UCLA EXTENSION COURSE SYLLABUS

Transcription

1 1 UCLA EXTENSION COURSE SYLLABUS Course Title: Predictive Analytics for Marketing Quarter: Fall quarter 2015 Instructor: Dr. Ash Pahwa Meeting Time: UCLA: 154 Dodd Hall Monday, 7-10pm, September 28 - December 7, 2015 Reg #: Course Description Predictive Analytics (PA) is a leading-edge technology that is being adopted by many successful Fortune 500 corporations. As the name suggests, it seeks to predict the outcome of certain events. Predictive analytics is derived from machine learning and data mining techniques and based on statistics. The course will focus on the marketing applications of Predictive Analytics. Microsoft Excel will be used as a work engine for data analysis. First the fundamental statistical concepts will be covered which will prepare the students to understand predictive analytics techniques. The basic statistics will be covered which includes central limit theorem, normal distribution, confidence interval, margin of errors and hypothesis testing. Predictive Analytics techniques covered would be regression, naïve Bayes, monte carlo simulation, time series data analysis and a few more. Each of these PA techniques will be covered using marketing applications like forecasting, advertising, market research, customer value and text mining. Digital Analytics (DA) is a set of business and technical activities that create and collect Big Data and process it into analyses, recommendations, optimizations, and predictions. Digital Analytics (DA) provides sophisticated traffic information about a web service and it delivers a comprehensive array of business intelligence and visitor behavior insights which is vital for any marketing department. DA data can be used for market predictions. DA data will be used for explaining PA techniques. This course is designed for marketing professionals who are currently working with Digital Analytics. It will provide insight into how analytics data can be used for market predictions. Prerequisites (classes or knowledge required for this course) Basic Marketing Basic Math: Excel, Statistics Basic Analytics Course Audience Corporate internet marketing professionals.

2 2 Textbook Marketing Analytics: Data-Driven Techniques with Microsoft Excel 1st Edition by Wayne L. Winston (Author) Instructor Information Ash Pahwa, Ph.D. Office Phone: (949) Website: Ash Pahwa, Ph.D., is an educator, author, entrepreneur, and technology visionary with three decades of industry and academic experience. He has founded several successful technology companies during his career. The latest, A+ Web Services ( provides internet marketing and digital analytics services. His complete bio and CV are available at Dr. Pahwa earned his doctorate in Computer Science from the Illinois Institute of Technology in Chicago. He is listed in Who's Who in the Frontiers of Science and Technology. He is also a Google Certified Analytics Consultant. His expertise includes search engine optimization, digital analytics, web programming, digital image processing, database management, digital video, and data storage technologies. In Industry, Dr. Pahwa has worked for General Electric, AT&T Bell Laboratories, Xerox Corporation, and Oracle. He founded CD-Gen, Inc. and DV Studio Technologies, LLC., which introduced successful products for CD- Recording (CDR), MPEG encoding, and videotape-to-dvd archiving. His book, CD-Recordable Bible, has been published in English, Japanese, and German. In Academia, Dr. Pahwa teaches internet technology courses and conducts webinars in the University of California system. Since 2008, he taught many courses at UC Irvine, UCLA, and UC San Diego, including: Website Development Digital Marketing Predictive Analytics WordPress CMS Search Engine Optimization R Programming Microsoft ASP.NET Digital Analytics Predictive Analytics Using Google Analytics Google AdWords MATLAB Programming

3 3 Methodology This course will be taught at the UCLA Campus. Course Objectives: The goal of this course is to examine the predictive power of analytics data for marketing applications. Students will learn the following: Predictive Analytics for marketing applications. Analytics as the source of data for Predictive Analytics. Predictive Analytics technique: Linear Regression: Pricing Predictive Analytics technique: Multiple Regression: Forecasting Predictive Analytics technique: Monte Carlo Simulation: Customer Value Predictive Analytics technique: Naïve Bayes: Market Research Excel statistical packages for data analysis. Problems marketing professionals face using Predictive Analytics. Expected Learning Outcomes: Data preparation techniques for Predictive Analytics projects Identifying the Predictive Analytics technique used for a specific project Understanding Predictive Analytics techniques Identifying marketing application for predictive analytics Accessing different Predictive Analytics techniques Grading Course grades will be based on the completion of assignments and class participation. Evaluation of Student Performance: Homework assignments 100 points Total 100 points Grading Scale: A = 90% 100% B = 80% 89% C = 70% 79% D = 60% 69% F = below 60% Midterm Exam: No Final Exam: No Note: ALL COURSE GRADES ARE FINAL

4 4 Course Outline Lesson Subject Marketing Application 1 What is Predictive Analytics? Tool for Predictive Analytics: Microsoft Excel 2 Displaying and Summarizing Data Variance and Standard Deviation Normal Distribution & z-scores 3 Covariance + Correlation Pricing + Linear Regression: 2 Variables Forecasting Multiple Regression 4 Sampling Distribution Central Limit Theorem: Mean + Proportion 5 Estimating the value of a parameter Confidence Interval + Margin of Error + t-scores 6 Hypothesis Testing Type I and Type II errors 7 Analysis of Variance (ANOVA) Market Research 8 Pay-Per-Click Advertising 9 Monte Carlo Simulation Customer Value 10 Naïve Bayes Predictive Analytics method Text Mining + Market research 11 Time Series Analysis: Moving Average Forecasting Trend and Seasonality Exponentially Weighted Moving Average

5 5 Expectations: Students are expected to: Actively participate in class discussions Complete all readings and homework as assigned Be on time in submitting homework assignments Communicate respectfully to instructors and fellow classmates Utilize professional level English in presentations and written assignments Policies: Incompletes: The interim grade Incomplete may be assigned when a student's work is of passing quality, but a small portion of the course requirements is incomplete for good cause (e.g. illness or other serious problem). It is the student s responsibility to discuss with the instructor the possibility of receiving an I grade as opposed to a non-passing grade. The student is entitled to replace this grade by a passing grade and to receive unit credit provided they complete the remaining coursework satisfactorily, under the supervision of and in a time frame determined by the instructor in charge, but in no case later than the end of the next academic quarter. At that time, the Registrar will cause all remaining Incompletes to lapse to the grade "F". Note: Receiving an I does not entitle a student to retake all or any part of the course at a later date. Academic Honesty Policy Academic dishonesty covers behavior in cheating, plagiarism, and fabrication of information. These behaviors are not tolerated. Students are encouraged to familiarize themselves with the UCLA Extension Student Conduct Code and the official statements regarding cheating and plagiarism at: Services for Students with Disabilities In accordance with Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act of 1990, UCLA Extension provides appropriate accommodations and support services to qualified applicants and students with disabilities. These include, but are not limited to, auxiliary aids/services, such as note takers, audiotaping of courses, sign language interpreters, and assistive-listening devices for hearing-impaired individuals, extended time for and proctoring of exams, and registration assistance. Accommodations and types of support services vary and are specifically designed to meet the disability-related needs of each stude3nt based on current, verifiable medical documentation. Arrangements for auxiliary aids/services are available only through UCLA Extension Disabled Student Services at (310) (voice/tty) or by at [email protected]. Please request such arrangements with at least five working days advance notice. All assistance is handled in confidence. Accommodations must be pre-approved. Requests for retroactive accommodation will not be accepted.