions in dplyr including pipes
- Data wrangling using dplyr
- Visualization in R using ggplot2
- Introduction to building web applications using shinyR
Day 3:
- Working with Tibbles
- Functions for Tibbles
- Functions in R
- R Markdown
- Understanding Model development using R
Day 4:
- Introduction to business analytics and its applications
- Introduction to various libraries and packages for business analytics
- Regression using R packages
- Classification using R packages
- Clustering using R packages
Day 5:
- Hands-on mini project/ case study to apply all the knowledge and skills gained in the program