Data-intensive Computing

CSE4/587 Spring 2018

"Hmm..What will I learn in this course?"


Welcome to Spring 2017 and Data-intensive Computing course. This course covers topics that are relevant to the emerging area of Data Science and Data-intensive Computing. Data Science deals with data acquisition, cleaning, exploratory data analysis, statistical modeling, algorithmic data processing, knowledge extraction, prediction and prescriptive analytics. Data-intensive computing deals with computing aspects such as the infrastructure, big-data architectures, data structures and algorithms that enable the Data Science. We will cover both aspects in this course.

Main text book for the course is:
F. Cady. The Data Science Handbook. Wiley. 2017. ISBN: 9781119092940.

We will be using many other references, online sources and textbooks throughout the semester. The details will be provided in the References tab.

Tentative Curriculum

A broad overview of the topics to be covered is given below.

		 Introduction to Data.
		 Data Aqusition and cleaning.
		 Exploratory Data Analysis (EDA) using R Language and Python.
		 Data Visualization.
		 Statistical modeling.
		 Algorithms for big-data processing.
		 Data bases for small-data and big-data.
		 Infrastructures for big-data (Hadoop Eco-system).
		 High speed, scalable big-data prcessing (Spark Eco-system).
		 Computing on the cloud.
		 Basics of blockchain technology.
		 Research issues in Data-intensive computing.


All concepts discussed during the lectures will be reinforced by 3 labs.