.png&w=1920&q=75)
M-SC in Data Science at GITAM, Gandhi Institute of Technology and Management
Visakhapatnam, Andhra Pradesh
.png&w=1920&q=75)
About the Specialization
What is Data Science at GITAM, Gandhi Institute of Technology and Management Visakhapatnam?
This M.Sc Data Science program at Gandhi Institute of Technology and Management focuses on equipping students with robust theoretical knowledge and practical skills in data analytics, machine learning, and big data technologies. Given India''''s burgeoning digital economy, the program emphasizes real-world application, preparing graduates to tackle complex data challenges across diverse sectors. Its blend of core concepts and hands-on labs makes it a strong foundation for future data professionals.
Who Should Apply?
This program is ideal for fresh graduates with a background in Science (especially Mathematics, Computer Science) or Engineering disciplines, aspiring to kickstart a career in data-driven roles. It also suits working professionals looking to upskill in cutting-edge data science techniques or career changers transitioning into the high-demand field of analytics and machine learning, provided they meet the mathematical and programming prerequisites.
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, Business Intelligence Developer, and AI Specialist. Entry-level salaries in India typically range from INR 4-8 LPA, with experienced professionals earning significantly higher. The program''''s comprehensive curriculum aligns with industry demands, fostering growth trajectories in top Indian IT companies, startups, and MNCs operating in the country.
Student Success Practices
Foundation Stage
Master Core Programming and Math- (Semester 1)
Dedicate significant time to Python programming fundamentals and mathematical concepts (linear algebra, statistics, probability). Utilize online platforms like HackerRank, LeetCode, and NPTEL courses to strengthen problem-solving skills and grasp theoretical underpinnings effectively.
Tools & Resources
Python Documentation, NumPy, Pandas, HackerRank, NPTEL Courses
Career Connection
A strong foundation is crucial for excelling in technical interviews and building efficient data solutions, directly impacting early career success and project contributions.
Build a Strong Data Management Base- (Semester 1)
Focus on developing solid skills in SQL and NoSQL databases. Practice designing schemas, writing complex queries, and understanding data warehousing concepts. Engage in small personal projects involving data storage and retrieval to solidify practical understanding.
Tools & Resources
MySQL, PostgreSQL, MongoDB, Online SQL tutorials, Kaggle datasets
Career Connection
Proficiency in data management is a core requirement for almost all data roles, enabling efficient data access and preparation, which is vital for analytics and model building.
Participate in Academic Projects and Peer Learning- (Semester 1)
Actively engage in initial academic projects, even small ones, to apply learned concepts. Form study groups with peers to discuss challenging topics, share knowledge, and collectively solve problems, fostering a collaborative learning environment.
Tools & Resources
GitHub (for version control), Google Colab, Discussion forums
Career Connection
Early project experience builds a portfolio, while peer learning enhances understanding and develops teamwork skills, both highly valued in professional data science teams.
Intermediate Stage
Dive Deep into Machine Learning and Big Data- (Semesters 2-3)
Beyond coursework, explore advanced machine learning algorithms and big data technologies like Spark and Hadoop. Participate in online machine learning competitions on platforms like Kaggle to gain practical experience with real-world datasets and diverse problem statements.
Tools & Resources
Scikit-learn, TensorFlow, PyTorch, Apache Spark, Kaggle, Coursera/edX advanced ML courses
Career Connection
Hands-on experience with complex ML and big data problems makes you a competitive candidate for roles like ML Engineer or Big Data Analyst in Indian companies.
Develop Advanced Visualization and Communication Skills- (Semesters 2-3)
Master data visualization tools like Tableau or PowerBI to effectively communicate insights. Practice creating compelling data stories and presenting findings clearly, as conveying technical information to non-technical stakeholders is a critical skill.
Tools & Resources
Tableau Public, PowerBI Desktop, Canva (for infographics), Presentation skills workshops
Career Connection
Strong visualization and communication skills differentiate candidates, enabling them to lead data-driven decision-making and secure roles in Business Intelligence and Data Storytelling.
Undertake Industry-Relevant Mini-Projects- (Semesters 2-3)
Identify real-world problems from local industries or open data sources and work on mini-projects. This could involve building a recommendation system, developing an NLP model for text analysis, or forecasting trends. Document your process and results meticulously.
Tools & Resources
GitHub, Jupyter Notebooks, Open-source datasets (e.g., from government portals)
Career Connection
Practical projects demonstrate your ability to apply theoretical knowledge to solve business problems, significantly boosting your resume for internships and entry-level positions.
Advanced Stage
Focus on a Capstone Project and Portfolio Building- (Semester 4)
Invest deeply in your final year project, aiming for a robust, production-ready solution that addresses a significant problem. Document your project comprehensively on platforms like GitHub and create a professional portfolio website showcasing your skills and projects.
Tools & Resources
GitHub, Personal portfolio website builder, LinkedIn
Career Connection
A strong capstone project and a well-curated portfolio are paramount for showcasing expertise to potential employers and securing desirable placements in top Indian tech firms.
Engage in Internships and Industry Exposure- (Semester 4)
Actively seek and participate in internships in data science roles during the summer or final year. This provides invaluable industry exposure, networking opportunities, and a chance to apply academic learning in a professional setting.
Tools & Resources
Internshala, Naukri.com (for internships), Company career pages, Alumni network
Career Connection
Internships often convert into full-time offers and provide a significant advantage in the competitive job market, offering a direct pathway to desired roles.
Prepare Rigorously for Placements and Interviews- (Semester 4)
Start placement preparation early, focusing on coding challenges, machine learning concepts, statistical aptitude, and behavioral interview questions. Participate in mock interviews and group discussions to refine your communication and problem-solving under pressure.
Tools & Resources
GeeksforGeeks, LeetCode, InterviewBit, Glassdoor (for company-specific questions)
Career Connection
Thorough preparation is key to navigating the rigorous interview processes of Indian companies, maximizing your chances of securing excellent placement offers post-graduation.
Program Structure and Curriculum
Eligibility:
- Bachelor’s degree with Mathematics as one of the subjects or BCA/B.Sc. (Computer Science) or B.Tech (any discipline) with 50% aggregate marks from a recognized university.
Duration: 2 years / 4 semesters
Credits: 78 Credits
Assessment: Internal: 40% (Theory) / 50% (Practical & Project), External: 60% (Theory) / 50% (Practical & Project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| GMA701 | Mathematical Foundations for Data Science | Core | 4 | Linear Algebra, Calculus, Probability and Statistics, Optimization Techniques, Graph Theory |
| GCS701 | Programming for Data Science | Core | 4 | Python Programming Fundamentals, Data Structures, Object-Oriented Programming, File Handling, Basic Algorithms |
| GCS702 | Data Management for Data Science | Core | 4 | Database Concepts, SQL and Relational Databases, NoSQL Databases, Data Warehousing, Data Modeling |
| GCS721 | Programming for Data Science Lab | Lab | 2 | Python programming practice, Implementation of data structures, Algorithm design and testing, Database connectivity with Python, Solving computational problems |
| GCS722 | Data Management for Data Science Lab | Lab | 2 | SQL query writing, Database design and implementation, NoSQL database operations, ETL process basics, Data integrity and security |
| GHS701 | Soft Skills | Core | 2 | Communication Skills, Presentation Skills, Group Discussion Techniques, Interview Preparation, Professional Etiquette |
| GAC701 | Professional Ethics & Human Values | Audit | 0 | Ethical Theories, Professional Responsibility, Cyber Ethics, Human Values, Moral Dilemmas |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| GDS701 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation and Validation, Ensemble Methods |
| GDS702 | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, MapReduce Programming, Data Ingestion and Processing |
| GDS703 | Data Visualization | Core | 4 | Principles of Data Visualization, Data Storytelling, Visualization Tools (Tableau/PowerBI), Interactive Dashboards, Infographics Design |
| GDS721 | Machine Learning Lab | Lab | 2 | Implementing ML algorithms in Python, Using Scikit-learn, Model training and testing, Hyperparameter tuning, Performance metric calculation |
| GDS722 | Big Data Analytics Lab | Lab | 2 | Hadoop HDFS operations, MapReduce programming exercises, Spark application development, Data processing with Big Data tools, Cluster management basics |
| GDS723 | Data Visualization Lab | Lab | 2 | Building dashboards with Tableau/PowerBI, Data cleaning for visualization, Creating various chart types, Designing interactive visualizations, Presenting data effectively |
| GAC702 | Research Methodology and IPR | Audit | 0 | Research Design, Data Collection Methods, Statistical Analysis, Report Writing, Intellectual Property Rights |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| GDS801 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Backpropagation, Transfer Learning |
| GDS802 | Advanced Analytics | Core | 4 | Time Series Analysis, Natural Language Processing, Recommender Systems, Spatial Analytics, Survival Analysis |
| GDS821 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras implementation, CNN for image recognition, RNN for sequence data, Model optimization techniques, Leveraging GPUs for deep learning |
| GDS822 | Advanced Analytics Lab | Lab | 2 | NLP libraries (NLTK, SpaCy), Time series forecasting models, Building recommendation engines, Text mining applications, Sentiment analysis implementation |
| GDS823 | Data Science Tools Lab | Lab | 2 | Cloud platforms (AWS/Azure/GCP) usage, Docker for containerization, Version control with Git, Data orchestration tools, Introduction to MLOps practices |
| GDS831 | Data Mining Techniques (Program Elective-I - Example) | Elective | 4 | Classification Algorithms, Clustering Algorithms, Association Rule Mining, Data Preprocessing Techniques, Web Mining |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| GDS849 | Project Work – I (Dissertation) | Project | 6 | Problem Identification, Literature Survey, Methodology Design, Data Collection and Preparation, Preliminary Results and Analysis |
| GDS850 | Project Work – II (Dissertation) | Project | 8 | System Implementation, Experimentation and Evaluation, Result Interpretation, Report Writing and Documentation, Project Presentation and Defense |
| GDS833 | Business Intelligence and Data Warehousing (Program Elective-II - Example) | Elective | 4 | Business Intelligence Architecture, Data Warehouse Design, ETL Processes, Online Analytical Processing (OLAP), Reporting and Dashboarding |
