AD 688 Big Data Analytics – AD 688 Web Analytics
AD 688 Big Data Analytics
Syllabus
2 Course Content and Objectives
2.1 Course Description
The Web Analytics for Business courses builds on the business analytics foundational course provides comprehensive introduction to Big Data, Data Visualizations, and Cloud Analytics. Students gain hands-on experience with a variety of tools for key concepts. Students will explore core concepts on Big Data and Cloud Analytics including Management of Big Data, Massive Data Stores, Cloud Analytics, Web scraping, Text & Web mining, with comprehensive theory and Practical Application. The course emphasizes hands-on learning, data analytics workflows, and cloudbased tools, preparing students to tackle real-world challenges in business analytics with scalable, data-driven solutions. This course is designed for students aspiring to become competent consultants, entrepreneurs, analysts, machine learning engineers, and data scientists. Upon completion of this course, you will have advanced knowledge of big data anc cloud analytics tools.
3 Course Learning Objectives
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 end tools.
The web analytics part of this course studies the metrics of websites, their content, user behavior, and reporting. The Google analytics tool is illustrated to collect analytics data.
The web mining module presents how data is extracted from websites and analyzed.
Email analytics and mobile analytics concepts are also introduced in this course.
A term project that will provide advanced overview an integrated overview of the above concepts.
4 Course Resources
There is no required textbook for this course. All required readings will be provided in the course website through notes and videos on canvas. The following textbooks are recommended for this course. Some of the books are freely available through the BU library and on the web:
Slides and lecture notes are created from combination of sources
1. Big Data Hands On
1. Judith S Hurwitz et al., Big Data for Dummies (John Wiley & Sons, 2013).
2. Venkat Ankam, Big Data Analytics (Packt Publishing Ltd, 2016).
3. Thomas Erl, Wajid Khattak, and Paul Buhler, Big Data Fundamentals: Concepts, Drivers & Techniques (Prentice Hall Press, 2016).
4. Scott Haines, “Modern Data Engineering with Apache Spark,” n.d.
5. Sridhar Alla, Big Data Analytics with Hadoop 3: Build Highly Effective Analytics Solutions to Gain Valuable Insight into Your Big Data (Packt Publishing Ltd, 2018).
2. Cloud Computing
1. Judith S Hurwitz and Daniel Kirsch, Cloud Computing for Dummies (John Wiley & Sons, 2020).
2. Thomas Erl, Ricardo Puttini, and Zaigham Mahmood, Cloud Computing: Concepts, Technology & Architecture (Pearson Education, 2013).
3. Gautam Shroff, Enterprise Cloud Computing: Technology, Architecture, Applications (Cambridge university press, 2010).
4. Sandeep Bhowmik, Cloud Computing (Cambridge University Press, 2017).
4.1 Recommended Textbook
1. Hurwitz et al., Big Data for Dummies.
2. Hurwitz and Kirsch, Cloud Computing for Dummies.
4.2 Optional Textbooks
3. Haines, “Modern Data Engineering with Apache Spark”.
4. Alan Anderson, Statistics for Big Data for Dummies (John Wiley & Sons, 2015).
5. Ron Kohavi, Diane Tang, and Ya Xu, Trustworthy Online Controlled Experiments: A Practical Guide to a/b Testing (Cambridge University Press, 2020).
6. Anthony DeBarros, Practical SQL: A Beginner’s Guide to Storytelling with Data (no starch Press, 2022).