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M-SC in Data Science And Analytics at JAIN (Deemed-to-be University)
Bengaluru, Karnataka
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About the Specialization
What is Data Science and Analytics at JAIN (Deemed-to-be University) Bengaluru?
This M.Sc. Data Science program at JAIN University, Bengaluru, focuses on equipping students with advanced analytical skills to derive insights from complex datasets. Given India''''s booming digital economy and a surge in data-driven decision-making across sectors like e-commerce, finance, and healthcare, this program is meticulously designed to meet the growing demand for skilled data professionals in the Indian market, blending theoretical knowledge with practical, industry-relevant applications.
Who Should Apply?
This program is ideal for fresh graduates from computer science, mathematics, statistics, or engineering backgrounds seeking entry into the lucrative field of data science. It also caters to working professionals aiming to upskill in analytics or transition their careers into data-centric roles, offering a comprehensive curriculum that builds from foundational concepts to advanced techniques, preparing them for diverse challenges in the Indian tech landscape.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, or Data Consultant in leading Indian and multinational companies. Entry-level salaries typically range from INR 5-8 lakhs per annum, with experienced professionals earning significantly more. The program fosters critical thinking and problem-solving, aligning with certifications like AWS Certified Machine Learning Specialist and Google Professional Data Engineer, boosting growth trajectories in India''''s competitive job market.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop strong programming proficiency in Python/R by diligently practicing coding challenges weekly. Focus on data structures, algorithms, and libraries like NumPy and Pandas, crucial for data manipulation.
Tools & Resources
HackerRank, LeetCode, DataCamp, GeeksforGeeks, Jupyter Notebook
Career Connection
Essential for technical interviews and efficient data manipulation in any data science role, forming the bedrock of practical implementation.
Build a Solid Statistical & Mathematical Base- (Semester 1-2)
Thoroughly grasp concepts in linear algebra, calculus, probability, and inferential statistics. Utilize online courses and textbooks to supplement classroom learning and solve numerous practice problems to solidify understanding.
Tools & Resources
Khan Academy, NPTEL courses, The Elements of Statistical Learning by Hastie et al.
Career Connection
Forms the theoretical backbone for understanding, developing, and critically evaluating robust machine learning models and analytical results.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups to discuss complex topics, share insights, and collaborate on mini-projects. Present findings to peers to enhance communication, critical thinking, and teamwork skills, simulating real-world project environments.
Tools & Resources
GitHub for version control, Google Meet/Zoom for discussions, Microsoft Teams
Career Connection
Develops crucial soft skills for team-based projects in industry, improving overall problem-solving efficiency and professional interaction.
Intermediate Stage
Deep Dive into Machine & Deep Learning Frameworks- (Semester 3)
Move beyond theoretical understanding by practically implementing various ML and DL algorithms using frameworks like Scikit-learn, TensorFlow, and PyTorch. Experiment with different datasets, fine-tune models, and understand their practical applications.
Tools & Resources
Kaggle, Google Colab, Official documentation of Scikit-learn, TensorFlow, PyTorch
Career Connection
Direct application of core data science skills, preparing for roles requiring hands-on model development, experimentation, and deployment in industry.
Undertake Industry-Relevant Minor Projects- (Semester 3)
Actively seek out real-world problems or datasets from platforms like Kaggle or through faculty connections. Focus on end-to-end project development, from data cleaning to model deployment and comprehensive reporting.
Tools & Resources
GitHub, Cloud platforms (AWS/Azure/GCP free tiers), Project management tools like Trello
Career Connection
Builds a strong portfolio, demonstrates practical problem-solving abilities, and significantly enhances presentation skills for technical and HR interviews.
Network with Professionals and Attend Workshops- (Semester 3)
Attend data science meetups, webinars, and workshops organized by industry leaders or the university. Connect with professionals on LinkedIn to gain insights into industry trends, career paths, and potential internship opportunities.
Tools & Resources
LinkedIn, Meetup.com, University career events, Industry-specific conferences and hackathons
Career Connection
Opens doors to internships, mentorship, and future job opportunities, while ensuring you stay updated with evolving industry demands and technologies.
Advanced Stage
Execute a Comprehensive Major Project/Dissertation- (Semester 4)
Choose a challenging, novel problem that integrates multiple data science concepts. Dedicate significant time to thorough research, design, implementation, and detailed documentation. Aim for publication or presentation at a conference if possible.
Tools & Resources
Academic journals and research papers, Specialized software/tools relevant to the project, LaTeX for thesis writing
Career Connection
Showcases advanced research capabilities, complex problem-solving skills, and deep specialization, highly valued by top recruiters and for higher studies.
Actively Pursue Internships and Placement Preparation- (Semester 4)
Apply for internships at reputable companies to gain hands-on experience and corporate exposure. Prepare rigorously for placement interviews, focusing on data science case studies, technical questions, and behavioral rounds with mock interviews.
Tools & Resources
University placement cell, LinkedIn Jobs, Naukri.com, Glassdoor, Mock interview platforms and career counselors
Career Connection
Provides a direct pathway to full-time employment, offers invaluable practical industry experience, and builds a professional network even before graduation.
Focus on Ethical AI and Data Governance- (undefined)
Develop a strong understanding of the ethical implications of data science projects, data privacy regulations (like GDPR and Indian data protection laws), and principles of responsible AI. Incorporate these considerations into all project work and discussions.
Tools & Resources
NITI Aayog''''s AI strategy documents, Industry best practices for ethical AI, Legal frameworks on data privacy and security
Career Connection
Positions graduates as responsible and trustworthy data professionals, a critical skill set for navigating the complex regulatory and societal challenges in the modern data industry.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree in Science (with Mathematics/Statistics/Computer Science/Information Technology as one of the subjects) / Engineering / Technology / MCA from any recognized University with a minimum of 50% aggregate marks.
Duration: 2 years / 4 semesters
Credits: 96 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS101 | Mathematical Foundations for Data Science | Core | 4 | Linear Algebra, Calculus, Probability Theory, Statistical Inference, Optimization Techniques |
| MDS102 | Programming for Data Science (Python/R) | Core | 4 | Python/R Fundamentals, Data Structures, Functions and Modules, Object-Oriented Programming, Data Manipulation Libraries (Numpy, Pandas) |
| MDS103 | Database Management Systems | Core | 4 | SQL and Relational Databases, Database Design Principles, Normalization, Transaction Management, NoSQL Databases Introduction |
| MDS104 | Data Warehousing and Mining | Core | 4 | Data Warehousing Concepts, OLAP Operations, Data Mining Techniques, Association Rule Mining, Classification and Clustering |
| MDS105P | Programming for Data Science Lab | Lab | 3 | Python/R Programming Exercises, Data Cleaning and Preprocessing, Data Visualization with Libraries, Basic Statistical Computations, Debugging and Error Handling |
| MDS106P | Database & Data Mining Lab | Lab | 3 | SQL Queries and Joins, Database Schema Design, Implementation of Data Mining Algorithms, Using Data Mining Tools, Report Generation |
| HS101 | Soft Skills & Professional Ethics | Ability Enhancement | 2 | Communication Skills, Presentation Techniques, Teamwork and Collaboration, Ethics in Data Science, Professional Conduct |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS201 | Inferential Statistics and Hypothesis Testing | Core | 4 | Sampling Distributions, Estimation Theory, Parametric Hypothesis Tests, Non-parametric Tests, ANOVA |
| MDS202 | Machine Learning Fundamentals | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| MDS203 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, Hive and Pig, NoSQL Databases (Cassandra, MongoDB) |
| MDS204 | Data Visualization Techniques | Core | 4 | Principles of Data Visualization, Matplotlib and Seaborn, Interactive Visualizations (Plotly), Dashboarding Tools (Tableau/PowerBI), Storytelling with Data |
| MDS205P | Machine Learning Lab | Lab | 3 | Implementing Regression Models, Building Classification Models, Clustering Techniques, Cross-Validation, Hyperparameter Tuning |
| MDS206P | Big Data Lab | Lab | 3 | HDFS Commands and Operations, MapReduce Programming, Spark RDDs and DataFrames, HiveQL Queries, NoSQL Database Interaction |
| MDS207 | Research Methodology | Core | 2 | Research Problem Formulation, Literature Review, Research Design, Data Collection Methods, Report Writing and Referencing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS301 | Deep Learning | Core | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow/PyTorch) |
| MDS302 | Natural Language Processing | Core | 4 | Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Sequence Models (LSTMs, Transformers) |
| MDS303 | Cloud Computing for Data Science | Elective | 3 | Cloud Service Models (IaaS, PaaS, SaaS), AWS/Azure/GCP Data Services, Serverless Computing, Big Data Analytics on Cloud, MLOps Principles |
| MDS304 | Business Intelligence and Analytics | Elective | 3 | BI Architectures, Data Governance, Reporting and Dashboards, Predictive Analytics for Business, Key Performance Indicators (KPIs) |
| MDS305P | Deep Learning Lab | Lab | 3 | Implementing CNNs for Image Classification, Building RNNs for Sequence Prediction, Transfer Learning Applications, Hyperparameter Optimization for Deep Models, Model Deployment basics |
| MDS306PR | Minor Project | Project | 7 | Problem Identification, Data Collection and Preprocessing, Model Development and Evaluation, Project Documentation, Presentation Skills |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS401 | Advanced Machine Learning / Reinforcement Learning | Core | 4 | Ensemble Methods (Boosting, Bagging), Time Series Analysis, Anomaly Detection, Markov Decision Processes, Q-Learning and Policy Gradients |
| MDS402 | Data Governance and Ethics | Core | 4 | Data Privacy and Security, Regulatory Compliance (GDPR, Indian Data Laws), Ethical AI Principles, Data Quality Management, Data Lifecycle Management |
| MDS403E | Elective II (e.g., IoT Analytics / Financial Analytics / Health Informatics) | Elective | 3 | Domain-specific Data Acquisition, Specialized Analytical Techniques, Case Studies in chosen domain, Relevant Tools and Frameworks, Industry Applications |
| MDS404 | Seminar | Core | 3 | Technical Presentation Skills, Literature Review on Advanced Topics, Research Paper Analysis, Public Speaking, Question and Answer Handling |
| MDS405PR | Major Project / Dissertation | Project | 10 | Independent Research and Development, Complex Problem Solving, System Design and Implementation, Comprehensive Documentation (Thesis), Viva Voce and Presentation |
