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.
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Competency |
Learning Outcomes (Career Skills) |
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Understand what is a database system and the relational model of data |
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Database Modeling & Design
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Understand database concepts and role of database management system |
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Model data requirements using conceptual modeling tools like ER diagrams and design database schemas based on the conceptual model. |
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Be proficient in SQL |
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Data Manipulation |
Write SQL commands to create tables insert, update and delete data. | |
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Write SQL commands to query data from single or multiple tables |
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Work with a mission critical virtual DBMS that is hosted on the cloud |
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Understand how programming languages (e.g., R and Python) interact with DBMS data |
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Understand web analytics |
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Web Analytics |
Understand web analytics terminology and metrics |
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Collecting analytics data and configure it for analysis & understanding attribution models |
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Defining web analytics goals and KPIs |
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Be proficient in Google Analytics |
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Google Analytics |
Explore the basic features of Google Analytics |
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Explore Google Analytics views and reports |
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Understand user, acquisition, behavior, and conversion reports |
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Analyze marketing campaigns, goal conversion tracking and user behavior |
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| Be proficient in Advanced Web Analytics and Dashboarding | ||
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Advanced Google Analytics
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Understand how web analytics data gets collected and processed
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Collect data and configure custom dimensions, metrics
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Segment data in order to obtain insight on data by channel and audience
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| Be proficient in Mobile Analytics | ||
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Mobile Analytics |
Understanding the conceptual difference between web and mobile analytics |
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Understanding how mobile marketing works with iOS and Android devices |
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Mobile App reporting in Google Analytics (E.g., Firebase) |
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| Appreciate enterprise level tools including cross channel marketing | ||
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Ecommerce Analytics
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Understand users, attract high-value traffic, improve site engagement
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Understand which products are performing well on a site |
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Improve product performance and examine abandoned shopping carts |
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Understand enterprise-level features of web analytics software (e.g..,Google Analytics 360 and Adobe Analytics) |
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| Be proficient in Email Analytics | ||
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Email Analytics |
deriving insight from email analytics and improving campaign performance
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Understanding email delivery and campaign measurements |
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Deriving insight from email analytics and improving campaign performance |
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| Understand Social Media Mining and Analytics Tools | ||
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Social Media Analytics |
Understanding social media marketing
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Using tools and platforms to track and report social media metrics |
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Advanced tactics to engage users |
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Use commercial tools that provide analytics |
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Understand Text Analytics and Analytics Tools |
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Text Analytics and Mining |
Text analytics and mining (e.g., parsing product reviews from social media and websites)
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Using tools that span the lifecycle from data preparation to machine learning to predictive model deployment |
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Using tools and platforms that provide an integrated environment to mine text and analyze data |
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Understand contemporary issues in Data Analysis |
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Big Data |
Understanding the need and role for big data
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Architectures, database systems and tools and techniques to process complex data |
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Understanding how complex data sets is processed using NoSQL, MapReduce and machine learning. |
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Be able to set up and lead an analytics operation |
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Setting up an Analytics Operation |
People, Processes and Tools
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Preserving security and data integrity |
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Communicating relevant information to stakeholders |
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Be proficient at Data Ethics |
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Ethics & Professional Responsi-bility |
Understand ethical considerations in every aspect of a data analytics operation
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Understand key privacy issues associated with big Data |
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Using big data analytics responsibly |
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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 MS Visio.
- 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-withtransactsql-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-seriesanalytics-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/exclusivesql-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:
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Discussions in class and in the Blackboard Discussion Forum Modules 1 to 5 @ max 3 pts/week
Participation & Attendance max 6 pts |
21% |
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Quizzes - 5 quizzes @ max. 5 pts/quiz |
25% |
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Assignments: |
18% |
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Individual Assignment 1 (Due Week 2, Day 7) 6% Individual Assignment 2 (Due Week 3, Day 7) 6% Individual Assignment 3 (Due Week 5, Day 7) 6% |
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Group Term Project
Phase 1 – Week 6, Day 7 (Problem statement, methodology & research) 7% Phase 2 – Week 9. Day 7 (Deliverables as per the proble statement) –7% Phase 3 – Week 13, Day 7 (Final Summary Report) 15%
Final Presentations 7% |
36% |
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TOTAL: |
100% |
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Grade Converter Table |
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Points |
Grade |
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96-100 |
4.00 |
A |
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91-95.99 |
3.67 |
A- |
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86-90.99 |
3.33 |
B+ |
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81-85.99 |
3.00 |
B |
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76-80.99 |
2.67 |
B- |
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71-75.99 |
2.33 |
C+ |
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66-70.99 |
2.00 |
C |
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61-65.99 |
1.67 |
C- |
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56-60.99 |
1.33 |
D+ |
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51-55.99 |
1.0 |
D |
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Under 50.99 |
0 |
F |
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 (max25min/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 asfollow:
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Total grading points |
7 |
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PowerPoint Slides: |
1 |
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Content of the presentation: |
4 |
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Delivery: |
1 |
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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.
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.
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Score
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Description
Note: The length of the initial contribution to the discussion topic should not exceed 250 words. Actively responding to another student's initial submission means providing the rationale as to why you agree or disagree with another student's submission; responses such as "I agree," will not be counted.
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3.0 |
Exceptional Participation – Met both of these conditions:
• Submitted own contribution and actively responded to two or more otherstudents.
• Exceptional quality - Student explored others' comments and built on others' insights. The contributions are especially insightful and represent new high-value added input with new insights, material, and/or references.
• Author builds on discussions of others and has several High-level contributions during the module. The student with High-Level Participation is in the top 10% of the class for themodule.
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2.75 |
Commendable Participation – Met both these conditions:
• Submitted own contribution and actively responded to two or more otherstudents.
• High quality - Student explored others' comments and built on others' insights. The contributions are insightful and represent high-value added input with insights, material, and/orreferences.
• Author builds on discussions of others and has several High-level contributions during the module. The student with High-Level Participation is in the top 25% of the class for themodule.
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2.0 |
Moderate Participation – Met both of these conditions:
• Submitted own initial contribution for a selected discussion topic and responded to two ormore other students.
• Moderate quality – Student was active in discussions made some valuable contributions, but the contributions were not noteworthy or did not include sufficient insights, material, and/or references.
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1.5 |
Acceptable Participation – Met both of these conditions
• Submitted own initial contribution for a selected discussion topic and responded to two or more other students.
• Low Level quality – Student participated in discussions made contributions, but the contributions did not add value to the discussion or did not include sufficient insights, material, and/or references.
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1.0 |
Minimal Participation – Met both of these conditions:
• Submitted own initial contribution for a selected discussion topic but did not respond to two or more other students.
• Minimal quality – Student participated in some discussions made irrelevant or incorrect contributions, and the contributions did not include insights, material, and/or references. Author has been in discussion during the module but tends to repeat others or make opinion-related statements. Quite below average postings.
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0.0 |
Inadequate Participation – Met both of these conditions:
• Did not participate in the discussion topic.
• Author is not active, and postings would be of a personal nature that do not contribute to the knowledge of the course. Postings are well below average as they merely restate or provide personal opinions.
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Academic Integrity
Students are expected to adhere to the highest standards of honesty and integrity for this course. University policy on academic integrity will be followed to the fullest. Students are encouraged to review the university policy on academic integrity including a detailed listing of activities warranting sanction. Anyone who fails to adhere to these requirements and/or otherwise engages in unethical behavior (including cheating on exams, false representation of self or one’s work efforts, use of unauthorized aids, etc.) will be referred to university administration for further action. In particular, the university's policy and consequences regarding plagiarism are clearly described in the official Boston University documents and will be enforced without any compromises.
Request for Accommodations
If you have a disability and will be requesting accommodations for this course, please inform the instructor early in the semester. Advance notice and appropriate documentation are required for accommodations.