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M-SC in Data Science at Bishop Heber College
Tiruchirappalli, Tamil Nadu
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About the Specialization
What is Data Science at Bishop Heber College Tiruchirappalli?
This M.Sc Data Science program at Bishop Heber College focuses on equipping students with advanced analytical and computational skills to thrive in the data-driven world. The curriculum is designed to meet the evolving demands of the Indian industry, emphasizing practical applications of machine learning, big data analytics, and artificial intelligence. It prepares graduates for challenging roles in various sectors, from IT to finance, with a strong theoretical and practical foundation.
Who Should Apply?
This program is ideal for fresh graduates with a background in Computer Science, IT, or related engineering fields who aspire to build a career in data science. It also caters to working professionals seeking to upskill in analytics or transition into data-centric roles. Candidates should possess a strong foundational understanding of mathematics and programming, eager to delve into complex data challenges and innovative solutions.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, and Business Intelligence Developer. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals earning significantly more. The program fosters skills aligned with professional certifications, leading to strong growth trajectories in leading Indian and global companies operating within India.
Student Success Practices
Foundation Stage
Master Programming & Data Structures- (Semester 1-2)
Dedicate significant time to mastering Python, SQL, and core data structures and algorithms. Participate in coding challenges regularly. Understanding these fundamentals thoroughly will be crucial for advanced topics like machine learning and big data.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, SQL Zoo
Career Connection
Strong programming skills are the bedrock for any data science role, crucial for coding interviews and efficient data manipulation in projects, leading to better placement opportunities.
Build a Strong Mathematical Foundation- (Semester 1)
Actively revisit and strengthen your understanding of linear algebra, probability, and statistics. These mathematical concepts underpin all machine learning algorithms. Utilize online courses and textbooks for supplementary learning.
Tools & Resources
Khan Academy, NPTEL courses (Probability & Statistics, Linear Algebra), Introduction to Statistical Learning textbook
Career Connection
A solid mathematical base enables deeper understanding of algorithms, critical for model selection, interpretation, and troubleshooting, essential for an effective data scientist.
Engage in Data Exploration Projects- (Semester 1-2)
Start working on small data exploration and visualization projects using publicly available datasets. Focus on understanding data cleaning, preprocessing, and basic descriptive analytics. Present your findings to peers for feedback.
Tools & Resources
Kaggle datasets, UCI Machine Learning Repository, Google Dataset Search, Jupyter Notebook, Pandas, Matplotlib, Seaborn
Career Connection
Early exposure to real datasets develops critical thinking, problem-solving skills, and a portfolio, making you more attractive for entry-level data analyst and junior data scientist roles.
Intermediate Stage
Deepen Machine Learning & Big Data Application- (Semester 2-3)
Beyond theoretical understanding, implement various machine learning algorithms from scratch or using libraries like scikit-learn. Work on projects involving big data tools like Hadoop and Spark. Explore different model architectures and hyperparameter tuning.
Tools & Resources
Scikit-learn documentation, TensorFlow/PyTorch tutorials, Apache Hadoop/Spark documentation, Databricks Community Edition
Career Connection
Practical expertise in ML and Big Data tools is highly valued. This stage builds a portfolio of applied projects, which is critical for securing roles like ML Engineer or Big Data Analyst.
Participate in Hackathons and Competitions- (Semester 2-3)
Actively participate in data science hackathons and Kaggle competitions. This provides hands-on experience with real-world problems, fosters teamwork, and helps build a competitive profile demonstrating practical skills.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for collaboration
Career Connection
Success or even participation in such events demonstrates problem-solving abilities under pressure, practical skill application, and distinguishes you during placements and interviews.
Build a Professional Network & Personal Brand- (Semester 2-4 (Ongoing))
Attend industry webinars, workshops, and local meetups related to data science. Connect with professionals and alumni on platforms like LinkedIn. Start a blog or contribute to open-source projects to showcase your learning journey and expertise.
Tools & Resources
LinkedIn, Meetup.com, professional data science communities, personal blog platform (e.g., Medium, GitHub Pages)
Career Connection
Networking opens doors to internship and job opportunities, mentorship, and keeps you updated on industry trends. A strong personal brand enhances visibility and credibility for future career prospects.
Advanced Stage
Undertake an Industry-Relevant Capstone Project- (Semester 3-4)
Collaborate with faculty or industry mentors on a substantial capstone project that solves a real-world business problem. Focus on end-to-end implementation, from data acquisition and modeling to deployment and impact analysis.
Tools & Resources
Cloud platforms (AWS, Azure, GCP), Docker, Git, project management tools
Career Connection
A strong, well-executed capstone project is the highlight of your resume, showcasing your ability to deliver comprehensive data science solutions and directly impacting placement opportunities.
Specialize and Gain Advanced Certifications- (Semester 3-4)
Identify a specific area within data science (e.g., NLP, Computer Vision, MLOps) that aligns with your career goals and delve deeper. Consider pursuing industry-recognized certifications to validate specialized skills.
Tools & Resources
Coursera/edX specializations, AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer
Career Connection
Specialization makes you a more targeted candidate for specific roles and industries. Certifications provide a competitive edge and demonstrate commitment to continuous learning in a rapidly evolving field.
Master Interview Skills and Placement Preparation- (Semester 4)
Prepare rigorously for technical and behavioral interviews. Practice coding questions, revise core data science concepts, and work on articulating your projects effectively. Participate in mock interviews and career counseling sessions.
Tools & Resources
InterviewBit, LeetCode, Glassdoor, college placement cell, alumni network
Career Connection
This direct preparation is vital for converting your academic and project work into successful job offers during campus placements or off-campus recruitment drives, ensuring a smooth career transition.
Program Structure and Curriculum
Eligibility:
- A candidate who has passed B.Sc. in Computer Science / Information Technology / Computer Technology / Software Development / Data Science / BCA / B.E. / B.Tech. in Computer Science Engineering / Information Technology / Software Engineering with at least a second class or equivalent grade of Bharathidasan University or any other University recognized by the Syndicate as equivalent thereto.
Duration: 2 years (4 semesters)
Credits: 96 Credits
Assessment: Internal: 25%, External: 75%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC101 | Mathematical Foundation for Data Science | Core | 4 | Probability and Statistics, Linear Algebra, Matrix Algebra, Differential Equations, Optimization |
| DSCC102 | Data Structures and Algorithms | Core | 4 | Array, Linked List, Stack, Queue, Trees, Graphs, Sorting Algorithms, Searching Algorithms, Algorithm Analysis |
| DSCC103 | Advanced Python Programming | Core | 4 | Python Fundamentals, Data Structures in Python, Functions, Modules, Packages, Object-Oriented Programming in Python, File Handling and Exception Handling, Web Scraping |
| DSCC104 | Database Management Systems | Core | 4 | DBMS Concepts, ER Model, Relational Model, SQL Commands, Normalization, Transaction Management |
| DSCP105 | Advanced Python Programming Lab | Core | 4 | Python programming exercises, Implementation of data structures, File operations, GUI programming, Database connectivity |
| DSCP106 | Database Management Systems Lab | Core | 4 | SQL DDL, DML, DCL commands, Advanced SQL queries, PL/SQL programming, Database design and implementation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC207 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Association), Reinforcement Learning Basics, Model Evaluation and Selection |
| DSCC208 | Big Data Analytics | Core | 4 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), NoSQL Databases, Hive and Pig, Spark and Stream Processing |
| DSCC209 | Cloud Computing and IoT | Core | 4 | Cloud Computing Paradigms, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Introduction to IoT, IoT Architecture and Protocols |
| DSCE210A | Web Data Analytics | Elective | 4 | Web Analytics Fundamentals, Web Crawling and Scraping, Web Mining Techniques, Social Network Analysis, Web Data Visualization |
| DSCE210B | Deep Learning | Elective | 4 | Introduction to Neural Networks, Perceptrons and Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM and Transfer Learning |
| DSCP211 | Machine Learning Lab | Core | 4 | Implementation of Supervised Learning algorithms, Implementation of Unsupervised Learning algorithms, Data preprocessing and feature engineering, Model evaluation using Python libraries, Scikit-learn applications |
| DSCP212 | Big Data Analytics Lab | Core | 4 | Hadoop installation and configuration, MapReduce programming, Hive and Pig scripting, Spark programming, Working with NoSQL databases |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCC313 | Natural Language Processing | Core | 4 | NLP Fundamentals, Text Preprocessing, N-grams and Language Models, POS Tagging and Named Entity Recognition, Sentiment Analysis |
| DSCC314 | Data Visualization | Core | 4 | Principles of Data Visualization, Data Storytelling, Matplotlib and Seaborn, Interactive Visualization tools, Tableau and Power BI concepts |
| DSCE315A | Image and Video Analytics | Elective | 4 | Digital Image Processing Basics, Image Feature Extraction, Object Recognition and Detection, Image Classification, Video Processing Techniques |
| DSCE315B | Reinforcement Learning | Elective | 4 | Markov Decision Process, Bellman Equation, Q-Learning, Deep Q-Networks, Policy Gradient Methods |
| DSCC316 | Research Methodology | Core | 4 | Research Design and Types, Data Collection Methods, Sampling Techniques, Hypothesis Testing, Report Writing and Ethics in Research |
| DSCP317 | Natural Language Processing Lab | Core | 4 | NLTK and SpaCy usage, Text classification exercises, Sentiment analysis implementation, Chatbot development, Named Entity Recognition tasks |
| DSCP318 | Data Visualization Lab | Core | 4 | Creating plots with Matplotlib and Seaborn, Interactive dashboards using Plotly, Data storytelling exercises, Introduction to Tableau/Power BI, Customizing visualizations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCE419A | Data Science using R | Elective | 4 | R Programming Fundamentals, Data Manipulation in R, Statistical Modeling with R, Data Visualization in R (ggplot2), R packages for Data Science |
| DSCE419B | Time Series Analysis and Forecasting | Elective | 4 | Time Series Components, ARIMA and SARIMA Models, Exponential Smoothing Methods, Forecasting Model Evaluation, Time Series in Python/R |
| DSCP420 | Project and Viva Voce | Project | 20 | Problem Identification and Literature Survey, System Design and Architecture, Implementation and Development, Testing and Evaluation, Project Report Writing and Presentation |
