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M-TECH in Data Science at GITAM (Gandhi Institute of Technology and Management)
Visakhapatnam, Andhra Pradesh
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About the Specialization
What is Data Science at GITAM (Gandhi Institute of Technology and Management) Visakhapatnam?
This M.Tech Data Science program at GITAM (Deemed to be University) focuses on equipping students with advanced analytical and computational skills to extract insights from vast datasets. It addresses the growing demand for data scientists in the Indian industry, emphasizing practical applications, cutting-edge algorithms, and real-world problem-solving. The program integrates statistical modeling, machine learning, and big data technologies to prepare graduates for high-impact roles.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech in CSE/IT/ECE/EEE/AE/CS&BS/Mathematics & Computing/Data Science/AI & ML/IT) or postgraduates (MCA or M.Sc in CSE/IT/Mathematics/Statistics/Electronics/Data Science) with a strong aptitude for mathematics, programming, and problem-solving. It caters to fresh graduates seeking entry into the data science field, as well as working professionals looking to upskill or transition into data-driven roles within various sectors of the Indian economy.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, Machine Learning Engineer, Big Data Analyst, AI Specialist, or Business Intelligence Developer in India''''s booming tech and analytics sectors. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning upwards of INR 20-30 LPA. The curriculum aligns with industry certifications, providing a strong foundation for career growth in both startups and established Indian and multinational corporations.
Student Success Practices
Foundation Stage
Build a Strong Mathematical & Programming Foundation- (Semester 1-2)
Dedicate significant time to mastering concepts in Linear Algebra, Probability, Statistics, and Python programming. Utilize online platforms for practice, focusing on understanding underlying principles rather than just rote learning.
Tools & Resources
NPTEL courses on Mathematics for Machine Learning, Khan Academy, HackerRank, LeetCode, NumPy and Pandas documentation
Career Connection
A solid foundation is crucial for understanding complex ML algorithms and writing efficient data processing code, which are core skills for any data scientist role in India.
Engage in Data Structures & Algorithm Competitions- (Semester 1-2)
Participate in coding contests and challenges focused on Advanced Data Structures and Algorithms. This will improve problem-solving abilities and coding efficiency, which are essential for technical interviews at top Indian tech companies.
Tools & Resources
CodeChef, GeeksforGeeks, InterviewBit, SPOJ
Career Connection
Strong DSA skills are a primary filter for recruitment in product-based companies and tech roles in India, directly impacting placement opportunities and competitive salary packages.
Start Small Data Science Projects & Kaggle Participation- (Semester 1-2)
Apply learned Machine Learning concepts by working on small-scale personal projects. Begin exploring Kaggle datasets and participate in beginner-friendly competitions to gain practical experience and understand model building workflows.
Tools & Resources
Kaggle, GitHub, Google Colab, Scikit-learn documentation
Career Connection
Building a portfolio of projects, even small ones, demonstrates practical application skills to recruiters and sets candidates apart in the Indian job market for data science roles.
Intermediate Stage
Deep Dive into Big Data & Deep Learning Frameworks- (Semester 3)
Beyond coursework, explore advanced features of Big Data tools like Spark and Deep Learning frameworks like TensorFlow/PyTorch. Work through tutorials and build more complex projects integrating these technologies.
Tools & Resources
Apache Spark documentation, TensorFlow/PyTorch official tutorials, Udacity/Coursera courses on Deep Learning
Career Connection
Expertise in Big Data and Deep Learning frameworks is highly sought after for roles in AI/ML engineering, especially in Indian companies dealing with large-scale data and advanced analytics.
Seek Industry Internships and Mentorship- (Semester 3)
Actively search for and pursue internships during the academic breaks or even part-time during the third semester. Connect with industry professionals on LinkedIn for mentorship and insights into real-world data science challenges in India.
Tools & Resources
LinkedIn, Internshala, Company career pages, University placement cell
Career Connection
Internships provide invaluable industry exposure, build professional networks, and often lead to pre-placement offers, significantly boosting employability in the Indian market.
Specialize through Electives and Advanced Topics- (Semester 3)
Thoughtfully choose program electives that align with your career interests (e.g., NLP, Computer Vision, Business Analytics). Beyond classroom, delve deeper into these specialized areas through advanced research papers and online courses.
Tools & Resources
ArXiv, Google Scholar, Specific domain conferences, Online specialized courses
Career Connection
Specialization makes you a more targeted and valuable candidate for specific roles (e.g., NLP Engineer) in Indian tech companies and research divisions.
Advanced Stage
Focus on Capstone Project & Thesis Quality- (Semester 4)
Treat the final project work as an opportunity to solve a significant real-world problem. Aim for a high-quality, deployable solution and a well-written thesis that can be showcased to potential employers.
Tools & Resources
Research papers, Industry reports, Expert consultation, Academic writing tools
Career Connection
A strong capstone project is a powerful talking point in interviews and demonstrates your ability to independently conceptualize, execute, and deliver a complete data science solution, highly valued by Indian employers.
Master Interview Skills & Behavioral Aspects- (Semester 4)
Practice technical interview questions, revise core data science concepts, and work on communication and presentation skills. Prepare for behavioral interviews by reflecting on past experiences and aligning them with desired job roles in India.
Tools & Resources
Mock interviews, Glassdoor, LeetCode, Cracking the Coding Interview for algorithms
Career Connection
Excellent interview performance, both technical and behavioral, is the final step to convert job offers from top companies in India.
Build a Professional Brand & Network- (Semester 4)
Actively maintain a strong LinkedIn profile showcasing projects and skills. Attend industry webinars, conferences (virtual or local), and networking events to connect with recruiters and industry leaders in the Indian data science ecosystem.
Tools & Resources
LinkedIn, Professional networking events (e.g., Data Science Summits in India), Personal website/blog
Career Connection
A strong professional network and personal brand can open doors to opportunities not advertised publicly and provide critical insights into career growth in the Indian market.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in CSE/IT/ECE/EEE/AE/CS&BS/Mathematics & Computing/Data Science/AI & ML/IT or MCA or M.Sc. in CSE/IT/Mathematics/Statistics/Electronics/Data Science with 50% aggregate marks or equivalent CGPA.
Duration: 4 semesters (2 years)
Credits: 80 Credits
Assessment: Internal: 40% (for Theory courses), 50% (for Lab/Project courses), External: 60% (for Theory courses), 50% (for Lab/Project courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| RDC111 | Advanced Data Structures | Program Core | 3 | Abstract Data Types, Stacks and Queues, Trees (Binary, AVL, B-Trees), Hashing and Collision Resolution, Graph Algorithms (DFS, BFS, Shortest Paths) |
| RDC112 | Advanced Database Management Systems | Program Core | 3 | Relational Database Concepts, Transaction Management, Concurrency Control and Recovery, Distributed Databases, NoSQL Databases (Cassandra, MongoDB) |
| RDC113 | Mathematical Foundations for Data Science | Program Core | 3 | Linear Algebra for Data Science, Probability Theory, Statistical Inference and Hypothesis Testing, Optimization Techniques, Calculus for Machine Learning |
| RDC114 | Machine Learning | Program Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Reinforcement Learning Basics, Model Evaluation and Validation, Ensemble Methods (Bagging, Boosting) |
| RDC121 | Advanced Data Structures Lab | Program Core Lab | 1.5 | Implementation of ADTs (Stacks, Queues), Tree Traversals and Operations, Graph Algorithms (BFS, DFS, Dijkstra), Hashing Techniques Implementation, Sorting and Searching Algorithms |
| RDC122 | Advanced Database Management Systems Lab | Program Core Lab | 1.5 | Advanced SQL Queries and Optimization, PL/SQL Programming, Transaction Management Exercises, NoSQL Database Operations, Database Design and Normalization |
| RDC123 | Machine Learning Lab | Program Core Lab | 1.5 | Python Libraries for ML (Scikit-learn, Pandas, NumPy), Implementation of Regression Models, Implementation of Classification Models, Clustering Algorithms Practice, Model Evaluation Metrics and Techniques |
| RSC111 | Research Methodology and IPR | Research Skill Course | 2 | Research Problem Formulation, Research Design and Methods, Data Collection and Analysis, Intellectual Property Rights Fundamentals, Patenting and Technology Transfer |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| RDC115 | Big Data Analytics | Program Core | 3 | Introduction to Big Data Ecosystem, Hadoop Distributed File System (HDFS), MapReduce Programming Model, Apache Spark for Big Data Processing, Data Stream Mining and Real-time Analytics |
| RDC116 | Deep Learning | Program Core | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow, PyTorch) |
| RDC141 | Natural Language Processing (Program Elective - I) | Program Elective | 3 | Text Preprocessing and Tokenization, N-gram Models and Language Modeling, Word Embeddings (Word2Vec, GloVe), Sequence-to-Sequence Models, Transformer Networks and Attention |
| RDC142 | Computer Vision (Program Elective - I) | Program Elective | 3 | Image Filtering and Edge Detection, Feature Detection and Matching, Image Segmentation and Object Recognition, Deep Learning for Vision (CNN Architectures), Object Detection and Tracking |
| RDC143 | Business Analytics (Program Elective - I) | Program Elective | 3 | Descriptive Analytics and Reporting, Predictive Analytics Models, Prescriptive Analytics and Optimization, Data Mining for Business Insights, Decision Making with Analytics |
| RDC144 | Time Series Analysis (Program Elective - II) | Program Elective | 3 | Time Series Components (Trend, Seasonality), ARIMA Models, Exponential Smoothing Methods, Stationarity and Differencing, Forecasting Techniques |
| RDC145 | Reinforcement Learning (Program Elective - II) | Program Elective | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| RDC146 | Data Visualization (Program Elective - II) | Program Elective | 3 | Principles of Data Visualization, Exploratory Data Analysis with Visuals, Statistical Graphics, Interactive Visualizations, Dashboard Design and Storytelling |
| RDC124 | Big Data Analytics Lab | Program Core Lab | 1.5 | Hadoop Ecosystem Components (HDFS, MapReduce), Spark Programming (RDDs, DataFrames), Hive and Pig for Data Processing, NoSQL Databases for Big Data, Data Ingestion and ETL with Big Data Tools |
| RDC125 | Deep Learning Lab | Program Core Lab | 1.5 | TensorFlow/PyTorch Implementation, Building and Training CNNs, Implementing RNNs for Sequence Data, Transfer Learning Techniques, Hyperparameter Tuning |
| RDC131 | Skill Development Course – I (Python for Data Science) | Skill Development Course | 2 | Python Fundamentals, Data Structures in Python, NumPy for Numerical Operations, Pandas for Data Manipulation, Data Cleaning and Preprocessing |
| RDC171 | Term Paper | Project | 2 | Literature Survey and Review, Problem Identification, Methodology Proposal, Report Writing, Presentation Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| RDC241 | Cloud Computing for Data Science (Program Elective - III) | Program Elective | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Platforms for Big Data (AWS, Azure, GCP), Serverless Computing for Data Pipelines, Machine Learning as a Service (MLaaS), Data Storage and Security in Cloud |
| RDC242 | Graph Neural Networks (Program Elective - III) | Program Elective | 3 | Graph Theory Fundamentals, Graph Representation Learning, Graph Convolutional Networks (GCNs), Graph Attention Networks (GANs), Applications of GNNs in Data Science |
| RDC243 | Edge AI (Program Elective - III) | Program Elective | 3 | Introduction to Edge Computing, Deploying AI Models on Edge Devices, Optimizing Models for Edge Inference, Federated Learning, Edge AI Use Cases and Challenges |
| RDC244 | Optimization Techniques for Machine Learning (Program Elective - IV) | Program Elective | 3 | Convex Optimization Basics, Gradient Descent and Variants, Stochastic Gradient Descent, Adaptive Learning Rate Methods (Adam, RMSprop), Regularization Techniques |
| RDC245 | Applied Cryptography for Data Privacy (Program Elective - IV) | Program Elective | 3 | Fundamentals of Cryptography, Symmetric and Asymmetric Encryption, Homomorphic Encryption, Differential Privacy, Secure Multi-Party Computation |
| RDC246 | Recommender Systems (Program Elective - IV) | Program Elective | 3 | Introduction to Recommender Systems, Collaborative Filtering, Content-Based Recommending, Hybrid Recommender Systems, Evaluation Metrics for Recommenders |
| RDC231 | Skill Development Course – II (Data Engineering with AWS/Azure/GCP) | Skill Development Course | 2 | Cloud Data Storage Services, ETL Pipelines in Cloud, Data Warehousing Solutions, Data Lake Architectures, Cloud Security Best Practices |
| RDC281 | Project Work – Part A (Literature Review and Problem Identification) | Project | 8 | Comprehensive Literature Review, Problem Statement Definition, Objective Formulation, Methodology Design, Preliminary Data Collection/Analysis |
| RGS101 | Research Paper Writing (Open Elective - I) | Open Elective | 3 | Structure of a Research Paper, Effective Abstract Writing, Introduction and Literature Review, Methodology and Results Section, Discussion, Conclusion, and Referencing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| RDC282 | Project Work – Part B (Implementation and Thesis Writing) | Project | 20 | System Implementation and Development, Experimentation and Evaluation, Results Analysis and Interpretation, Comprehensive Thesis Writing, Project Defense and Presentation |
