Fundamentals of Analytics and R Programming

Program: Fundamentals of Analytics including Predictive & Big Data Analytics with ‘R’ Programming for Data Science & Analytics 

Program Fees: 44,000

Early Bird: ₹ 39,600 (after 10% discount )

Program Dates: To be announced ( 5 full days 9 am - 5:30 pm)

Program Venue: India ( further details to be sent out after registering interest)

Program Background & Overview

Analytics is an essential skill needed to run your business successfully. Common applications of analytics include the study of business data using mathematical & statistical analysis to discover and understand patterns which will help predict & improve business performance in the future. Analytics is an integral part of most businesses, and most successful entrepreneurs and business managers have generally been analysts in their own right, even if the analysis they have been doing has largely been intuitive.

It was projected, sometime during 2013, that 90% of the world's data had been created during the preceding two years going back from that date. However, many businesses still lack the ecosystem, talent & orientation to process and analyze all this data in order to gain competitive advantages and new insights about their markets and customers. With the right analytical tools and algorithms applied to the pertinent volumes of data, major insights can be gained and strategies devised, particularly in the domains of Marketing, Customer Services and HR, among others. The accuracy of answers, based on key queries posed by functional managers in these domains, would also improve significantly. Forecasts and projections, based on intuitive analysis, often no longer deliver the competitive edge necessary for businesses to stay ahead.

Analytics can be a rigorous and ever-evolving discipline and data scientists and analysts need to have a sound quantitative background and hands-on knowledge of the many tools, apps and algorithms needed to massage the relevant data and arrive at certain answers. However, it is up to the functional manager and professional to pose the ‘right questions’ and seek the relevant projections which will enable the business to keep its nose ahead of the competition.

Key Takeaways for Participants

  • Identifying and Framing the Analytical Problem: A proper quantitative analysis starts with recognizing a problem or decision and beginning to solve it. In decision analysis, this step is called framing.
  • Working with Quantitative People: Speaking of quantitative analysts, it’s really important for managers & functional professionals to establish a close working relationship with them. While you have the understanding of the business problem; your “quant” has the understanding of how to gather data on and analyze it.
  • Understanding Different Types of Data and Their Implications: These days, you’ll hear a lot about big data and how valuable it can be to your business. But most managers don’t really understand the difference between big and small data. 
  • Understanding Different Types of Analytics and Their Implications:. Predictive analytics use statistical models on data about the past to predict the future. Prescriptive analytics create recommendations for how workers can make decisions in their jobs. 
  • Exploring Internal and External Uses of Analytics: Managers & professionals need to be aware of the distinction between internal and external uses of analytics. While, historically, analytics were used almost exclusively to support internal decisions, presently, several companies are also using data and analytics to create new products and services.
  • Putting Analytics to Work Yourself: ‘R’ is arguably the most widely used programming language for Analytics. A knowledge of ‘R’ programming equips a manager or professional to write programs for some of the analytics required for his or her functional area, without having to depend on the organization’s tech. team or on purchasing & implementing expensive apps & tools for the job. 
Course Content Outline:
Fundamentals of Analytics (2 days)

Day 1 Session 1 - Metrics, Analytics, Sources and Types of Data

Evolution of Analytics

Big Data and Digital Technologies

Types and Sources of Data -- Internal & External, Digital, Transactional, Survey, Data Lakes, Structured and Unstructured Data

Importance of Metrics & Analytics for 21st Century Businesses - Digital, Social

Data for decision making and as a source of competitive advantage

Four V’s of data

Data Mining, Big Data and Predictive Analytics – Point of View

Day 1 Session 2 - Analytics, Big Data

Big Data -- Overview, History, Evolution, Applicability

Big Data – Big Picture – Hierarchies and Software Tools

Analytical and Software Platforms and Methodologies

Data Preparation – Determination of Time & Effort

Descriptive, Diagnostics, Predictive & Prescriptive Analytics - Overview, History, Evolution

Problem Definition and Key Business Questions

Strategy Alignment & asking the right questions

Case Study

Day 2 Session 1 – Problem Definition, Frameworks and Approaches

Overview of a typical Customer Data Base Analytical Project

Developing Predictive Algorithms – “Small or Little Data Analytics Sandbox”

Experimental Approaches and Analysis

Predictive Models – Classification vs. Prediction

Data Visualization

Natural Language Processing Applications

Social Media Analytics

Case Study

Day 2 Session 2 – Applications of Analytics across Industries, Ethics and Talent

Presentation of Insights and Story Telling

Detailed case study (1)

Detailed Case Study (2)

Cost and Time Parameters- In-house vs. Outsource - Key issues.

Group Project

Summing up; QnA

R Programming for Data Science & Analytics (3 days)

Day 3

Core Programming Principles

An Introduction to R

Fundamentals of R

Your first R session

Arithmetic with R



Day 4

Objects & Data Types

Vectors & Matrices

Data Frames



Conditions & Loops




Day 5

The basics of Graphics

Different plot types

Plot customization

Refresher on essential Statistics

Descriptive statistics using R