Analytics: Putting It All to Work

  • Business analysts
  • Senior data analysts
  • Quantitative analysts
  • Data miners
  • Senior CRM analysts
  • Marketing analysts
  • Risk analysts
  • Analytical model developers
  • Online marketers
  • Marketing modelers in the banking, finance, insurance, Telco, online retailers, advertising, and the pharmaceutical industries

Prerequisite
Please contact us for information about prerequisites.

Expected Duration
2 day

Description

Many companies are flooded with huge amounts of data available in corporate databases and/or data warehouses. A key challenge is how to optimally manage this data overload and use analytics to better understand, manage, and strategically exploit the complex dynamics of customer behavior.

In this course, you will learn about the steps involved when working out an analytics project in a practical business setting. Additionally, you will learn about the key data preprocessing activities as well as how you can efficiently use and deploy both predictive and descriptive state-of-the-art analytics to optimize and streamline your strategic business processes such as marketing campaigns and risk management. Examples of business applications that are covered include credit scoring and risk modeling, customer retention and response modeling, market basket analysis and cross-selling, customer lifetime value modeling, as well as web intelligence and social network analytics. You will receive extensive practical advice and guidelines on how to put all the analytical tools and concepts to work in a real-life setting. The class focuses on analytical concepts, techniques, and methodologies and their applications. Software demonstrations illustrate and clarify the concepts, but no hands-on use of software is included. The class includes self-study sections with additional real-life case studies.

Objective

1. Introduction

  • Examples of business analytics
  • The analytics process model
  • Predictive vs. descriptive analytics
  • Analytics model requirements
  • Post processing

2. Data Collection, Sampling, and Preprocessing

  • Types of data sources
  • Sampling
  • Missing values
  • Outlier detection and treatment
  • Categorization
  • Weights of evidence coding
  • Information value

3. Predictive Analytics

  • Target definition
  • Regression
  • Logistic regression
  • Decision trees
  • Regression trees
  • Evaluating classification models
  • ROC analysis
  • Lift curve
  • Regression diagnostics
  • Churn prediction in a telco context (case study)

4. Descriptive Analytics

  • Association rules (support, confidence, a priori, interestingness, and so on)
  • Cross selling and market basket analysis
  • Recommender systems
  • Sequence analysis
  • Segmentation
  • Hierarchical vs. non-hierarchical (for example, k-means) clustering

5. Social Network Analytics

  • Social network applications
  • Social network metrics
  • Social network-based inference
  • Markov property
  • Relational logistic regression

6. Putting Analytics to Work

  • Analytics model requirements
  • Model interpretation
  • Monitoring analytical models
  • Backtesting
  • Benchmarking
  • Data quality
  • Corporate governance and management oversight

SUBSCRIPTION COST


$850.00

 

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