BBM467 - Data Intensive Applications, Fall 2022

BBM467 - Data Intensive Applications (Fall 2022)

The objective of this course is to teach students fundamentals of big data management and analytics. They will gain experience with some key technologies, platforms, tools and systems used by big data scientists and engineers. Key words: Big Data, Data Science, Distributed Computing, Cluster Computing, Scalable Machine Learning, Cloud Computing and Virtualization, Data and Ethics.

Prior to attending this course, participants better have basic understanding of computer systems, databases, and machine learning as well as Python programming and SQL knowledge. If you do not have these capabilities and still want to enroll, you must be willing to invest a lot of time to make up.

Beware that this course requires HARD WORK. Please consider a past student's testimony before you enrolll: "... aside from this course I have no other course which I consider 'hard'. ... Nobody should be forced to give all of their free time to a single course, and nobody should be forced to give all of their free thoughts to a single course."

Lectures will be conducted in English in the classroom. Past year's video recordings can be found on youtube. Course materials will be in English. There will be reading materials as well. Students are responsible from course materials, topics covered in practicum, assignments, quizzes, and readings. Full attendance to classes is recommended. No points will be awarded for attendance. It may be considered for bonus points when necessary while giving the final grades though.

The lecturer strongly believes that any student can pass the class with a good grade as long as she/he tries hard enough. There will be one written midterm examination (15%), a Blog Post Project (10%), a Data Science Capstone Project (35%), and one final examination (40%). Also note that a student must achieve at least 40% in the final exam to pass the class.

You will be working on two different projects. You may work alone or in groups of maximum two.

Data Science Capstone Project (DSCP): The projects may be in either of the following themes: disease diagnosis/prognosis/treatment in rheumatology, microscopic image classification, sleep monitoring, patient monitoring, nlp in healthcare, biomedical text processing, or cryptocurrency trading. Each theme can be studied by at most 20% of the groups. Therefore, it is of paramount importance to decide on your group members and project titles quickly. Projects will be delivered as a Jupyter Notebook and a presentation (Power Point + Video recording).

Blog Post Project (BPP): You will write a blog post on a subject you desire. The subject must be in the scope of the lecture. An additional 10% bonus will be awarded for BPPs that exceed expectations. See The Good Class for example posts.

Hard work always leads to great reward. We published two conference papers as the outcome of students projects in Fall 2021 semester.

I welcome students who are interested in publishing their work. Writing a paper, submitting it, waiting for a decision, and receiving the acceptance letter is an amazing experience. You could make your resume stronger as well.

Here are more incentives. At the end of the semester, if you deliver a publishable work

at a national conference, you can get up to 10% bonus points.
at an international conference, you can get up to 20% bonus points.
at a journal indexed by TR-Dizin, you can get up to 25% bonus points.
at a journal indexed by the Science Citation Index (Expanded), you can get up to 50% bonus points.

Although BBM469 is an independent lecture, it goes hand in hand with BBM467. It is strongly recommended that you enroll in BBM469 only if you are enrolled in BBM467.

There will be five assignments. You will be working alone. Assignments #1, #2, and #4 will be on DataCamp. You will be completing some online courses and doing quizzes. Assignments #3 and #5 will be on Google Cloud Skills Boost as online courses and hand-on labs. Google credits to conduct labs will be provided by the lecturer.

Assignments and projects must be delivered within deadlines. Late deliveries are not allowed.

Week #	Date	Title	Slides	BBM467 Practicum	BBM469 Lab Work Deadlines are on lab days unless stated explicitly
1	03.10	What is Data Science? Intoduction to Big Data?	pdf
2	10.10	Data Science Methodology	pdf
3	17.10	Data Analysis with Python	pdf		Assignment 1 (out): Introduction to Data Science in Python, Python Data Science Toolbox (Part 1 and 2)
4	24.10	Data Visualization with Python	pdf	Establishing DSCP and BPP Groups Deadline: 23/10/2022, 23:59
5	31.10	Machine Learning with Python	pdf		Assignment 2 (out): Supervised Learning with scikit-learn, Unsupervised Learning in Python Assignment 1 (due)
6	07.11	Foundations for Big Data Systems, Clusters, Hadoop	pdf
7	14.11	Cloud Computing, Virtualization, Containerization	pdf	Submission of DSCP and BPP Proposals Deadline: 13/11/2022, 23:59	Assignment 3 (out): Google Cloud Fundamentals: Core Infrastructure Assignment 2 (due)
8	21.11	Midterm	pdf
9	28.11	SQL for Data Science	pdf		Assignment 4 (out): Exploratory Data Analysis in SQL, Data-Driven Decision Making in SQL
10	05.12	NOSQL	pdf		Assignment 3 (due)
11	12.12	Scalable Machine Learning with Spark	pdf		Assignment 5 (out): Google Cloud Big Data and Machine Learning Fundamentals
12	19.12	Blockchain	pdf		Assignment 4 (due)
13	26.12	Data and Ethics		DSCP Final Deliveries Deadline: 30/12/2022, 23:59
14	02.01	Review for Final Examination	pdf	BPP Final Deliveries Deadline: 06/01/2023, 23:59	Assignment 5 (due)

I do not follow a specific text book but, here are few books I can refer to.

Python for Data Analysis, 2nd Edition, Data Wrangling with Pandas, NumPy, and IPython, By William McKinney, 2017.
Mining of Massive Datasets (2nd Edition), Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author), Cambridge University Press, 2014.
Learning Spark: Lightning-Fast Big Data Analysis, Holden Karau and Andy Konwinski, O'Reilly, 2015.

The course webpage will be kept up-to-date throughout the semester. All course related communications will be carried out through Piazza.

Please enroll at Piazza for BBM467.
Please enroll at Piazza for BBM469.

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