2 Years
Post Graduate
Management
The MBA (Data Analytics) syllabus at Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar is designed to provide overall knowledge to the students with a strong foundation. MBA (Data Analytics) faculty at Kalinga Institute of Industrial Technology (KIIT) specially focus on in-depth learning to relevant subjects. At first semester syllabus of MBA (Data Analytics) at Kalinga Institute of Industrial Technology (KIIT), students learn the basics of programme. A strong foundation is very important for comprehensive learning. MBA (Data Analytics) syllabus at Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar maintains a balance between theoretical knowledge and practical knowledge.
MBA (Data Analytics) first year students at Kalinga Institute of Industrial Technology (KIIT) are introduced with core subjects. Then they are encouraged to explore other area for a broader perspective. Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar also provides practical training sessions, workshops, projects, and case studies to enhance student skills. MBA (Data Analytics) syllabus at Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar is also frequently updated to give industry relevant training and knowledge to students. Kalinga Institute of Industrial Technology (KIIT) strives to provide a nurturing environment where students can learn new skills. The hands-on training sessions at Kalinga Institute of Industrial Technology (KIIT) enable MBA (Data Analytics) students to apply knowledge and skills in a controlled environment and get required experience.
According to syllabus of MBA (Data Analytics) progress, students learn advanced topics and complex concepts. The MBA (Data Analytics) curriculum at Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar mainly focuses on analytical and critical thinking. As the MBA (Data Analytics) course unfolds, students develop several important skills that increases their employability. As per syllabus of MBA (Data Analytics) at Kalinga Institute of Industrial Technology (KIIT) also includes real-life projects and internship programs. It helps students critical thinking and gives them real-world experience.
MBA (Data Analytics) curriculum at Kalinga Institute of Industrial Technology (KIIT) includes group discussions, guest lectures, case studies, and skill development workshops to enhance the learning experience. The MBA (Data Analytics) syllabus at Kalinga Institute of Industrial Technology (KIIT) aims to create well-rounded professionals equipped with the necessary skills and knowledge to succeed in their chosen fields.
Additional curriculum at Kalinga Institute of Industrial Technology (KIIT)
Note: Given below syllabus is based on the available web sources. Please verify with the Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar for latest MBA (Data Analytics) curriculum.
| Semester 1 | Semester 2 |
| Data Analytics Foundations | Machine Learning for Data Analytics |
| Statistical Methods for Business Analytics | Big Data Analytics and Management |
| Managerial Economics | Operations Research for Data Analytics |
| Financial Accounting for Analytics | Marketing Analytics |
| Organizational Behavior and Leadership | Data Visualization and Communication |
| Business Communication Skills | Database Management and SQL |
| Business Analytics Capstone Project (Part 1) | Business Analytics Capstone Project (Part 2) |
| Semester 3 | Semester 4 |
| Predictive Analytics and Modeling | Text Analytics and Natural Language Processing |
| Data Mining Techniques | Data Governance and Ethics |
| Supply Chain Analytics | Customer Analytics and Relationship Management |
| Financial Analytics | Social Media Analytics |
| Strategic Management for Analytics | Strategic Analytics and Decision Making |
| Business Analytics Capstone Project (Part 3) | Business Analytics Capstone Project (Part 4) |
Projects
Throughout the MBA in Data
Analytics program, students are required to work on various projects to
apply their skills and knowledge in real-world scenarios. These projects aim to
enhance their analytical abilities, problem-solving skills, and decision-making
capabilities.
The projects can involve tasks such as data analysis, data
visualization, predictive modeling, and business strategy formulation based on
data insights. Students typically work individually or in teams to complete
these projects, which may culminate in a final capstone project where they
showcase their skills and present their findings to faculty and industry
experts.
Reference Books
(1). "Data Science for Business" by Foster Provost and Tom
Fawcett
(2). "Python for Data Analysis" by Wes McKinney
(3). "R for Data Science" by Hadley Wickham and Garrett
Grolemund
(4). "Big Data: A Revolution That Will Transform How We Live, Work,
and Think" by Viktor Mayer-Schönberger and Kenneth Cukier
(5). "Predictive Analytics: The Power to Predict Who Will Click,
Buy, Lie, or Die" by Eric Siegel
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