CSE4/587 Spring 2018

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.

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

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