INFM 203-10
Big Data Analytics & Management
Fall 2019 Syllabus

Dr. Glen Mules
E-mail
Office: office location
Phone: 914.235.7916
Office Hours: Virtually, by appointment (use e-mail to schedule). Zoom optional drop-in office hours will also be held as needed. Eastern timezone.

Canvas Information: Courses will be available beginning October 14, 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.

This two-unit course runs from October 14 to December 9. The class will be available on Canvas on October 14.

Course Description

Covers important big data technologies, trends, infrastructure, and management issues that enable users to make informed and strategic decisions with the presence of large-scale data sets

Course Requirements and Assignments

Home computing requirements are posted online for prospective students: Home Computing Requirements. Students must meet those minimum requirements to participate in the activities for this course.

This schedule and related dates/readings/assignments is tentative and subject to change with fair notice. Any changes will be announced in due time in class and on the course’s web site in the Canvas Learning Management System (LMS). The students are obliged to consult the most updated and detailed version of the reading material and syllabus, which will be posted on the course’s website.

Assignments: All assignments are due by Sunday Midnight at the end of the week in which scheduled as noted in the table below. Practical Labs work is evidenced as completed by submitting an MS Word file (YourLastName-Weeknn-Report.docx) to the discussion section and the appropriate week-folder for this course on Canvas.

Assignments are subject to change with fair notice.

All assignments are due by Midnight Pacific Time on the Sunday Night at the end of the relevant week unless noted differently below.

Labs / Hands-on Practices (50 percent of overall grade, supports CLO 2)

Individual, hands-on practices will be given throughout the semester to help students review and reinforce what they have learned in class. Students will learn how to analyze and visualize big data with practical tools.

These Labs (or Hands-on Practices) are an important part of the course. The Labs generally require the submission of a Lab Report (MS Word File, .docx) to the Canvas website for the course.

Semester Project (25 percent of overall grade, supports CLOs 1-4) 

Students will work in teams on a project that consists of three phases (more details TBA on the Canvas site). The main requirement of the project is that it uses at least one of the tools covered in the class

• Milestone I - Initial Thoughts: Students will submit a short paragraph discussing the potential topics and directions of the semester project. Students will also briefly present the motivation of the study and the approach that might be taken. 

Emphasis in the Project is on understanding the chosen data-set, data gathering, data munging / preparation, and the steps leading towards data analysis (this work is generally 60-80% of any data project).  Heavy analysis and computer programming are beyond the scope of the project.

• Milestone II - Mini Report: Students will submit a one-page report outlining the current progress of the project. The report will include what has been done, what the current status and results are, and what needs to be accomplished.
• Final Report and Demo: Students will submit a detailed, 10-page report for the project. The report should at least include the following sections: motivation, problem statement, methodology, analysis results, discussion, and conclusions. Students will also prepare a short "demo" to present and discuss their work.

Intermediate Quiz (Multiple Choice Questions [MCQ]) + Final Exam (20 percent supports CLOs 1-4)

Grading Information

Grading will be based on a total accumulation of possible 100 percent, distributed as follows:

The deliverables will be graded using larger numbers but the totals for each type of deliverable will be scaled to the relative percentages shown.

Recommended supplementary reading

In addition to the required and recommended readings mentioned further down this page, the following supplementary readings are recommended. 

Spivey, B., & Echeverria, J. (2015). Hadoop security: Protecting your big data platform. Sebastopol, CA: O'Reilly. ISBN 978-1-695-90098-7. 320pp. Amazon: $43.53. You can read excerpts of this book for free online at Google Books https://books.google.com/books?id=enEJCgAAQBAJ&printsec=frontcover&source=gbs_ge_summary_r
&cad=0#v=onepage&q&f=false

Zikopoulos, P., deRoos, D., Bienko, C., Buglio, R., & Andrews, M. (2015). Big data beyond the hype: A guide to conversations for today’s data center. New York: McGraw Hill Education. Available for free download at Big Data Beyond the Hype ftp://ftp.software.ibm.com/software/uk/bigdatabeyondthehype/
BigDataBeyondTheHype-final.pdf  

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. Available from Big data: The next frontier https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation

Various Apache Software Foundation (ASF) websites:

• Overview: http://hadoop.apache.org and Overview (current) http://hadoop.apache.org/docs/current
• MapReduce tutorial: http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html
• WordCount 2: http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Example:_WordCount_v2.0
• Spark: http://spark.apache.org
• Flume: http://flume.apache.org
• Sqoop: http://sqoop.apache.org
• Google’s Paper on Big Table: http://research.google.com/archive/bigtable.html
• Google’s Paper on MapReduce: http://research.google.com/archive/mapreduce.html
• FTC Report on Big Data: Tool for Inclusion and Exclusion: https://goo.gl/YgPCWv

Articles on specific topics:

• Spark: https://databricks.com/spark
• Other electronic articles referenced during the course itself.

top

Course Workload Expectations

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Prerequisites

Graduate Standing or Instructor Consent. 

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

1. Describe and explain how the main technologies and trends in big data work, specifically data visualization, large-scale database management, map-reduce paradigm, and big data mining.
2. Demonstrate proficiency in using current big data technologies to solve big data analytical problems.
3. Interpret and communicate big data analysis and visualization results appropriately, effectively and accurately.
4. Discuss, articulate and compare various big data management issues (e.g., big data privacy).

SLOs and PLOs

This course supports Informatics SLO 3: Demonstrate proficiency in using current big data and electronic records technologies to solve analytical problems; including developing policies, standards, and practices in particular specialized contexts and interpreting and communicating analysis and visualization results appropriately and accurately.

SLO 3 supports the following Informatics Program Learning Outcomes (PLOs):

• PLO 2 Evaluate, manage, and develop electronic records programs and applications in a specific organizational setting.
• PLO 3 Demonstrate strong understanding of security and ethics issues related to informatics, user interface, and inter-professional application of informatics in specific fields by designing and implementing appropriate information assurance and ethics and privacy solutions.
• PLO 6 Conduct informatics analysis and visualization applied to different real-world fields, such as health science and sports.

top

Textbooks

Required Textbooks:

• Akhtar, S. (2018). Big data architect's handbook: A guide to building proficiency in tools and systems used by leading big data experts. Packt Publishing. Available through Publisher

• Godsey, B. (2017). Think like a data scientist: Tackle the data science process step-by-step. Manning. Available through Amazon: 1633430278

• Kaldero, N. (2018). Data science for executives: Leveraging machine intelligence to drive business ROI. Lioncrest Publishing. Available through Amazon: 1544511256

Recommended Textbooks:

• White, T. (2015). Hadoop: The definitive guide (4th ed.). O'Reilly Media. Available as free download http://grut-computing.com/HadoopBook.pdf

top

Grading Scale

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

• C represents Adequate work; a grade of "C" counts for credit for the course;
• B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
  For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  
• A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).

University Policies

Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: https://www.sjsu.edu/curriculum/courses/syllabus-info.php. Make sure to visit this page, review and be familiar with these university policies and resources.

In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.

Download Adobe Acrobat Reader to access PDF files.

More accessibility resources.