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)
- 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.
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 |
Day 3 Core Programming Principles An Introduction to R Fundamentals of R Your first R session Arithmetic with R Tutorial Quiz |
Day 4 Objects & Data Types Vectors & Matrices Data Frames Factors Lists Conditions & Loops Functions Tutorial Assignment |
Day 5 The basics of Graphics Different plot types Plot customization Refresher on essential Statistics Descriptive statistics using R Quiz Project |