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M-SC in Data Science at CHRIST (Deemed to be University)
Bengaluru, Karnataka
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About the Specialization
What is Data Science at CHRIST (Deemed to be University) Bengaluru?
This M.Sc Data Science program at CHRIST (Deemed to be University) focuses on equipping students with advanced analytical and computational skills vital for the rapidly evolving Indian data industry. It emphasizes a strong foundation in statistics, machine learning, and big data technologies, preparing graduates for cutting-edge roles in various sectors, from finance to e-commerce, meeting the high demand for data professionals across India.
Who Should Apply?
This program is ideal for fresh graduates with a background in Computer Science, IT, Mathematics, Statistics, or Engineering seeking entry into the data science field. It also caters to working professionals aiming to upskill in analytics, machine learning, and AI, enabling career transitions or advancements. Aspirants with a strong analytical aptitude and a desire to solve complex real-world problems through data are well-suited.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Scientists, Machine Learning Engineers, Data Analysts, or AI Specialists, with entry-level salaries typically ranging from INR 6-10 LPA, growing significantly with experience. The curriculum is designed to align with industry certifications, fostering a strong foundation for professional growth within major Indian IT firms, startups, and multinational corporations operating in India.
Student Success Practices
Foundation Stage
Master Programming & Statistical Foundations- (Semester 1-2)
Dedicate significant time to mastering Python (NumPy, Pandas) and fundamental statistical concepts. Regularly solve problems on platforms like HackerRank and Kaggle, focusing on data manipulation and statistical hypothesis testing. Collaborate with peers on small data analysis projects to apply theoretical knowledge.
Tools & Resources
Python, Jupyter Notebook, NumPy, Pandas, scikit-learn, CodeChef, GeeksforGeeks, Khan Academy
Career Connection
A strong base in programming and statistics is non-negotiable for any data science role, forming the bedrock for advanced machine learning and AI applications, directly impacting internship and entry-level job opportunities.
Build a Strong Data Science Portfolio- (Semester 1-2)
Begin working on mini-projects from publicly available datasets (e.g., UCI Machine Learning Repository, Kaggle). Document your code and findings thoroughly. Create a GitHub profile to showcase your projects and contributions, making them accessible to potential employers and for peer review.
Tools & Resources
GitHub, Kaggle, UCI Machine Learning Repository, Google Colab
Career Connection
A well-curated portfolio demonstrates practical skills and initiative, significantly boosting your profile during internship and placement drives, especially for roles requiring hands-on experience.
Engage in Peer Learning & Academic Competitions- (Semester 1-2)
Form study groups with classmates to discuss complex topics, share insights, and prepare for exams. Participate in internal university data hackathons or competitive programming challenges. Such engagements enhance problem-solving skills and expose you to diverse approaches.
Tools & Resources
University Computing Labs, Online forums (Stack Overflow), Competitive programming platforms
Career Connection
Teamwork and competitive spirit are highly valued in industry. Participation in such events showcases your ability to work under pressure and collaborate, which are crucial for project-based roles.
Intermediate Stage
Dive Deep into Specialised Domains- (Semester 3)
Focus on practical application of Machine Learning, Deep Learning, and NLP concepts. Undertake projects that involve real-world datasets and problems in these areas. Explore frameworks like TensorFlow, Keras, and PyTorch in depth. Seek out industry mentors or faculty for guidance on complex implementations.
Tools & Resources
TensorFlow, PyTorch, Keras, Hugging Face, Google AI Platform, Coursera/edX advanced courses
Career Connection
Specialised skills in Deep Learning and NLP are highly sought after in advanced data science roles in product development, research, and AI divisions of companies like Amazon India, Flipkart, and TCS.
Secure and Excel in Internships- (Semester 3)
Actively search for and apply to internships at data-driven companies. During the internship, immerse yourself in the project, take initiative, and network with professionals. Apply classroom knowledge to real-world scenarios and document your learnings meticulously for your resume.
Tools & Resources
LinkedIn, Internshala, College Placement Cell, Industry contacts
Career Connection
Internships are crucial for gaining industry experience, building a professional network, and often lead to pre-placement offers, significantly easing the job search process post-graduation in the competitive Indian market.
Attend Workshops & Industry Seminars- (Semester 3)
Actively participate in workshops, webinars, and industry seminars organized by professional bodies or industry leaders. Stay updated with the latest trends, tools, and research in data science. These events provide networking opportunities and expose you to diverse perspectives and potential career paths.
Tools & Resources
Meetup, Data Science Conferences (e.g., Cypher, GIDS), Tech forums
Career Connection
Staying current with industry trends and networking can open doors to new opportunities, help in understanding industry expectations, and make you a more well-rounded candidate for a variety of roles.
Advanced Stage
Execute an Impactful Major Project- (Semester 4)
Choose a challenging and relevant major project that addresses a real-world problem or contributes to academic research. Focus on demonstrating a complete data science pipeline from problem definition and data collection to model deployment and evaluation. Aim for publishable quality if possible.
Tools & Resources
Advanced ML/DL frameworks, Cloud platforms (AWS, Azure, GCP), Version control (Git), Academic journals, Research papers
Career Connection
A strong major project is your ultimate showcase, demonstrating your ability to lead and execute complex data science initiatives, significantly influencing your employability for senior analyst or junior data scientist roles.
Master Interview & Presentation Skills- (Semester 4)
Practice technical interviews covering algorithms, data structures, SQL, probability, and machine learning concepts. Refine your communication and presentation skills, especially for explaining complex technical topics to diverse audiences. Participate in mock interviews arranged by the placement cell.
Tools & Resources
LeetCode, HackerRank (SQL, ML), Pramp, InterviewBit, University career services
Career Connection
Excellent interview and presentation skills are critical for converting opportunities into job offers, particularly in the highly competitive Indian IT and data analytics sector where communication is key for client-facing or team lead roles.
Strategic Career Planning & Networking- (Semester 4)
Clearly define your career goals (e.g., Data Scientist, ML Engineer). Tailor your resume and LinkedIn profile to these goals. Network extensively with alumni and industry professionals through conferences, LinkedIn, and university events to explore job prospects and gain insights into different roles and companies.
Tools & Resources
LinkedIn, Alumni network, Professional data science communities, Career counselors
Career Connection
Proactive career planning and networking are essential for identifying suitable job roles, gaining referrals, and navigating the nuances of the Indian job market, leading to more targeted and successful placement outcomes.
Program Structure and Curriculum
Eligibility:
- A pass in Bachelor’s degree with 60% aggregate marks in Computer Science/ Computer Applications/ Information Technology/ B.Voc in Software Development/ B.Voc in IT/ Mathematics/ Statistics/ Electronics/ Physics/ BCA/ B.Sc. (IT)/ B.Sc. (CS)/ BE/ B.Tech from any recognised University in India or abroad. (Applicants who are in the final year of their studies should have 60% or above aggregate in all the semesters/years of undergraduate examinations)
Duration: 2 years (4 semesters)
Credits: 76 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS101 | Linear Algebra for Data Science | Core | 4 | Vector Spaces and Subspaces, Linear Transformations and Matrices, Eigenvalues and Eigenvectors, Matrix Decompositions, Principal Component Analysis |
| DS102 | Python for Data Science | Core | 4 | Python Programming Fundamentals, Data Structures and Control Flow, Functions and Modules, NumPy for Numerical Computing, Pandas for Data Manipulation |
| DS103 | Probability and Statistics for Data Science | Core | 4 | Probability Theory and Distributions, Random Variables, Hypothesis Testing, ANOVA, Regression Analysis |
| DS104 | Data Warehousing and Data Mining | Core | 4 | Data Warehousing Concepts, OLAP Operations, Data Mining Techniques, Association Rule Mining, Classification and Clustering |
| DS105 | Research Methodology and IPR | Core | 2 | Research Design and Methods, Data Collection and Analysis, Report Writing and Presentation, Intellectual Property Rights, Patents and Copyrights |
| DS1L1 | Data Science Lab - I (Python) | Lab | 2 | Python Programming Exercises, NumPy and Pandas Applications, Data Cleaning and Preprocessing, Basic Data Visualization, File Handling and Data Import |
| DS1F1 | Foundation Course | Foundational | 1 | Value Education Principles, Indian Constitution Fundamentals, Environmental Studies, Ethics and Morality, Social Responsibility |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS201 | Artificial Intelligence and Machine Learning | Core | 4 | Introduction to AI, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Evaluation and Validation, Bias and Variance |
| DS202 | Database Management System for Data Science | Core | 4 | Relational Database Concepts, SQL Queries and Joins, NoSQL Databases, Data Modeling, Database Security and Integrity |
| DS203 | Big Data Technologies | Core | 4 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, Big Data Storage and Processing, Stream Processing |
| DS204 | Data Visualization Techniques | Core | 4 | Principles of Data Visualization, Types of Charts and Graphs, Interactive Dashboards, Data Storytelling, Tools: Tableau, PowerBI, Matplotlib, Seaborn |
| DS2E1.1 | Data Analysis using R | Elective | 3 | R Programming Basics, Data Structures in R, Data Manipulation with dplyr, Statistical Graphics with ggplot2, Statistical Modeling in R |
| DS2L2 | Data Science Lab - II (Machine Learning) | Lab | 2 | Implementing ML Algorithms, Model Training and Testing, Hyperparameter Tuning, scikit-learn Library Usage, Cross-Validation Techniques |
| DS2L3 | Minor Project - I | Project | 2 | Problem Identification and Scoping, Data Collection and Preprocessing, Methodology Design, Implementation and Results, Project Report and Presentation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS301 | Deep Learning | Core | 4 | Neural Networks Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow, Keras), Transfer Learning and Fine-tuning |
| DS302 | Natural Language Processing | Core | 4 | NLP Fundamentals and Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Text Classification, Sentiment Analysis, Language Models and Transformers |
| DS303 | Business Intelligence and Analytics | Core | 4 | Business Intelligence Concepts, Data Integration and ETL, Reporting and Dashboarding Tools, Predictive Analytics in Business, Data-Driven Decision Making |
| DS3E2.1 | Cloud Computing for Data Science | Elective | 3 | Cloud Computing Paradigms, Cloud Services (IaaS, PaaS, SaaS), Data Storage and Processing in Cloud, Serverless Computing, Cloud Security and Governance |
| DS3L4 | Data Science Lab - III (Deep Learning & NLP) | Lab | 2 | Deep Learning Model Implementation, NLP Task Execution, Text Preprocessing Techniques, Embedding Techniques, Model Training and Evaluation |
| DS3P1 | Internship | Internship | 4 | Industry Project Execution, Application of Data Science Skills, Professional Communication, Report Writing and Presentation, Problem-Solving in Industry Setting |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS401 | Ethics and Governance in Data Science | Core | 2 | Data Ethics and Privacy, Bias and Fairness in AI, Data Governance Frameworks, Regulatory Compliance (GDPR, Indian Context), Responsible AI Development |
| DS4P2 | Major Project | Project | 12 | Project Proposal and Literature Review, System Design and Architecture, Implementation and Experimentation, Results Analysis and Discussion, Thesis Writing and Viva Voce |
