Course Overview
The Master of Science in Data Science program is a rigorous, interdisciplinary program designed to equip students with the theoretical knowledge and practical skills needed to excel in the rapidly evolving field of data science. Students will learn to extract meaningful insights from complex datasets using cutting-edge techniques in statistical modeling, machine learning, data visualization, and big data analytics. The program emphasizes hands-on experience through real-world projects, case studies, and internships, preparing graduates for high-demand roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Business Intelligence Analyst across diverse industries like finance, healthcare, e-commerce, and technology.
This program caters to both recent graduates and working professionals seeking to advance their careers in data science. The curriculum incorporates a strong foundation in mathematics, statistics, and programming, while also delving into specialized areas like deep learning, natural language processing, and computer vision. Graduates of this program will be well-positioned to address complex business challenges, drive innovation, and contribute to data-driven decision-making in their chosen fields.
Course Information
| COURSE NAME | DURATION | FEES (IN ₹) | UNIVERSITY |
|---|---|---|---|
| Master of Science in Data Science | 2 years | 5,00,000 | University of Oslo |
Curriculum
| YEAR/SEMESTER | SUBJECTS/MODULES | DESCRIPTION |
|---|---|---|
| Semester 1: | Data Visualization | learning_outcomes: Develop strong mathematical foundations for data analysis; learning_outcomes: Apply statistical techniques for data interpretation; learning_outcomes: Master programming languages used in data science; learning_outcomes: Communicate data insights effectively through visualizations |
| Semester 2: | Big Data Analytics | learning_outcomes: Build predictive models using machine learning algorithms; learning_outcomes: Manage and manipulate large datasets using database technologies; learning_outcomes: Extract patterns and knowledge from large datasets; learning_outcomes: Analyze massive datasets using distributed computing frameworks |
| Semester 3: | Data Science Capstone Project | learning_outcomes: Develop deep learning models for complex tasks; learning_outcomes: Analyze and process text data; learning_outcomes: Utilize cloud platforms for data storage and analysis; learning_outcomes: Apply learned skills to a real-world data science problem |
| Semester 4: | Internship/Research Project | learning_outcomes: Specialize in a chosen area of data science; learning_outcomes: Gain deeper knowledge in a specific data science domain; learning_outcomes: Gain practical experience in a data science role |
Eligibility Criteria
Bachelor's degree in any discipline with a minimum of 50% aggregate marks. Strong background in mathematics and statistics is preferred. Some universities may require entrance exams like GATE, or institute-specific entrance tests.
Admission Process
1. Online application submission through the university portal.
2. Submission of required documents (transcripts, entrance exam scores, letters of recommendation).
3. Shortlisting of candidates based on academic performance and entrance exam scores.
4. Interview process for shortlisted candidates.
5. Admission offer based on overall performance.
6. Fee payment and enrollment.