Starting Date 1/20/2018
Complete Date 3/10/2018 (45 hours)
Lecturing time Saturday 1:00 pm to 5:00 pm
Friday 7:00 pm to 9:00 pm
Place 1601 McCarthy Blvd., Milpitas, CA 95035

A. COURSE DESCRIPTION This course introduces to you the latest analytical concepts, tools and methods in data mining, statistics and machine learning used to solve critical business problems in an organization. In this course, you will learn to identify, evaluate, and capture business analytic opportunities that create value. You will also learn how to transform data into deep business insights and actionable business strategy. This is a very practical course that focus on real business cases and examples, based on the actual working experience of the instructor as a marketing data science director.

At the end of the course, you will gain a holistic view of common analytical problems in the key functional areas of an organization, including but not limit to product, operations, finance, sales and marketing management. You will know how to solve these business problems using the most effective tools and methods in data science. This course will position you as an analytical expert or leader in your organization who understands where and how to apply advanced analytics to create business value.

  • Help students to think critically about data and the business implications behind data.
  • Provide students with a holistic view of common analytical problems in an organization that are critical to business decision making.
  • Teach students the latest analytical concepts, methods and tools in business analytics, including but not limit to descriptive, predictive, and prescriptive methods, by employing both traditional and advanced models in data mining, machine learning and statistics.
  • Position students as an expert in business analytics who understand how to frame business problems into analytical problems, model and discover deep business insights, transform data into actionable business strategy, and identify analytical opportunities to create business value.

C. COURSE TOPICS The course will cover the following topics:

Module 1: Introduction to business analytics and their real world application

  • Business challenges and requirements on data analytics
  • Business analytics processes and project management

Module 2: Customer analytics

  • Segmentation
  • Customer life time value
  • Cross-sell/up-sell
  • Retention/Churn/Attrition

Module 3: Sales and Marketing analytics

  • Demand waterfall and lead funnel
  • Predictive lead scoring
  • Predictive campaign analytics

Module 4: Product / pricing analytics

  • Pricing analytics basics
  • Conjoint analysis

Module 5: Operations analytics

  • News vendor model
  • Queueing theory

Module 6: Supply chain analytics

  • Forecasting methods

Module 7: Financial analytics
Module 8: Digital / web analytics (optional)

About the Instructor Mr. Liao is an industry recognized thought leader in advanced marketing analytics and data science. He has over 20 years of experience in business analytics, enabling data-driven decision making for executive leadership teams in finance, operations, sales and marketing. Most recently, as the director of advanced marketing analytics at Citrix Inc., he manages a data science team which leverages the latest tools, algorithms and software in Big Data, predictive analytics, machine learning and data mining to discover deep insights about customer behavior and to accelerate revenue generation by optimizing go-to-market program mix and strategy. He designed and operationalized the first predictive lead scoring model at Citrix which optimized the revenue generation process that influences $3B sales pipeline. His work in marketing data science is recognized by Gartner, IDC, SiriusDecisions and Mr. Liao is an active lecturer and presenter in marketing analytics and data science conferences. He won Analytics50 award in 2016 and is a two-time SiriusDecisions marketing analytics award winner (in 2012 and 2017). Mr. Liao holds a MBA degree from the Haas School of Business at UC Berkeley and a bachelor’s degree from University of Science and Technology of China.