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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 focuses on equipping students with advanced analytical skills, machine learning expertise, and big data technologies essential for navigating the complex data landscape. It emphasizes practical application and theoretical foundations, preparing graduates for high-demand roles in India''''s rapidly growing digital economy. The program integrates statistical modeling, computational techniques, and domain knowledge to solve real-world problems, making it highly relevant to contemporary industry needs.
Who Should Apply?
This program is ideal for engineering graduates (CSE, IT, ECE) or science postgraduates (CS, IT, Mathematics, Statistics) who possess a strong analytical bent and a foundational understanding of programming and mathematics. It caters to freshers aspiring to launch careers as Data Scientists, Machine Learning Engineers, or Data Analysts, as well as working professionals seeking to upskill and transition into data-centric roles in diverse sectors like finance, healthcare, and e-commerce within India.
Why Choose This Course?
Graduates of this program can expect to secure lucrative positions in India''''s leading IT companies, startups, and analytics firms. Typical career paths include Data Scientist, ML Engineer, AI Specialist, or Data Architect, with entry-level salaries ranging from INR 6-10 LPA, growing significantly with experience. The curriculum is designed to foster critical thinking and problem-solving, aligning with industry certifications and enabling graduates to contribute meaningfully to data-driven decision-making processes across Indian businesses.
Student Success Practices
Foundation Stage
Master Core Concepts with Practical Application- (Semester 1-2)
Focus rigorously on understanding the theoretical foundations of Advanced Data Structures & Algorithms, Mathematical Foundations, and Applied Machine Learning. Immediately apply these concepts by coding solutions in labs using Python or R. Actively participate in problem-solving sessions and doubt-clearing.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy, NPTEL, Jupyter Notebooks
Career Connection
Strong fundamentals are critical for passing technical interviews and building efficient data science solutions, directly impacting early career success in Indian tech companies.
Build a Strong Portfolio of Projects- (Semester 1-2)
Beyond lab assignments, undertake small personal projects from platforms like Kaggle or UCI Machine Learning Repository. Document your code, methodology, and results thoroughly on GitHub. This demonstrates initiative and practical skill.
Tools & Resources
Kaggle, GitHub, Google Colab, scikit-learn, TensorFlow/Keras
Career Connection
A well-curated GitHub portfolio is a significant asset during placements, showcasing your ability to apply learned concepts to real-world datasets, highly valued by Indian employers.
Engage in Peer Learning and Technical Discussions- (Semester 1-2)
Form study groups with peers to discuss challenging topics, solve problems together, and explain concepts to each other. Participate actively in departmental seminars, workshops, and tech talks. Seek mentorship from senior students or faculty.
Tools & Resources
Discord/WhatsApp groups for study, Departmental forums, Faculty office hours
Career Connection
Enhances communication skills, fosters a collaborative mindset, and deepens understanding, preparing you for team environments in Indian IT firms.
Intermediate Stage
Advanced Stage
Strategic Internship & Practical Experience- (Semester 3)
Proactively identify and secure a relevant industry internship in Semester III, focusing on applying theoretical knowledge to real-world data science challenges. Leverage university connections, alumni, and online platforms. Ensure the internship project aligns with your career aspirations and provides tangible output.
Tools & Resources
LinkedIn, Internshala, University career services, Professional networking events
Career Connection
High-quality internships often lead to pre-placement offers (PPOs) in leading Indian firms, providing critical industry exposure and a strong competitive edge in the job market.
Specialized Skill Development & Project Execution- (Semester 3)
Make informed choices for Professional Electives (III & IV) in Semester III to build niche expertise (e.g., IoT Analytics, Financial Analytics, Explainable AI). Concurrently, embark on your Project Work Phase-I with a well-defined problem, thorough literature review, and robust methodology, leading to a strong foundation for your final thesis.
Tools & Resources
Research databases (Scopus, Web of Science), Domain-specific libraries (e.g., PyTorch, Spark), Specialized online courses
Career Connection
Deep specialization makes you a targeted hire for specific roles, while a strong Phase-I project demonstrates independent research and application capabilities, crucial for advanced roles.
Comprehensive Project Completion & Placement Readiness- (Semester 4)
In Semester IV, dedicate maximum effort to Project Work Phase-II, focusing on complete implementation, rigorous testing, performance evaluation, and high-quality thesis documentation. Simultaneously, engage in intensive placement preparation including resume optimization, mock interviews, and technical aptitude practice, targeting specific roles and companies.
Tools & Resources
Grammarly, LaTeX for thesis, Company-specific interview prep platforms, Alumni mentorship for placement insights
Career Connection
A successful capstone project is a powerful differentiator, and holistic placement preparation is non-negotiable for securing top-tier positions in India''''s competitive data science landscape.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in CSE/IT/ECE/EEE/AE/EIE/Mechatronics or M.Sc. in CS/IT/Mathematics/Statistics/Electronics/Physics/Data Science/AI & ML/Actuarial Science/Cognitive Science/Geospatial Science/Geoinformatics, or MCA. Minimum 50% aggregate marks. Qualify in GITAM Admission Test (GAT) PG (Engineering).
Duration: 2 years (4 semesters)
Credits: 72 Credits
Assessment: Internal: 40% (Theory), 50% (Practical), 30% (Project Phase), External: 60% (Theory), 50% (Practical), 70% (Project Phase)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 1ECS501 | Advanced Data Structures & Algorithms | Core | 3 | Introduction to Data Structures, Trees and Heaps, Hashing Techniques, Graph Algorithms, Dynamic Programming, Greedy Algorithms |
| 1ECS502 | Mathematical Foundations for Data Science | Core | 3 | Linear Algebra, Probability and Statistics, Calculus for Machine Learning, Optimization Techniques, Eigenvalues and Eigenvectors, Random Variables and Distributions |
| 1ECS503 | Applied Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Evaluation and Selection, Feature Engineering, Ensemble Methods |
| 1ECS521 | Data Structures & Algorithms Lab | Lab | 1.5 | Implementation of ADTs, Tree Traversals, Sorting Algorithms, Searching Algorithms, Graph Algorithms Implementation, Dynamic Programming Solutions |
| 1ECS522 | Applied Machine Learning Lab | Lab | 1.5 | Data Preprocessing Techniques, Implementation of Regression Models, Implementation of Classification Models, Clustering Algorithms, Hyperparameter Tuning, Model Evaluation Metrics |
| 1EAR501 | Research Methodology and IPR | Core | 2 | Research Design and Methods, Data Collection and Analysis, Technical Writing and Presentation, Intellectual Property Rights, Patents and Copyrights, Research Ethics |
| 1XXX5XX | Open Elective – I | Open Elective | 2 | Varies based on the chosen elective from the general university pool. |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 1ECS504 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, HDFS Architecture, Apache Spark, NoSQL Databases |
| 1ECS505 | Deep Learning | Core | 3 | Neural Network Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 1ECS506 | Data Warehousing and Data Mining | Core | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Concepts, Association Rule Mining, Classification and Clustering Techniques |
| 1ECS523 | Big Data Analytics Lab | Lab | 1.5 | Hadoop Installation and Configuration, MapReduce Program Development, Spark Applications, Hive and Pig Scripting, Data Ingestion with Sqoop, NoSQL Database Interaction (MongoDB/Cassandra) |
| 1ECS524 | Deep Learning Lab | Lab | 1.5 | Building CNNs for Image Recognition, Implementing RNNs for Sequence Data, Transfer Learning Applications, Natural Language Processing with Deep Learning, Generative Adversarial Networks, Hyperparameter Tuning in Deep Models |
| 1ECS5XX | Professional Elective – I | Professional Elective | 3 | Natural Language Processing, Computer Vision, Reinforcement Learning, Time Series Analysis |
| 1ECS5XX | Professional Elective – II | Professional Elective | 3 | Cloud Computing for Data Science, Ethical Hacking, Optimization Techniques, Parallel and Distributed Computing |
| 1EHS501 | Soft Skills | Skill Enhancement | 1 | Communication Skills, Presentation Techniques, Teamwork and Collaboration, Leadership Qualities, Professional Ethics, Interpersonal Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 1ECI591 | Internship | Internship | 3 | Industry Exposure, Practical Application of Data Science, Project Implementation, Report Writing, Presentation Skills, Professional Networking |
| 1ECP691 | Project Work Phase - I | Project | 8 | Problem Identification and Formulation, Extensive Literature Review, Methodology Design, Data Collection and Preprocessing, Initial Model Prototyping, Project Proposal and Planning |
| 1ECS6XX | Professional Elective – III | Professional Elective | 3 | Advanced Database Systems, IoT Analytics, Conversational AI, Explainable AI |
| 1ECS6XX | Professional Elective – IV | Professional Elective | 3 | Health Analytics, Geospatial Data Science, Financial Analytics, Customer Analytics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 1ECP692 | Project Work Phase - II | Project | 15 | Full System Development and Implementation, Rigorous Testing and Debugging, Performance Evaluation and Optimization, Results Analysis and Interpretation, Thesis Writing and Documentation, Project Defense and Presentation |
