CSEE4121 - Computer Systems for Data Science

Spring ‘20, Columbia University

Course Overview

Data scientists and engineers increasingly have access to a powerful and broad range of systems they use to conduct big data analysis and machine learning at scale: from databases, large-scale analytics to distributed machine learning frameworks. The goal of this class is to provide data scientists and engineers that work with big data a better understanding of the foundations of how the systems they will be using are built. It will also give them a better understanding of the real-world performance, availability and scalability challenges when using and deploying these systems at scale. In the course we will cover foundational ideas in designing these systems, while focusing on specific popular systems that students are likely to encounter at work or when doing research. The class will include some written homework and programming assignments. Some of the assignments will be done in groups. In this course we will answer the following questions:


Asaf Cidon
OH: Mondays 2:30-3:30 PM (By appointment only)

Location and Time

501 Northwest Corner Building.
Mondays 4:10pm - 6:40pm


(All Office Hours Held in the CS TA Room, Mudd 1st Floor)
Hongyi Wang – Mon 10am - 12pm
Qianrui Zhang – Tue 9am - 11am
Yujian Wu – Wed 2pm - 4pm
Junlin Song – Thu 12pm - 2pm
Ke Li – Thu 3pm - 5pm
Mingen Pan – Fri 4pm - 6pm



Students are expected to have solid programming experience in Python or with an equivalent programming language. This class is intended to be accessible for data scientists who do not necessarily have a background in databases, operating systems or distributed systems.

Schedule (this is a work in progress, and is likely to change)

Date Topic Homework
Jan 27 Introduction [pptx][pdf]
Feb 3 Infrastructure for Big Data [pptx] [pdf] Relational Model Part I [pptx] [pdf] Programming Homework 1 [TA-Solution]
Rescheduled: Feb 14 (Friday), 1:30-3:40 PM, 501 SCH (Schermerhorn) SQL and Relational Model [pptx] [pdf]
Rescheduled: Feb 17, 8:00 - 10:10 AM, 501 NWC Transactions [pptx] [pdf]
Feb 24 Transactions [pptx] [pdf] Storage and Memory Hierarchy [pptx] [pdf] Written Homework [TA-Solution]
Mar 2 DB Architecture [pptx] [pdf]
Mar 9 Cancelled
Mar 16 Spring Break
Mar 23 Cancelled
Mar 30 Key-value Stores and Single DB architecture [pptx] [pdf] Partitioning [pptx] [pdf] Programming Homework 2
Apr 6 Distributed File Systems and Transactions [pptx] [pdf]
Apr 13 MapReduce and Spark [pptx] [pdf]
Apr 20 Spark (continued) and Streaming [pptx] [pdf] Caching [pptx] [pdf]
Apr 27 Systems for Machine Learning [pptx] [pdf] Programming Homework 3
May 4 Security and Privacy [pptx] [pdf]

Grade Breakdown

20% Programming Homework 1
10% Written Homework
20% Programming Homework 2
10% Programming Homework 3
15% Midterm
25% Final

Collaboration/Copying Policy

Programming assignment 1 and the written assignment will be done alone. Programming assignments 2-3 will be done in pairs . You may not copy answers and code. We will enforce this policy when checking the assignments (we use a code similarity system).

Course Materials

No textbook.