MIS5600
DATA STRUCTURE & BIG DATA COMP
- Department
- Management Information Systems
- Terms offered
- 2
- Data refreshed
- Jun 27, 2026
Description
Section information text: The astounding growth of data in all aspects of life in the form of emails, weblogs, tweets, sensors, videos and text has necessitated the use of Big Data and advanced analytics techniques to support large scale data analytics. The goal of this course is to enable students to design and build Big Data applications through highly scalable systems capable of collecting, processing, storing and analyzing large volumes of structured and unstructured data. By extending the Cross Industry Standard Process for Data Mining (CRISP-DM) to build Big Data applications using distributed and parallel computing architecture, this course brings together key Big Data tools on Hadoop platforms such as pig, hive, R-Hadoop, flume, and SQL-MR. Students will learn how to efficiently manage data with three main characteristics: volume, velocity and variety. Topics include the Hadoop platforms such as Cloudera Hadoop, Teradata Aster and IBM Infosphere Streams, and analytics techniques such as social media analytics, link analysis, and stream analytics.
Syllabi
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Offerings and forecast 2018–2019
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: 7% chance, medium confidence. Offered in 0 of 9 fall terms since 2018. | 2030 Spring: 13% chance, medium confidence. Offered in 2 of 9 spring terms since 2018. | 2030 Summer: 7% chance, medium confidence. Offered in 0 of 9 summer terms since 2018. | 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020. |
| 2029 | 2029 Fall: 6% chance, medium confidence. Offered in 0 of 10 fall terms since 2017. | 2029 Spring: 13% chance, medium confidence. Offered in 2 of 9 spring terms since 2018. | 2029 Summer: 6% chance, medium confidence. Offered in 0 of 10 summer terms since 2017. | 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2028 | 2028 Fall: 5% chance, medium confidence. Offered in 0 of 11 fall terms since 2016. | 2028 Spring: 12% chance, medium confidence. Offered in 2 of 9 spring terms since 2018. | 2028 Summer: 5% chance, medium confidence. Offered in 0 of 11 summer terms since 2016. | 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2027 | 2027 Fall: 5% chance, high confidence. Offered in 0 of 12 fall terms since 2015. | 2027 Spring: 12% chance, medium confidence. Offered in 2 of 9 spring terms since 2018. | 2027 Summer: 5% chance, high confidence. Offered in 0 of 12 summer terms since 2015. | 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2019 | Not offered | 1 section | Not offered | No data |
| 2018 | Not offered | 1 section | Not offered | No data |
Meeting times
Recent firstNo meeting times.
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Spring 2019 | Pankush Kalgotra | Lecture | 1 | 13 of 15 enrolled | 36328 |
| Spring 2018 | Pankush Kalgotra | Lecture | 1 | 13 of 15 enrolled | 36328 |