DS256: Scalable Systems for Data Science [3:1] (Jan, 2018)

Department of Computational and Data Sciences

Scalable Systems for Data Science

  • Instructors: Yogesh Simmhan (email)
  • TA: Aakash Khochare (email)
  • Course number: DS256
  • Credits: 3:1
  • Semester: Jan, 2018
  • Lecture: Tue/Thu 330-5PM
  • Room: CDS 202
  • Pre-requisites: Data Structures, Programming and Algorithm concepts. Programming experience required, preferably in Java.
  • First class on Tue, Jan 9 at 330pm at CDS202
  • Register for Course Online using IISc AcadServer
  • See 2017 webpage


This course will teach the fundamental Systems aspects of designing and using Big Data platforms, which are a specialization of scalable systems for data science applications. This course will address three facets of these platforms.

  • The design of distributed program models and abstractions, such as MapReduce, Dataflow and Vertex-centric models, for processing volume, velocity and linked datasets, and for storing and querying over NoSQL datasets.
  • The approaches and design patterns to translate existing data-intensive algorithms and analytics into these distributed programming abstractions.
  • Distributed software architectures, runtime and storage strategies used by Big Data platforms such as Apache Hadoop, Spark, Storm, Giraph and Hive to execute applications developed using these models on commodity clusters and Clouds in a scalable manner.

It will cover topics on: Why Big Data platforms are necessary? How they are designed? What are the programming abstractions (e.g. MapReduce) that are used to compose data science applications? How the programming models are translated to scalable runtime execution on clusters and Clouds (e.g. Hadoop)? How do you design algorithms for analyzing large datasets? How do you map them to Big Data platforms? and How can these be used to develop Big Data applications in an integrated manner?

Several key and contemporary research papers will be discussed as part of the course. paper As part of a hands-on Project in this course, students will work with real, large datasets and commodity clusters, and use scalable algorithms and platforms to develop a Big Data application. The emphasis will be on designing applications that show good “weak scaling” as the size, speed or complexity of data increases, and using distributed systems such as commodity clusters and Clouds.

Besides class lectures, there will be several guest lectures by experts from the Industry who work on Big Data platforms, Cloud computing and data science.

This course extends from the systems basics introduced in the DS 221: Introduction to Scalable Systems course at CDS, and is complementary to the DS 222: Machine Learning with Large Datasets is offered in the Aug term. This course also complements other breadth courses on data science like the E0 229: Foundations of Data Science and E0 259: Data Analytics.

Intended Learning Objectives

At the end of the course, students will have learned about the following concepts.

  1. Types of Big Data, Design goals of Big Data platforms, and where in the systems landscape these platforms fall.
  2. Distributed programming models for Big Data, including Map Reduce, Stream processing and Graph processing.
  3. Design of and development on Big Data platforms and their optimizations on commodity clusters and Clouds.
  4. Scaling data Science algorithms and analytics using Big Data platforms.


This is an introductory course on platforms and tools required to develop analytics over Big Data. However, it builds upon prior knowledge that students have on computing and software systems, programming, data structures and algorithms. Students must be familiar with Data Structures (e.g. Arrays, Queues, Trees, Hashmaps, Graphs) and Algorithms (e.g. Sorting, Searching, Graph traversal, String algorithms, etc.).

It is recommended that students have good programming skills (preferably in Java) which is necessary for the programming assignments and projects. Familiarity with one or more of the following courses will also be helpful (although not mandatory): DS 292 (HPC), DS 295 (Parallel Programming), E0 253 (Operating Systems), E0 264 (Distributed Computing Systems), SE252 (Introduction to Cloud Computing), E0 225 (Design and Analysis of Algorithms), E0 232 (Probability and Statistics), E0 259 (Data Analytics).


The total assessment score for the course is based on a 1000 point scale. Of this, the weightage to different activities will be as follows:

45% Homework Two programming assignments (50+2*200 points)
20% Project One final project, to be done individually or in teams (200 points)
30% Exams One Final exam (300 points)
5% Participation Participation (i.e. not just “attendance”) in classroom discussions and online forum for the course (50 points)

Academic Integrity

Students must uphold IISc’s Academic Integrity guidelines. We have a zero-tolerance policy for cheating and unethical behavior in this course and failure to follow these guidelines will lead to sanctions and penalties. This includes a reduced or failing grade in the course, and recurrent academic violations will be reported to the Institute and may lead to an expulsion.

Learning takes place both within and outside the class. Hence, discussions between students and reference to online material is encouraged as part of the course to achieve the intended learning objectives. However, while you may learn from any valid source, you must form your own ideas and complete problems and assignments by yourself. All works submitted by the student as part of their academic assessment must be their own.

Verbatim reproduction of material from external sources (web pages, books, papers, etc.) is not acceptable. If you are paraphrasing external content (or even your own prior work) or were otherwise influenced by them while completing your assignments, projects or exams, you must clearly acknowledge them. When in doubt, add a citation!
While you may discuss lecture topics and broad outlines of homework problems and projects with others, you cannot collaborate in completing the assignments, copy someone else’s solution or falsify results. You cannot use notes or unauthorized resources during exams, or copy from others. The narrow exception to collaboration is between team-mates when competing the project, and even there, the contribution of each team member for each project assignment should be clearly documented.
Classroom Behavior
Ensure that the course atmosphere, both in the class, outside and on the online forum, is conducive for learning. Participate in discussions but do not dominate or be abusive. There are no “stupid” questions. Be considerate of your fellow students and avoid disruptive behavior.


Teaching & Office Hours

  • Lecture: Tue/Thu 330-5PM, CDS 202 (Yogesh)
  • Office Hours: By appointment

Tentative Schedule

  • First Class on Jan 9

    • Assignment 0 (5%)
  • Processing large data volumes (~8 lectures)
  • Processing fast data streams (~8 lectures)
    • Storm, Kafka
    • Streaming Analytics/Algorithms
    • Nimbus, R-Storm scheduling
    • Assignment 2 (20%)
  • Project topic selection
  • Research reading on distributed big data platforms (~6 lectures)
    • From: ACID/BASE, CAP Theorem, Dynamo DB, Distributed locking/ZooKeeper, P2P
  • Technical topics and guest lectures (~4 lectures)
    • Graph processing, NoSQL, MLLib/TensorFlow, IoT, Cloud computing
  • Final exam (30%)
  • Project presentation (20%)



  • Assignment 0
    • v1: [ds256.2018.A0_v1.txt] (Posted on Jan 16, 2018)
    • 50 points. Due on Jan 26, 2018 11:59PM.
    • Watch out for updates on datasets, running spark on turing, submission instructions, etc.


Project Topics

  • TBD

Public Datasets