Thursday, August 15, 2013

Designing a Business Analytics program, Part 2: Content

This post follows Part 1: Intro of Designing a Business Analytics program. In this post, I focus on the content to be covered in the program, in the form of courses and projects.

The following design is based on my research of many programs, on discussions with faculty in various analytics areas, with analysts and managers at different levels, and on feedback from many past MBA students who have taken my analytics courses over the years (data mining, forecasting, visualization, statistics, etc.) and are now managing data at a broad range of companies and organizations.

Dealing with data, little or mountains, and being able to tackle an array of business challenges and opportunities, requires a broad and diverse set of tools and approaches. From data access and management to modeling, assessment and deployment requires a skill set that derives from the fields of statistics, computer science, operations research, and more. In addition, one needs integrative and "big picture" thinking and effective communication skills. Here is a list of 16 courses, divided into four sets, that attempts to achieve such a skill set (by no means is this the only set - would love to hear comments):

Set I
  1. Analytic Thinking (what is a model? what is the role of a model? data in context and data-domain integration)
  2. Data Visualization (data exploration, interactive visualization, charts and dashboards, data presentation and effective communication, use of BI tools)
  3. Statistical Analysis 1: Estimation and inference (observational studies and experiments; estimating population means, proportions, and more; testing hypotheses regarding population numbers; using programming and menu-driven software)
  4. Statistical Analysis 2: Regression models (linear, logistic, ANOVA)
Set II
  1. Data Management 1: Database design and implementation, data warehousing
  2. Forecasting Analytics: Exploring and modeling time series
  3. Data Management 2: Big Data (Hadoop-MapReduce and more)
  4. Operations 1: Simulation (principles of simulation; Monte Carlo and Discrete Event simulation)
  1. Operations 2: Optimization (optimization techniques, sensitivity analysis, and more)
  2. Statistical Analysis 3: Advanced statistical models (censoring and truncation, modeling count data, handling missing values, design of experiments (A/B testing and beyond))
  3. Data Collection (Web data collection, online surveys, experiments)
  4. Data Mining 1: Supervised Learning - Predictive Analytics (predictive algorithms, evaluating predictive power, using software)
Set IV
  1. Data Mining 2: Unsupervised Learning (dimension reduction, clustering, association rules, recommender systems)
  2. Contemporary Analytics 1 (choose between: text mining, network analytics, social analytics, customer analytics, web analytics, risk analytics)
  3. Contemporary Analytics 2 (from the list above)
  4. Integrative Thinking (BA in different fields, choosing and integrating tools and analytic approaches into an effective solution)
The courses are divided into sets of four, where courses in each set can be offered in parallel. The order should take into account coverage of other courses and natural linkages.

Lastly: two industry team projects that require integrating skills from multiple courses should give participants the opportunity to interface with industry, test their skills in a more realistic setting, and gain initial experience and confidence to move forward on their own.

Continue to Part 3: Structure

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