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BU MET AD688 Web Analytics, Syllabus Fall 2020

MET AD688 Web Analytics for Business

Course Content and Objectives

The Web Analytics for Business courses builds on the business analytics foundational course to provide a wide-ranging overview of digital analytics tools and techniques. The course introduces database fundamentals, web analytics, and ecommerce analytics concepts of importance in the industry today. Students gain hands-on experience with a variety of tools for each of the key concepts. Students explore web analytics, text mining, and web mining, within the context of a practical application. The text-mining module covers the analysis of text and discusses mining of data to gather business intelligence. Application areas such as mining the social web is extensively investigated. This course should be of interest to students who want to become competent business analysts, ecommerce consultants, entrepreneurs, marketers, analysts, and data scientists.

Prerequisite: AD100, AD 571, and ADR100

Upon completion of this course, you will have advanced knowledge of digital, ecommerce and web analytics. See the Table below for details of the course and the learning outcomes.

. The database part of this course introduces students to designing a mission critical database application including importing and exporting content and analyzing and presenting the information using front endtools. You will also use Azure with SQL Server within this context.

. The web analytics component of this course studies the metrics of websites, their content, user behavior, and reporting. The Google analytics tool is illustrated to collect and analyze analytics data. A large real- world dataset called Google Merchandise Store is used to master concepts.

. Email analytics and SEO concepts are also introduced in this course.

. A term project that will provide advanced overview an integrated overview of the above concepts.

Course Resources

Suggested Textbooks and Case Studies

Data Analytics Made Accessible 2018. Edition Kindle Edition Author: Anil Maheshwari, Publisher: Amazon Digital Services LLC ASIN: B00K2I2JL8 (Purchase it from amazon.com – NOTE: it is free with Kindle Unlimited Account and you can request a trial subscription as well).

Recommended Resources

We strongly recommend that you install software ahead of time and review tutorials shown below.

. Databases: Lucid Chart or Visio to draw ERD and your database schema (your first assignment). BothSoftware are available at no cost to you. See http://www.bu.edu/metit/hw-and-sw/msdn-academic-alliance-software- center/ to download MSVisio.

. ERD – Study the following tutorial ERD Video review Lucid Chart

Azure – SQLServer - http://www.bu.edu/metit/hw-and-sw/msdn-academic-alliance-software-center/

. Light Database to practice SQL: SQLite (https://sqlite.org/download.html) and then study http://www.sqlitetutorial.net/

SQL:

. SQL Database Fundamentals (https://mva.microsoft.com/en-US/training-courses/sql-database-fundamentals- 16944?l=DJNMjaPnD_9605244527);

. Microsoft Azure – Querying with T-SQL (https://mva.microsoft.com/en-US/training-courses/querying-with- transactsql-10530?l=TjT07f87_9804984382).

. For a quick start visit W3Schools (https://www.w3schools.com/sql/)

Google Analytics:

. Read all the three modules – GA for Beginners, Advanced GA (https://analytics.google.com/analytics/academy

. Dashboards: Two Resources a) Power BI (https://mva.microsoft.com/en-US/training-courses/data-series- analytics-bi-power-bi-17708?l=uGTGJnp2D_6611787171) b) Google Dashboard

(https://analytics.google.com/analytics/academy

Text Analytics

. ADR100 lab resources

. R & SQL: Learn to Use R – Hands-on Guide

(https://images.techhive.com/assets/2015/02/20/r4beginners_v3.pdf) SQL Server R tutorial

(https://docs.microsoft.com/en-us/sql/advanced-analytics/tutorials/sql-server-r-tutorials?view=sql-server- 2017)

. Exclusive SQL Tutorial on Data Analysis in R (https://www.hackerearth.com/blog/machine-learning/exclusive- sql-tutorial-on-data-analysis-in-r/)

Course Structure

This course is organized around 6 modules. The material is presented as Lectures and there are two lectures per module (thus 12 lectures in total). Some lectures are assigned for project presentations and research paper presentations. Please adhere to the due dates posted on the course website. Late work is not accepted unless an official medical letter is presented.

Grading Structure and Distribution

Your performance in the course will be graded in the following areas:

Additional details for each grading component are provided below.

Timely Submission of Materials Due

All work requests from the instructor (quizzes, assignments, contributions in the team work, etc.) have due dates (see the Course Schedule). These are the last dates that stated material is due. This means that it is a good idea to set

personal targets before then as your personal completion date to avoid difficulties. Dates are often viewed by

students as the date to turn in an assignment. We view assignment due dates as the last date on which to turn in an assignment. With this warning please note that we are not inclined to accept late work; if late work should be

accepted it will be done only after considerable weighing of rationale, and with penalty.

Submission Format

All written contributions should follow the APA writing style, in particular, the requirements how to lay out a paper, as well as how to cite and reference correctly.

Quizzes

Each of the quizzes will consist of several multiple choice and/or true/false questions. There may be written formats as well for certain questions. They will be derived from the topical coverage of the recent lectures, including assigned

reading assignments and practice problems. On campus quizzes will generally be conducted at the start of the class. If you are late or miss a class, we will not offer make up quizzes.

Assignments

There are individual and group assignments.

The individual assignments can be found in the “Assignments” area of the BB course website and should be submitted there.

Individual Assignment 1: Conceptual data modeling of a schema from a dataset, Entity Relationship Diagram (ERD).

Individual Assignment 2: Create SQL tables; query and update tables.

Individual Assignment 3: Create a Google Analytics (GA) account and gather data on a personal site. Gather and report email Analytics from a campaign. Example, you will be requested to promote the personal site to fellow students. You are expected to write an APA style paper on your findings.

Group Term Project: This is a capstone project dealing with web analytics of a large ecommerce dataset. The data is based on Google Merchandise Store,

. Group size (Recommended Maximum Size: Five Students). Peer evaluations will be conducted.

. The Group Term Project is structured in three phases: Phase 1: Problem Statement (due Week 6, Day 7)

Phase 2: Business Questions and KPI’s considered to solve the problem statement (due Week 9, Day 7)

Phase 3: Completed Term paper and summary report with analytics, dashboards and recommended solutions (due Week 13, Day 7)

Final Presentation (Based on the Summary Report) will include live demonstration of Google Dashboard. You are encouraged to show evidence of exemplary work by showing database results and graphics from further insightful analysis generated using R.

. Topics covered: Web Analytics using Google Analytics (GA) to analyze a business problem(s) on the Google Merchandise Store (GMS)

. Duration for each team presentation: max 20 minutes plus Q/A max 5 min (max 25min/team)

. All PPT-based final presentations will be delivered in person.

. All students are expected to be active in fellow student presentations.

. Presentations will be recorded and posted on the course website.

. The grading and evaluation criteria for the presentations are as follow:

Total grading points 7

PowerPoint Slides: 1

Content of the presentation: 4

Delivery: 1

Discussion: Q/A 1

In-Class Exercises

Several individual and group exercises are conducted in class. The exercises are not graded. See the Course Schedule for topics and dates. You should be prepared on those days to use a computer and the assigned software.

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Class Discussions

Discussions: Students will take part in a set of focused discussions that typically deal with the material covered during that week’s lectures. Participation requirements include one or more posts and a requirement that each student

respond to posting from at least two other students. These assignments will be done individually. Initial posts are due by Day 4 at 11:59 PM, and response to other students’ posts must be completed by Day 7 at 11:59 PM. Grades will be based on the criteria described in the Discussion Participation Grades table. Discussions will be based on ideas,

arguments and analysis presented. If discussion post expressed a lack of understanding of the discussion topic or if the comments are irrelevant, off-topic, and confusing to follow or if a particular viewpoint is not supported with evidence or examples and citation in APA style, you will receive a below average grade. If the discussions contribute to the

learning and motivates further group discussion that will result in above average grades. You are requested to ask follow-up questions, respectfully encourage a variety of viewpoints and invites.

Note: In Week 1, you will be asked to “Introduce Yourself” (i.e., create a message to introduce yourself to your fellow students and the instructor). This posting will include a few sentences describing yourself, your interests, and your expectations for this course. This discussion will not be graded.

Your instructor might optionally add or delete in-class discussion topics in order to leverage his working experience. The topics of the Bb Discussion Forums and in class discussions are listed in the Course Calendar.