(M.Tech.) Master of Technology in Data Analytics Syllabus

  • course years 2 Years
  • type of course Post Graduate
  • course stream Engineering
  • course type Full Time

Explore advanced topics in data analytics. Study data mining, machine learning, big data technologies, and more. Develop expertise in extracting insights from large datasets.

Syllabus and Subjects of Master of Technology in Data Analytics

The Master of Technology (M.Tech.) in Data Analytics program is an advanced and comprehensive course designed to meet the growing demand for skilled data professionals in today's data-driven industries. In an era where data is considered the new currency, organizations across sectors rely heavily on data analytics to make informed decisions, optimize processes, and gain a competitive edge. This program provides students with a deep understanding of data analytics techniques, tools, and technologies, equipping them with the expertise to mine, interpret, and leverage data effectively.

The course curriculum covers a wide spectrum of topics, including data preprocessing, statistical analysis, machine learning, big data technologies, and data visualization. Moreover, students delve into real-world applications, exploring how data analytics can be applied to domains such as finance, healthcare, marketing, and more. With a blend of theoretical knowledge and practical skills, graduates of this program are well-prepared to embark on dynamic careers as data analysts, data scientists, business intelligence professionals, or consultants.

Here is a comprehensive overview of the syllabus and subjects covered in the Master of Technology in Data Analytics program:

S.noSubjects
1Statistical Computing
2Big Data Analytics
3Machine Learning Technique                                                                                                             
4Real Time Systems
5Next Generation Databases

2nd Year OR 3rd & 4th Semester Syllabus of Master of Technology (M.Tech.) in Data Analytics

S.NoSubjects
1High-Performance Computing
2Digital Forensics 
3Natural Language Computing
4Data Acquisition and Productization                                                                                                   
5Project Work

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