Big Data

Course Details

Course Title:    BIG DATA

Course Code

MSCSC3001E04

Credits

4

L + T + P

3 + 1 + 0

Course Duration

One Semester

Semester

Odd

Contact Hours

45 (L) + 15 (T) Hours

Methods of Content Interaction

Lecture, Tutorials, self-study, assignments.

Assessment and Evaluation

·         30% - Continuous Internal Assessment (Formative in nature but also contributing to the final grades)

·         70% - End Term External Examination (University Examination)

 

Course Objectives                                                                                                                                                                         

·         Understand the concept and challenge of big data and why existing technology is inadequate to analyze the big data.

·         Collect, manage, store, query, and analyze various form of big data.

·         Gain hands-on experience on large-scale analytics tools to solve some open big data problems.

·         Understand the impact of big data for business decisions and strategy.

·         Understand, and practice big data analytics and machine learning approaches, which include the study of modern computing big data technologies and scaling up machine learning techniques focusing on industry applications

 

Learning Outcomes

·         Ability to identify the characteristics of datasets and compare the trivial data and big data for various applications.

·         Ability to select and implement machine learning techniques and computing environment that are suitable for the applications under consideration.

·         Ability to solve problems associated with batch learning and online learning, and the big data characteristics such as high dimensionality, dynamically growing data and in particular scalability issues. 

·         Ability to understand and apply scaling up machine learning techniques and associated computing techniques and technologies.

·         Ability to recognize and implement various ways of selecting suitable model parameters for different machine learning techniques.

·         Ability to integrate machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and map reduce.

 

 Prerequisites: Basic of Computer science, Algorithms, Data structure