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M-SC-DATA-SCIENCE-AND-BIG-DATA-ANALYTICS-SIG in General at Symbiosis International University (SIU)
Pune, Maharashtra
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About the Specialization
What is General at Symbiosis International University (SIU) Pune?
This M.Sc. Data Science and Big Data Analytics program at Symbiosis International University focuses on equipping students with advanced skills in data manipulation, statistical modeling, machine learning, and big data technologies. In the burgeoning Indian digital economy, this program addresses the critical demand for skilled data professionals across various sectors. It differentiates itself through a strong blend of theoretical foundations and practical, industry-relevant application, preparing students for real-world challenges.
Who Should Apply?
This program is ideal for engineering, science, commerce, or management graduates with a strong aptitude for mathematics and analytical thinking, seeking entry into the high-growth data science field. It also caters to working professionals aiming to upskill in advanced analytics, big data, or machine learning, and career changers from IT or quantitative backgrounds desiring a transition into data-centric roles within India''''s dynamic tech landscape.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Machine Learning Engineer, Big Data Engineer, Data Analyst, and Business Intelligence Developer. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals commanding significantly higher packages. Growth trajectories are robust in Indian companies, with opportunities to lead data initiatives and gain valuable professional certifications in cloud or specific analytics tools, ensuring a competitive edge.
Student Success Practices
Foundation Stage
Build a Strong Programming and Statistical Foundation- (Semester 1-2)
Focus intensively on mastering Python programming for data science and understanding core statistical concepts. Regularly practice coding challenges on platforms like HackerRank or LeetCode, and solve statistical problems from textbooks. Engage in peer study groups to clarify doubts and collaboratively tackle complex problems, ensuring a robust academic base.
Tools & Resources
Python (Jupyter Notebooks, Spyder), Numpy, Pandas, Scikit-learn, Khan Academy, Coursera (statistics courses), CodeChef, GeeksforGeeks
Career Connection
A solid foundation is crucial for cracking technical interviews, understanding advanced concepts, and building effective data solutions, directly impacting early career success in analytical roles.
Engage in Exploratory Data Analysis (EDA) Projects- (Semester 1-2)
Actively seek out small datasets (e.g., from Kaggle or government open data portals) and apply learned statistical and programming skills for exploratory data analysis. Focus on data cleaning, visualization, and extracting initial insights. Document your process thoroughly and present findings to peers or mentors for feedback, honing your analytical storytelling.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Matplotlib, Seaborn, Plotly, Tableau Public
Career Connection
Developing strong EDA skills from the beginning makes candidates valuable for data analyst roles and forms the bedrock for more complex machine learning projects, making them highly placement-ready for Indian companies.
Network with Seniors and Attend Introductory Webinars- (Semester 1-2)
Connect with senior students and alumni to understand their project experiences, career paths, and study strategies. Attend free introductory webinars or workshops on emerging data science trends offered by industry bodies or platforms like LinkedIn Learning, to gain broader industry perspective and identify areas of interest early on.
Tools & Resources
LinkedIn, University Alumni Network, Eventbrite, Data Science Community meetups
Career Connection
Early networking opens doors to mentorship, internship leads, and helps in understanding industry expectations, giving a competitive edge in the Indian job market.
Intermediate Stage
Develop Practical Machine Learning and Big Data Projects- (Semester 3)
Apply theoretical knowledge of machine learning and big data technologies to build end-to-end projects. Work on datasets that require distributed computing (e.g., using Spark) or complex ML models (e.g., deep learning for image/text). Focus on problem definition, data pipeline construction, model training, and rigorous evaluation.
Tools & Resources
Apache Spark, Hadoop, TensorFlow/PyTorch, Scikit-learn, Google Colab, AWS/Azure free tiers
Career Connection
Building a robust portfolio of practical projects is paramount for demonstrating competency to recruiters for Data Scientist and Big Data Engineer roles, often a key differentiator during placements in India.
Participate in Data Science Competitions and Hackathons- (Semester 3)
Actively participate in online data science competitions on platforms like Kaggle, Analytics Vidhya, or university-organized hackathons. These provide real-world problem-solving experience under pressure, exposure to diverse datasets, and opportunities to learn from others'''' solutions. Focus on learning new techniques and improving model performance.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for solution sharing, Online forums
Career Connection
Success in competitions showcases problem-solving abilities, analytical rigor, and resilience, which are highly valued by Indian employers and can lead to direct hiring opportunities or interviews.
Secure an Industry Internship (Mini Project)- (Semester 3 (summer break))
Seek out and complete a summer internship (as part of the mini-project/internship in Sem 3) at a relevant company in India. Focus on applying academic learning to real-world business problems, collaborating with industry professionals, and understanding organizational data workflows. Document your contributions and learnings thoroughly.
Tools & Resources
University Placement Cell, LinkedIn Jobs, Naukri.com, Internship portals (Internshala)
Career Connection
Internships provide invaluable industry exposure, build professional networks, and often convert into pre-placement offers, significantly easing the job search process post-graduation in India.
Advanced Stage
Master MLOps and Deployment Strategies- (Semester 4)
Beyond model development, concentrate on understanding how to deploy, monitor, and maintain machine learning models in production environments. Learn about Docker, Kubernetes, CI/CD pipelines for ML, and cloud-based deployment services. Build a project demonstrating robust model deployment and lifecycle management.
Tools & Resources
Docker, Kubernetes, FastAPI/Flask, AWS SageMaker, Azure ML, GCP AI Platform, Git
Career Connection
MLOps skills are in high demand in India''''s tech industry, as companies seek to operationalize their ML investments. This makes graduates highly employable for ML Engineer and MLOps Engineer roles.
Undertake a Capstone Dissertation Project with Impact- (Semester 4)
Dedicate significant effort to your final dissertation/major project, choosing a challenging problem that aligns with industry needs or addresses a gap in research. Aim for a novel solution or a comprehensive application of advanced techniques, ensuring your project has tangible outcomes and is well-documented with proper research methodology.
Tools & Resources
Research papers (arXiv, Google Scholar), Industry reports, Cloud resources, Advanced ML/DL frameworks
Career Connection
A strong capstone project serves as a powerful testament to your expertise and problem-solving capabilities, acting as a centerpiece for your resume and interview discussions, often leading to specialized roles in India.
Prepare for Specific Industry Roles and Certifications- (Semester 4)
Identify target roles (e.g., Data Scientist, ML Engineer, Data Architect) and tailor your resume and interview preparation accordingly. Practice domain-specific case studies and technical questions. Consider pursuing relevant industry certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer) to enhance your credibility and marketability.
Tools & Resources
Online mock interview platforms, LeetCode, HackerRank, Industry-specific certification guides, Professional networking
Career Connection
Focused preparation and certifications directly impact placement success, enabling graduates to secure desired roles with competitive packages in leading Indian and global firms.
Program Structure and Curriculum
Eligibility:
- Graduate from any recognised University/Institution of National Importance with a minimum of 50% marks or equivalent grade (45% for SC/ST category).
Duration: 2 years (4 semesters)
Credits: 80 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS101 | Foundations of Data Science | Core | 4 | Introduction to Data Science, Data Types and Sources, Data Ecosystem, Ethics in Data Science, Data Science Lifecycle, Career Paths |
| MDS102 | Statistical Methods for Data Science | Core | 4 | Descriptive Statistics, Inferential Statistics, Probability Theory, Hypothesis Testing, Regression Analysis, ANOVA |
| MDS103 | Mathematical Foundations for Machine Learning | Core | 4 | Linear Algebra, Calculus, Optimization Techniques, Vector Spaces, Matrix Operations, Eigenvalues |
| MDS104 | Programming for Data Science (Python) | Core | 4 | Python Fundamentals, Data Structures in Python, Control Flow, Functions and Modules, Object-Oriented Programming, File Handling |
| MDS105 | Database Management Systems | Core | 4 | Relational Databases, SQL Queries, Database Design, Normalization, Transaction Management, Indexing |
| MDS106 | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks, Queues, Trees, Graph Algorithms, Sorting Algorithms, Searching Algorithms, Algorithm Complexity |
| MDS107 | Data Science Lab I (Python & SQL) | Lab | 2 | Python libraries for data manipulation (Numpy, Pandas), Data Cleaning and Preprocessing, SQL for Data Retrieval and Manipulation, Basic Exploratory Data Analysis, Data Visualization with Matplotlib/Seaborn, Creating Functions and Modules in Python |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS201 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Model Evaluation and Selection, Ensemble Methods, Bias-Variance Tradeoff, Feature Engineering |
| MDS202 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, HDFS, MapReduce, Apache Spark Fundamentals, Distributed Computing Concepts, Hive and Pig |
| MDS203 | Data Warehousing and Data Mining | Core | 4 | Data Warehousing Concepts, OLAP and OLTP, ETL Process, Data Mining Techniques, Association Rule Mining, Classification Algorithms |
| MDS204 | Business Intelligence and Data Visualization | Core | 4 | BI Architecture and Strategy, Dashboards and Reporting, Data Storytelling, Tableau/Power BI Introduction, Interactive Visualizations, Key Performance Indicators (KPIs) |
| MDS205 | NoSQL Databases | Core | 4 | NoSQL Concepts and Types, Document Databases (MongoDB), Key-Value Stores (Redis), Column-Family Stores (Cassandra), Graph Databases, Data Modeling in NoSQL |
| MDS206 | Advanced Statistical Modeling | Core | 4 | Time Series Analysis, Survival Analysis, Bayesian Statistics, Factor Analysis, Discriminant Analysis, Generalized Linear Models |
| MDS207 | Data Science Lab II (Machine Learning & Big Data) | Lab | 2 | Implementing Machine Learning Algorithms with Scikit-learn, Apache Spark Programming, HDFS Operations and Data Ingestion, Building Predictive Models, Model Tuning and Optimization, Working with Big Data Tools |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS301 | Deep Learning | Core | 4 | Neural Network Architectures, CNNs, RNNs, LSTMs, Backpropagation and Optimization, Transfer Learning, TensorFlow and Keras Frameworks, Image and Sequence Processing |
| MDS302 | Natural Language Processing | Core | 4 | Text Preprocessing, Tokenization and N-grams, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Introduction to Chatbots |
| MDS303 | Cloud Computing for Data Science | Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), AWS/Azure/GCP for Data Science, Cloud Storage Solutions, Serverless Computing, Big Data Services on Cloud, Cloud Security Fundamentals |
| MDS304 | Stream Analytics | Core | 4 | Real-time Data Processing Concepts, Apache Kafka, Apache Flink, Apache Storm, Stream Processing Use Cases, Event Driven Architectures |
| MDS305 | Elective I | Elective | 3 | Advanced Time Series Forecasting, Reinforcement Learning Fundamentals, IoT Data Analytics, Geospatial Data Science, Ethical AI Principles, Financial Analytics Models |
| MDS306 | Elective II | Elective | 3 | Social Media Analytics, Web Analytics and SEO, Healthcare Data Analytics, Marketing Analytics Strategies, Customer Analytics, Supply Chain Analytics |
| MDS307 | Research Project / Internship | Project | 6 | Problem Definition and Scoping, Literature Review and Gap Analysis, Methodology Design, Data Collection and Preparation, Implementation and Experimentation, Report Writing and Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS401 | Big Data Analytics Platforms | Core | 4 | Data Lake Architectures, Data Governance and Security, Data Pipelines and ETL Workflows, Workflow Orchestration, CI/CD for Data Projects, Data Cataloging and Metadata Management |
| MDS402 | MLOps and Deployment of ML Models | Core | 4 | Model Deployment Strategies, Version Control for Models, Containerization (Docker), Orchestration (Kubernetes), Monitoring ML Models in Production, A/B Testing and Model Governance |
| MDS403 | Elective III | Elective | 3 | Computer Vision Techniques, Explainable AI (XAI), Quantum Machine Learning Concepts, Generative AI Models, Edge AI Deployments, Advanced Geospatial Analytics |
| MDS404 | Elective IV | Elective | 3 | Advanced Marketing Analytics, Financial Risk Modeling, Econometrics for Data Science, Customer Lifetime Value Prediction, Fraud Detection Analytics, Personalization and Recommender Systems |
| MDS405 | Dissertation / Major Project | Project | 12 | Comprehensive Problem Solving, Advanced Research and Innovation, Independent Study and Analysis, Solution Design and Implementation, Thesis Writing and Documentation, Oral Defense and Presentation |
