CSIS 485 Big Data & Analytics

Course Description

A special-topics course discussing data mining and machine learning algorithms for analyzing and data visualization techniques for visualizing very large amounts of data. The course emphasizes MapReduce as a tool for creating parallel algorithms that can process very large amounts of data. Programming assignments are completed in one or more high-level languages.


Brian R. Snider
Office hours: Klages 202 (see schedule)





Students will be introduced to data mining, machine learning, and data visualization techniques. Students will use both descriptive and predictive methods on non-trivial data sets. Students will become familiar with various modalities of data and the challenges inherent with each.

This course provides additional exposure to database, machine learning, distributed/parallel computing, and algorithmic methods. In this course, students will expand their skills in each of these areas.

Course Organization

The course will include regular homework and/or programming assignments. Unless otherwise specified, assignments are due before the beginning of class on the due date. There will be no credit given for late assignments (without an excused absence)—turn in as much as you can.

Reading assignments should be completed before the lecture covering the material. Not all reading material will be covered in the lectures, but you will be responsible for the material on homework and exams. Quizzes over the assigned reading may be given at any time.


See the GFU CS/IS/Cyber policies for collaboration and discussion of collaboration and academic integrity. Most students would be surprised at how easy it is to detect collaboration in programming—please do not test us! Remember: you always have willing and legal collaborators in the faculty.

Almost all of life is filled with collaboration (i.e., people working together). Yet in our academic system, we artificially limit collaboration. These limits are designed to force you to learn fundamental principles and build specific skills. It is very artificial, and you'll find that collaboration is a valuable skill in the working world. While some of you may be tempted to collaborate too much, others will collaborate too little. When appropriate, it's a good idea to make use of others—the purpose here is to learn. Be sure to make the most of this opportunity but do it earnestly and with integrity.

Engineering Your Soul

The mission and vision statement of the Computer Science & Information Systems (CSIS) program states that our students are distinctive by "bringing a Christ-centered worldview to our increasingly technological world."

As one step towards the fulfillment of this objective, each semester, the engineering faculty will collectively identify an influential Christian writing to be read and reflected upon by all engineering faculty and students throughout the term. As part of the College of Engineering, CSIS students participate in this effort, known as Engineering Your Soul (EYS). This exercise will be treated as an official component of every engineering course (including CSIS courses) and will be uniquely integrated and assessed at my discretion, typically as a component of the quiz grade.

Each Monday morning students should email a brief reflection on the previous week's assigned reading. In addition, regular meetings will be scheduled throughout the semester that can be attended for chapel elective credit.

It is our hope that students will not view this as one more task to complete, but as a catalyst for continued discussion ultimately leading to a deeper experience of Jesus Christ.

Online Portfolio

All students in the College of Engineering are required to create and maintain an online portfolio on Portfolium to showcase their best work. Portfolium is a "cloud-based platform that empowers students with lifelong opportunities to capture, curate, and convert skills into job offers, while giving learning institutions and employers the tools they need to assess competencies and recruit talent."

Students will post portions of their coursework to Portfolium as directed by their instructor. For example, a portfolio entry might be PDF of poster or presentation content, screenshots or a video demonstration of a software or hardware project, or even an entire source code repository. In addition to required portfolio entries, students are encouraged to post selected work to their portfolios throughout the year.

Students will work with their faculty advisor to curate and refine their portfolios as they progress through the program. Students shall ensure that all portfolio entries are appropriate for public disclosure (i.e., they do not reveal key components of assignment solutions to current or future students).

University Resources

If you have specific physical, psychiatric, or learning disabilities and require accommodations, please contact the Disability Services Office as early as possible so that your learning needs can be appropriately met. For more information, go to ds.georgefox.edu or contact Rick Muthiah, Director of Learning Support Services (503-554-2314 or rmuthiah@georgefox.edu).

The Academic Resource Center (ARC) on the Newberg campus provides all students with free writing consultation, academic coaching, and learning strategies (e.g., techniques to improve reading, note-taking, study, time management). The ARC, located in the Murdock Learning Resource Center (library), is open from 1:00–10:00 p.m., Monday through Thursday, and 12:00–4:00 p.m. on Friday. To schedule an appointment, go to the online schedule at arcschedule.georgefox.edu, call 503-554-2327, email the_arc@georgefox.edu, or stop by the ARC. Visit arc.georgefox.edu for information about ARC Consultants' areas of study, instructions for scheduling an appointment, learning tips, and a list of other tutoring options on campus.


Grading Scale

Current Grades

The final course grade will be based on:

Tentative Schedule

Week 1

Data Mining and MapReduce

Reading: Ch. 1–2

Week 2

Finding Similar Items

Reading: Ch. 3

Week 3

Data Streams

Reading: Ch. 4

Week 4

Link Analysis

Reading: Ch. 5

Week 5

Frequent Itemsets

Reading: Ch. 6

Week 6


Reading: Ch. 7

Week 7

Web Advertising

Reading: Ch. 8

Week 8

Data Visualization


Midterm exam

Reading: Ch. 1–8

Week 9

Recommendation Systems

Reading: Ch. 9

Week 10


Reading: Ch. 10

Week 11

Spring break — no class

Week 12

Dimensionality Reduction

Reading: Ch. 11

Week 13–14

Machine Learning

Reading: Ch. 12

Week 15

Institutional Analytics


Final exam

Reading: Ch. 1–12

This page was last modified on 2017-08-18 at 07:44:59.

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