Course Teacher : Sajal Halder, Lecturer, Dept. of CSE, Jagannath University
Office Location : Room no-602, New Academic Building, Jagannath University
Email Address : sajal{at}cse{dot}jnu{dot}ac{dot}bd
Day & Time : Monday & Thursday 1.30 pm - 3.00 pm
Credit for course: 3
Prerequisites : Students need to know at least one or more programming languages: C, C++, Java, Perl, Python, and/or Javascript to complete and final project.
Prerequisites Courses: Algorithms, Data Structures, Database Design and Data Mining Techniques
Course Objectives:
Students will learn how to analyze Big Data and gain latent knowledge from Big Data. It is a preliminary course for M.Sc students who are interested to learn Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments.
Now a days, Big Data Analytics is probably the fastest evolving issue in the IT world. New tools and algorithms are being created and adopted swiftly. Learn insight on what tools, algorithms, and platforms to use on which types of real world use cases.
Learner will get hands-on experience on Analytics, Mobile, Social and Security issues on Big Data through homework's and final project.
Final project reports will be submitted to international conference proceedings.
Description:
Big Data has become a novel norm of life because of advance of IT storage, processing, computation, and sensing technologies. Computers are able to capture and analysis all sorts of large-scale data from all kinds of fields like as people, behavior, information, devices, sensors, biological signals, finance, vehicles, astronology, neurology, etc. Therefore, industries collect Big data and want to dig out valuable information to get insight to solve their challenges.
This Big Data Analytics course shall first introduce the overview applications, market trend, and the things to learn. Then, we will learn the fundamental platforms, such as Hadoop, Spark, and other tools, such as IBM System G for Linked Big Data. Afterwards, the course will introduce several data storage methods and how to upload, distribute, and process them. This shall include HDFS, HBase, KV stores, document database, and graph database.
Students will choose the topics of their own interest for a final project. This will be a good opportunity for students to apply what's learned in the class for their needs, either for the future work requirements and research.
Course Outline