Quantitative Data Analytics & Its Applications in Business

Course details

Who must attend?

• Decision makers who are extensive users of business information/research and /or, would like to develop processes within their organization that provide relevant marketing and financial analysis / research for decision making are encouraged to participate. All senior level research and analytics managers who are entrusted in the organization with the responsibility of ensuring quality business information management and analysis for the purpose of actively supporting business decision making will benefit. Some prior exposure to mining and/or analysis of business data will be useful (albeit not absolutely necessary) towards appreciating the inputs from the program. While not mandatory, some basic knowledge of statistics will be an advantage.
• While the list is certainly not comprehensive, for the sake of illustration, organizations in the FMCG, consumer durables, consumer financial services, insurance, banking, retailing, etc. will find this program very relevant.

Why should you attend?

Participants will benefit from this program if they encounter some or any of the following situation(s) at work:

• If you are interested in understanding financial modeling and risks management using fat tailed distributions and copula
• If you are interested in evaluating risks using simulations in financial and insurance applications
• If you are concerned about how to use customer and competitor information to effectively drive your marketing initiatives and would like to evolve an internal process within your organization to do so.
• If you are interested in issues such as, a) identifying customer segments from data, b) measuring effectiveness of your marketing initiatives, c) perceptions of consumers regarding product offerings, d) appreciating demand projections, e) optimizing communications budget, f) estimating/forecasting impacts of alternative marketing plans, etc., through a process of collection and analysis of relevant data.
• If you are interested in redesigning your ongoing research to make them more useful for business decision-making.
• If you want to develop/refresh your understanding about some relevant data analytic tools and their applications.

Objectives

• The objective of the program is to provide a framework for participants to process and manage information that can be productively used for decision making purposes in (though not exclusively to) marketing and finance.
• The first phase of the program will focus on providing an overview of the generic statistical concepts. The second phase will be devoted to building some appreciation for analytical tools that can be used for processing business information arising out in marketing, finance, banking and insurance applications. The last phase of the program will be devoted to sharing some best management practices and initiating general guidelines and rules for building effective analytical processes in organizations
• The emphasis of the program will be more on development of data analytics - research appreciation among practitioners. While knowledge of specific statistical tools will be disseminated as a part of the overall objective of the program, it will not attempt to be at a specialist level.

Content

The major topics to be covered (tentatively) in this programme are:

• An overview of basic statistical tools used for processing business information:
• Summarization of Data Using Tables, Graphs and Charts
• Descriptive Measures, Standard Probability Distributions,
• Confidence Intervals, Testing of Statistical Hypotheses, Multiple Regression Analysis

Standard Data Analytic Tools:

• Factor Analysis, Cluster Analysis
• Applications of Factor Analysis, Cluster Analysis
• Discriminate Analysis and its Applications
• Choice Models and its Applications

Financial Models and Risks Management:

• Financial Models Using Fat tailed Marginals & Copulas
• Monte Carlo Based Evaluation of Value at Risk

Some Applications:

• Conjoint Analysis in Competitive Pricing
• Segmentation Using CART
• Practices from Industry Cases