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95-869 Big Data and Large Scale Computing
Fall 2017




Time: Tue & Thu 10:30AM - 11:50AM
Place: HBH 2008


Instructor: Leman Akoglu, ( lakoglu @ andrew )
  • Office: HBH 2118C, office ph 412-268-30 four three
  • Office hours: Tue 1-2 PM; also, by appointment

Teaching Assistant: Abhinav Maurya, ( amaurya @ andrew )
  • Office: HBH 3034
  • Office hours: Mon 1:30-3:30 PM

Grader: Sathwik Chenna Madhavuni, ( schennam @ andrew )
  • Office hours: by appointment only


The rate and amount of data being generated in today's world by both humans and machines are unprecedented. Being able to store, manage, and analyze large-scale data has critical impact on business intelligence, scientific discovery, social and environmental challenges.

The goal of this course is to equip students with the understanding, knowledge, and practical skills to develop big data / machine learning solutions with the state-of-the-art tools, particularly those in the Spark environment, with a focus on programming models in MLlib, GraphX, and SparkSQL. See the syllabus for more details. Students will also gain hands-on experience with MapReduce and Apache Spark using real-world datasets.

This course is designed to give a graduate-level student a thorough grounding in the technologies and best practices used in big data machine learning. The course assumes that the students have the understanding of basic data analysis and machine learning concepts as well as basic knowledge of programming (preferably in Python or Java). Previous experience with Hadoop, Spark or distributed computing is NOT required.

Learning Objectives

By the end of this class, students will
  • gain understanding of the MapReduce paradigm and Hadoop ecosystem
  • understand scalability challenges for common ML tasks
  • study distributed machine learning algorithms
  • understand details of SparkSQL, GraphX, and MLlib (Spark's ML library)
  • implement distributed pipelines in Apache Spark using real datasets


There is no official textbook for the course. I will post all the lecture notes and several readings on course website.
Below you can find a list of recommended reading.

BULLETIN BOARD and other info

  • We will use the Canvas for course materials, homework deposits, announcements, and grades.
  • We will use Piazza for questions and discussions.
  • Carnegie Mellon 2017-2018 Official academic calendar


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