Course Content: Practical Analytics with R


Classroom Training on 26-Dec-2015 @ IST 8 AM


Venue: Cenacle Research Office, Vijayawada.

Course Overview

Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. R is one of the prominent open-source statistical modelling tools these days.

A Hands-on Practical classroom training from Cenacle Research, for professionals working in data science and machine learning. This one day weekend training covers basics of analytics and jumps straight into the practical aspects of how to solve business problems with data science. Attendees will be working hands-on on analyzing large data sets with R, solving real-world business problems with machine learning techniques and learning to formulate production grade algorithms.

Who should attend this

Data scientists who are looking for gaining practical insight and first hand industry problem-solving experience. Previous experience of R or statistical background is NOT required.

Training outline:

  1. Practical Aspects of R

    • R Language basics
    • Data types and Vector operations
    • Difference between traditional programming & R programming
    • R Studio IDE
  2. Predictive Modelling

    • Supervised Learning
    • Unsupervised Learning
    • Clustering
    • Classification
  3. R for Analytics

    • Regression
    • K-Means
    • Decision Trees
    • Random Forests
    • Visualization methods
    • Time-series Analysis
    • Web scraping methods
    • Connecting Facebook to R
    • Text Processing Techniques
  4. Hands-on session: Working with Larget datasets in R

    • How to load and process large datasets (>1 GB) in R?
    • Techniques of large scale exploratory analysis
    • Dimensionality Reduction
    • Professional report generation
  5. Hands-on session: Real-world business case-studies

    • Customer Churn Analysis:
      • How to identify the customers who will leave your service
    • Market Basket Analysis:
      • What are the items customers are buying more frequently
    • Credit Risk Modelling:
      • How to identify the customer who are likely to default a bank loan?
    • Sentiment Analysis:
      • How customers are feeling about your brand in social media?


  • Attendees should bring their own Laptops (min 4GB RAM configuration)