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B-SC 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 B.Sc Data Science program at Gandhi Institute of Technology and Management focuses on equipping students with essential skills in data analysis, machine learning, and big data technologies. It is designed to meet the growing demand for skilled data professionals in the Indian industry, offering a comprehensive curriculum that blends theoretical knowledge with practical application. The program emphasizes a strong foundation in mathematics, statistics, and computer science tailored for data-driven insights.
Who Should Apply?
This program is ideal for fresh graduates with a strong mathematical aptitude and a keen interest in technology and data. It also caters to individuals seeking a robust entry point into the burgeoning fields of analytics and artificial intelligence. Students with a background in 10+2 with Mathematics are particularly well-suited, as the curriculum builds upon these foundational quantitative skills.
Why Choose This Course?
Graduates of this program can expect to pursue exciting career paths as Data Analysts, Junior Data Scientists, Business Intelligence Developers, or Machine Learning Engineers in India. Entry-level salaries typically range from INR 4-7 LPA, with significant growth trajectories in companies across IT, finance, healthcare, and e-commerce sectors. The program''''s structure also aligns with prerequisites for various professional certifications in data science tools and platforms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate significant time to mastering Python programming, including data structures and algorithms. Participate in coding challenges regularly to build logic and problem-solving skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks Python tutorials
Career Connection
A strong programming base is non-negotiable for all data science roles, enabling efficient data manipulation, algorithm implementation, and scripting for automation, which are vital for securing internships and entry-level positions.
Build a Robust Mathematical & Statistical Base- (Semester 1-2)
Focus intensely on mathematical foundations (linear algebra, calculus) and statistical concepts. Regularly solve problems from textbooks and online resources to solidify understanding.
Tools & Resources
Khan Academy for Math, NPTEL courses on Probability and Statistics, Reference books
Career Connection
These foundational skills are crucial for understanding the ''''why'''' behind data science algorithms and models, making you a more effective and adaptable data scientist, highly valued in Indian analytical firms.
Engage in Peer Learning & Discussion Groups- (Semester 1-2)
Form study groups to discuss complex topics, solve problems collaboratively, and clarify doubts. Teach concepts to peers to deepen your own understanding.
Tools & Resources
Microsoft Teams, Discord servers for study groups, College library discussion rooms
Career Connection
Develops critical communication and teamwork skills, which are highly sought after in corporate environments, and helps build a strong academic network for future collaborations and referrals.
Intermediate Stage
Undertake Practical Mini-Projects- (Semester 3-5)
Apply theoretical knowledge by working on mini-projects using real-world datasets. Focus on end-to-end implementation from data collection to model deployment.
Tools & Resources
Kaggle for datasets and competitions, GitHub for project version control, Google Colab
Career Connection
Showcases your ability to apply data science concepts practically, creating a portfolio of work that significantly boosts your resume for internships and enhances your chances during placement drives.
Explore Data Science Tools & Libraries- (Semester 3-5)
Beyond classroom learning, actively learn and experiment with industry-standard data science tools and libraries like Pandas, NumPy, Scikit-learn, Tableau, and SQL.
Tools & Resources
Official documentation of libraries, Coursera/edX specialized courses, YouTube tutorials
Career Connection
Proficiency in these tools is a primary requirement for most data science roles in India, making you immediately productive and attractive to employers.
Participate in Hackathons & Competitions- (Semester 3-5)
Regularly participate in data science hackathons and coding competitions organized by colleges or external platforms. This exposes you to diverse problem statements and time-bound problem-solving.
Tools & Resources
Kaggle competitions, Analytics Vidhya hackathons, College technical fests
Career Connection
Develops quick thinking, teamwork, and practical application skills under pressure. Awards and rankings in such events are impressive additions to your professional profile for Indian tech companies.
Advanced Stage
Focus on Specialized Skill Development- (Semester 6)
Choose electives wisely and delve deeper into a niche area like Deep Learning, NLP, or Big Data. Complete advanced certifications or build specialized projects in your chosen area.
Tools & Resources
DeepLearning.AI courses, TensorFlow/PyTorch documentation, AWS/Azure/GCP certifications
Career Connection
Allows you to stand out in a competitive job market by becoming an expert in a specific domain, opening doors to specialized roles with higher earning potential in Indian startups and MNCs.
Intensive Placement Preparation- (Semester 6)
Begin rigorous preparation for placements including resume building, mock interviews (technical and HR), aptitude tests, and practicing case studies. Network with alumni for guidance.
Tools & Resources
Company-specific interview guides, LinkedIn for networking, Placement cell workshops
Career Connection
Directly impacts your ability to secure a desirable full-time role. A well-prepared candidate navigates the Indian recruitment process more effectively, leading to better offers.
Undertake a Capstone Project/Internship- (Semester 6)
Engage in a significant final year project that solves a real-world problem, ideally through an industry internship. This integrates all learned skills into a comprehensive solution.
Tools & Resources
Industry partners of GITAM, Internal faculty for guidance, Online project repositories
Career Connection
Provides invaluable practical experience, often leading to a pre-placement offer. It''''s the ultimate demonstration of your capabilities to potential employers and essential for career launch.
Program Structure and Curriculum
Eligibility:
- Pass in Intermediate (10+2) or its equivalent examination with Mathematics as one of the subjects from a recognized board, with a minimum of 50% aggregate marks.
Duration: 6 semesters / 3 years
Credits: 85 Credits
Assessment: Internal: 40% (Theory), 50% (Practical), External: 60% (Theory), 50% (Practical)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 15CDS101 | Programming for Data Science | Core | 3 | Introduction to Python, Control Structures and Loops, Functions and Modules, Data Structures (Lists, Tuples, Dictionaries), File Handling and Exceptions |
| 15CDS102 | Fundamentals of Data Science | Core | 3 | Data Science Lifecycle, Data Acquisition and Cleaning, Exploratory Data Analysis, Data Visualization Principles, Introduction to Machine Learning |
| 15CDS131 | Programming for Data Science Lab | Lab | 1.5 | Python programming exercises, Implementing basic data structures, Functions and modular programming, File I/O operations, Debugging practices |
| 15CDS132 | Fundamentals of Data Science Lab | Lab | 1.5 | Data loading and preprocessing, Performing EDA with Pandas, Creating visualizations with Matplotlib/Seaborn, Basic statistical analysis in Python, Data story-telling |
| 15MDC101 | Mathematical Foundations for Data Science | Core | 3 | Linear Algebra (Matrices, Vectors), Calculus (Differentiation, Integration), Probability Theory (Distributions, Bayes'''' Theorem), Descriptive Statistics, Optimization Techniques |
| 15LAC101 | Language & Communication Skills – I | Foundation | 2 | English Grammar and Usage, Reading Comprehension, Vocabulary Building, Sentence Construction, Basic Presentation Skills |
| 15EVS101 | Environmental Science | Audit | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Climate Change Impacts, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 15CDS103 | Data Structures and Algorithms | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees (Binary, BST, AVL), Graphs (Traversal, Shortest Path), Sorting Algorithms, Searching Algorithms |
| 15CDS104 | Statistical Methods for Data Science | Core | 3 | Probability Distributions, Hypothesis Testing, Regression Analysis, Analysis of Variance (ANOVA), Time Series Fundamentals |
| 15CDS133 | Data Structures and Algorithms Lab | Lab | 1.5 | Implementing various data structures, Coding sorting and searching algorithms, Graph traversal algorithms implementation, Algorithm efficiency analysis, Problem-solving with data structures |
| 15CDS134 | Statistical Methods for Data Science Lab | Lab | 1.5 | Statistical programming with R/Python, Performing hypothesis tests, Building regression models, Data manipulation for statistical analysis, Interpreting statistical results |
| 15LAC102 | Language & Communication Skills – II | Foundation | 2 | Advanced Grammar and Syntax, Public Speaking and Presentation, Group Discussion Techniques, Technical Report Writing, Professional Communication Ethics |
| 15CSS101 | Computer System Architecture | Core | 3 | Digital Logic and Gates, Combinational and Sequential Circuits, CPU Organization and Design, Memory Hierarchy and Management, Input/Output Organization |
| 15CDS191 | Term Paper | Project/Research | 1 | Research Topic Selection, Literature Review, Data Collection and Analysis, Scientific Writing, Presentation of Findings |
| 15EAC101 | Indian Constitution | Audit | 0 | Preamble and Fundamental Rights, Directive Principles of State Policy, Structure of Union Government, State Government and Judiciary, Constitutional Amendments |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 15CDS201 | Database Management Systems | Core | 3 | Data Models and Schema, Relational Algebra and SQL, Normalization and Dependencies, Transaction Management, Database Security and Integrity |
| 15CDS202 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Selection, Bias-Variance Tradeoff, Ensemble Methods |
| 15CDS231 | Database Management Systems Lab | Lab | 1.5 | SQL DDL, DML, DCL commands, Complex Queries and Joins, PL/SQL Programming, Database Design and Implementation, Views, Triggers, and Stored Procedures |
| 15CDS232 | Machine Learning Lab | Lab | 1.5 | Implementing ML algorithms with Scikit-learn, Data preprocessing and feature engineering, Model training and hyperparameter tuning, Evaluating model performance, Practical case studies |
| 15OEC201 | Open Elective - I (Example: Introduction to Cloud Computing) | Elective | 3 | Cloud Computing Paradigms (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Deployment Models, Cloud Service Providers Overview, Benefits and Challenges of Cloud Computing |
| 15CDS291 | Value Added Course – I | Skill-based/Value Added | 1 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 15CDS203 | Big Data Analytics | Core | 3 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark Framework, NoSQL Databases (MongoDB, Cassandra), Data Ingestion and Processing |
| 15CDS204 | Data Visualization | Core | 3 | Principles of Effective Visualization, Static and Interactive Plotting, Dashboard Design (Tableau/Power BI), Data Storytelling, Advanced Chart Types |
| 15CDS233 | Big Data Analytics Lab | Lab | 1.5 | Hadoop command-line operations, MapReduce program development, Spark RDD and DataFrame operations, Hive Query Language, Working with NoSQL databases |
| 15CDS234 | Data Visualization Lab | Lab | 1.5 | Creating visualizations with Matplotlib/Seaborn, Building interactive dashboards, Using Tableau/Power BI for business intelligence, Customizing plots for presentation, Exploring various data visualization tools |
| 15CDS252 | Data Science Elective - I (Example: Deep Learning) | Elective | 3 | Introduction to Neural Networks, Activation Functions and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/Keras) |
| 15CDS292 | Value Added Course – II | Skill-based/Value Added | 1 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 15CDS301 | Deep Learning | Core | 3 | Advanced Neural Network Architectures, Transfer Learning and Fine-tuning, Generative Adversarial Networks (GANs), Autoencoders, Deep Reinforcement Learning basics |
| 15CDS302 | Natural Language Processing | Core | 3 | Text Preprocessing (Tokenization, Stemming), Word Embeddings (Word2Vec, GloVe), Sentiment Analysis and Text Classification, Sequence Models (RNNs, LSTMs), Transformer Architectures |
| 15CDS331 | Deep Learning Lab | Lab | 1.5 | Implementing CNNs for image classification, Building RNNs for sequence data, Experimenting with transfer learning, Developing simple GANs, Optimizing deep learning models |
| 15CDS332 | Natural Language Processing Lab | Lab | 1.5 | Using NLTK/SpaCy for text analysis, Building text classifiers, Developing sentiment analysis models, Implementing chatbots, Extracting information from text |
| 15CDS352 | Data Science Elective - II (Example: Computer Vision) | Elective | 3 | Image Processing Fundamentals, Feature Extraction and Matching, Object Detection Algorithms, Image Segmentation, Facial Recognition Techniques |
| 15CDS391 | Project – I (Minor Project) | Project | 2 | Problem Definition, Data Collection and Preparation, Model Development and Implementation, Result Analysis and Reporting, Project Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 15CDS357 | Data Science Elective - III (Example: Generative AI) | Elective | 3 | Introduction to Generative Models, Variational Autoencoders (VAEs), Diffusion Models, Large Language Models (LLMs) principles, Ethical considerations in Generative AI |
| 15CDS361 | Data Science Elective - IV (Example: Geospatial Data Science) | Elective | 3 | Geographic Information Systems (GIS) Basics, Spatial Data Structures and Formats, Remote Sensing Fundamentals, Geospatial Data Analysis, Map Visualization Techniques |
| 15CDS392 | Project – II (Major Project) | Project | 8 | Advanced Problem Solving, Real-world Data Application, System Design and Architecture, Research Methodology and Experimentation, Comprehensive Documentation and Defense |
| 15INT399 | Internship | Internship | 2 | Practical Industry Exposure, Application of Data Science Skills, Professional Networking, Problem Solving in a Corporate Environment, Reporting on Internship Experience |
