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B-TECH in Data Science at Aditya Institute of Technology and Management
Srikakulam, Andhra Pradesh
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About the Specialization
What is Data Science at Aditya Institute of Technology and Management Srikakulam?
This Data Science program at Aditya Institute of Technology and Management, Srikakulam, focuses on equipping students with the theoretical knowledge and practical skills required to extract insights from complex datasets. Given India''''s rapid digital transformation, the program emphasizes real-world applications in areas like business analytics, healthcare, and finance, preparing graduates for the surging demand for data professionals across various sectors. Its comprehensive curriculum covers a blend of mathematics, statistics, computer science, and core data science methodologies.
Who Should Apply?
This program is ideal for fresh graduates with a strong analytical bent and a background in mathematics or computer science who aspire to build a career in data-driven fields. It also caters to working professionals seeking to upskill in advanced data science techniques or career changers from traditional IT roles looking to transition into specialized analytics and machine learning domains within the burgeoning Indian tech industry. A foundational understanding of programming and logical reasoning is beneficial.
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, or AI Specialist in both Indian startups and multinational corporations operating in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals potentially earning INR 15-30+ LPA. The program aligns with industry-recognized skills, paving the way for certifications in cloud data platforms or specialized AI/ML tools, fostering strong growth trajectories.
Student Success Practices
Foundation Stage
Master Core Programming and Mathematics- (Semester 1-2)
Dedicate significant time to thoroughly understand C and Python programming fundamentals, alongside discrete mathematics, linear algebra, and probability. Utilize online platforms for coding practice and problem-solving, ensuring a strong base for advanced topics.
Tools & Resources
GeeksforGeeks, HackerRank, Coursera (for foundational math courses), Textbooks and Lecture Notes
Career Connection
A solid foundation in programming and mathematics is critical for acing technical interviews and understanding the underlying principles of data science algorithms, crucial for placement success.
Build a Strong Academic Network and Peer Learning- (Semester 1-2)
Engage actively with faculty during office hours for conceptual clarity and form study groups with peers. Participate in departmental quizzes and academic competitions to reinforce learning and develop collaborative problem-solving skills.
Tools & Resources
Departmental Study Groups, Faculty Mentorship, Technical Clubs
Career Connection
Strong networking skills and collaborative experience are highly valued in team-oriented data science roles, improving communication skills essential for placements.
Develop Early Problem-Solving Skills- (Semester 1-2)
Focus on applying theoretical knowledge to practical problems. Regularly solve logical reasoning and quantitative aptitude problems, which are integral parts of campus recruitment processes for entry-level data roles.
Tools & Resources
IndiaBix, Online Aptitude Tests, Basic Data Structures and Algorithms practice
Career Connection
Early development of problem-solving and analytical thinking directly translates into better performance in technical and aptitude rounds of company placements.
Intermediate Stage
Engage in Hands-on Data Science Projects- (Semester 3-5)
Actively participate in lab sessions for Data Structures, DBMS, and Machine Learning. Start building small projects using real-world datasets from platforms like Kaggle, applying concepts learned in Data Mining and Machine Learning courses.
Tools & Resources
Kaggle, GitHub, Python libraries (Pandas, Scikit-learn), Google Colab
Career Connection
Practical project experience is a major differentiator in resumes and interviews, demonstrating applied knowledge and problem-solving abilities to potential employers during placements.
Seek Early Industry Exposure and Certifications- (Semester 3-5)
Look for summer internships or virtual internships (e.g., AICTE Internshala) in data analytics or machine learning roles. Consider pursuing entry-level certifications in SQL, Python for Data Science, or cloud fundamentals (AWS/Azure/GCP).
Tools & Resources
Internshala, NPTEL, Coursera Specializations, LinkedIn Learning, DataCamp
Career Connection
Certifications and early industry exposure enhance employability, showing commitment and practical skills that attract recruiters for campus placements and off-campus opportunities.
Participate in Technical Competitions and Hackathons- (Semester 3-5)
Join college-level or external data science competitions, hackathons, and coding challenges. This helps in understanding teamwork, time management, and applying diverse skills under pressure, building a robust portfolio.
Tools & Resources
Devpost, D2C (Dare2Compete), College Technical Fests
Career Connection
Success in competitions and hackathons provides concrete examples of problem-solving, innovation, and teamwork, significantly boosting your profile for placements.
Advanced Stage
Specialize through Advanced Electives and Research- (Semester 6-8)
Choose professional electives aligned with your career interests (e.g., Deep Learning, NLP, IoT Analytics). Consider undertaking research projects under faculty guidance, potentially leading to publications or advanced skill development.
Tools & Resources
Research Papers (arXiv, Google Scholar), Advanced Python Libraries, University Research Labs
Career Connection
Specialized knowledge and research experience are highly valued for roles requiring specific expertise and can open doors to R&D positions or higher studies.
Intensive Placement Preparation and Mock Interviews- (Semester 6-8)
Focus intensely on refining resume, preparing for technical, HR, and behavioral interviews. Participate in mock interviews conducted by the placement cell, alumni, or peers to simulate real-world scenarios and receive constructive feedback.
Tools & Resources
College Placement Cell, Mock Interview Platforms, Glassdoor for company-specific interview experiences
Career Connection
Rigorous preparation ensures confidence and proficiency in interviews, directly impacting success rates for final year placements in top companies.
Complete a Capstone Project and Professional Portfolio- (Semester 6-8)
Develop a comprehensive major project that integrates various data science concepts, demonstrating end-to-end problem-solving. Curate a professional online portfolio showcasing all your projects, skills, and achievements to prospective employers.
Tools & Resources
GitHub Repository, Personal Website/Blog, LinkedIn Profile, Medium (for project write-ups)
Career Connection
A strong capstone project and well-maintained portfolio are essential for visually demonstrating your capabilities and securing high-quality placements in data science roles.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics (PCM) as compulsory subjects, with a minimum aggregate percentage as per JNTUK/State Government norms and a valid rank in EAPCET (formerly EAMCET).
Duration: 4 years (8 semesters)
Credits: 150 Credits
Assessment: Internal: 30-40% (Continuous Internal Evaluation), External: 60-70% (Semester End Examination)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS1101 | Professional English | Core | 3 | Listening and Speaking Skills, Reading Comprehension, Writing Skills, Vocabulary and Grammar, Professional Communication |
| BS1102 | Linear Algebra and Calculus | Core | 3 | Matrices and Determinants, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus, Multivariable Calculus |
| BS1103 | Applied Physics | Core | 3 | Wave Optics, Lasers and Fiber Optics, Quantum Mechanics, Semiconductor Physics, Dielectric and Magnetic Materials |
| ES1101 | Programming for Problem Solving using C | Core | 3 | C Language Fundamentals, Control Structures, Arrays and Strings, Functions and Pointers, Structures, Unions, and File I/O |
| ES1102 | Engineering Drawing | Core | 1.5 | Conic Sections, Projections of Points and Lines, Projections of Planes, Projections of Solids, Orthographic and Isometric Projections |
| ES1103 | Programming for Problem Solving using C Lab | Lab | 1.5 | C Program Execution, Conditional Statements and Loops, Functions and Recursion, Arrays and Pointers, Structures and File Operations |
| BS1104 | Applied Physics Lab | Lab | 1.5 | Diffraction Grating, Newton''''s Rings, Laser Characteristics, Photoelectric Effect, Semiconductor Device Characteristics |
| HS1101 | Professional English Lab | Lab | 1.5 | Phonetics and Pronunciation, Role Plays and Dialogues, Group Discussions, Presentations, Public Speaking |
| ES1104 | IT Workshop | Lab | 1.5 | Computer Hardware Assembly, Operating System Installation, Networking Basics, MS Office Applications, Internet and Web Browsing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS1201 | Probability and Statistics | Core | 3 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression |
| BS1202 | Chemistry | Core | 3 | Water Technology, Electrochemistry, Polymers and Composites, Fuels and Combustion, Corrosion and its Control |
| CS1201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| ES1201 | Python Programming | Core | 3 | Python Language Fundamentals, Control Flow and Functions, Data Structures (Lists, Tuples, Dictionaries), Object-Oriented Programming in Python, Modules and Packages |
| MC1201 | Environmental Science | Mandatory Non-Credit | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Climate Change and Global Issues, Environmental Protection and Management |
| BS1203 | Chemistry Lab | Lab | 1.5 | Titrations (Acid-Base, Redox), pH and Conductometric Measurements, Viscosity and Surface Tension, Water Hardness Determination, Cement Analysis |
| CS1202 | Data Structures Lab | Lab | 1.5 | Linked List Implementations, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting and Searching Algorithms |
| ES1202 | Python Programming Lab | Lab | 1.5 | Basic Python Programs, Data Structure Manipulation, File Handling, GUI Programming, Introduction to Libraries (Numpy, Pandas) |
| ES1203 | Basic Electrical and Electronics Engineering | Core | 3 | DC and AC Circuits, PN Junction Diode, Rectifiers and Filters, Bipolar Junction Transistors, Digital Logic Gates |
| ES1204 | Basic Electrical and Electronics Engineering Lab | Lab | 1.5 | Verification of Circuit Laws, Diode Characteristics, Transistor Characteristics, Rectifier Circuits, CRO Usage |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS2101 | Discrete Mathematics | Core | 3 | Mathematical Logic, Set Theory and Functions, Relations and Posets, Graph Theory, Combinatorics and Recurrence Relations |
| CS2101 | Object Oriented Programming through Java | Core | 3 | Java Fundamentals, OOP Concepts (Encapsulation, Inheritance, Polymorphism), Abstract Classes and Interfaces, Exception Handling, Multithreading and Collections |
| CS2102 | Database Management Systems | Core | 3 | Relational Model and SQL, ER Diagrams and Schema Design, Normalization, Transaction Management, Concurrency Control and Recovery |
| DS2101 | Introduction to Data Science | Core | 3 | Data Science Life Cycle, Data Collection and Preprocessing, Exploratory Data Analysis, Data Visualization Fundamentals, Introduction to Machine Learning |
| ES2101 | Digital Logic Design | Core | 3 | Number Systems and Codes, Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits (Flip-Flops, Counters), Memory and Programmable Logic |
| HS2101 | Business English and Communication Skills | Core | 1.5 | Oral Communication Skills, Group Discussions and Presentations, Interview Skills, Report Writing, Email and Business Correspondence |
| CS2103 | Object Oriented Programming through Java Lab | Lab | 1.5 | Implementing OOP Concepts in Java, Exception Handling Programs, Multithreading Applications, Collection Framework Usage, GUI Programming with AWT/Swing |
| CS2104 | Database Management Systems Lab | Lab | 1.5 | SQL Commands (DDL, DML, DCL), Advanced SQL Queries, Database Schema Creation, PL/SQL Programming, Trigger and Stored Procedure Implementation |
| DS2102 | Data Science Lab | Lab | 1.5 | Python for Data Manipulation (Pandas), Data Cleaning Techniques, Basic Data Visualization (Matplotlib, Seaborn), Feature Engineering, Simple Statistical Analysis |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS2201 | Data Mining | Core | 3 | Data Preprocessing and Data Warehousing, Association Rule Mining, Classification Techniques, Clustering Algorithms, Web Mining and Text Mining |
| CS2201 | Operating Systems | Core | 3 | Operating System Concepts, Process Management and CPU Scheduling, Memory Management, File Systems and I/O Systems, Deadlocks and Concurrency |
| CS2202 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis and Complexity, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms and NP-Completeness |
| CS2203 | Computer Networks | Core | 3 | Network Topologies and Models (OSI, TCP/IP), Physical and Data Link Layer Protocols, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS) |
| DS2202 | Advanced Python Programming | Core | 3 | Advanced Data Structures in Python, Decorators and Generators, Web Frameworks (e.g., Flask, Django), API Development and Consumption, Testing and Debugging in Python |
| MC2201 | Constitution of India | Mandatory Non-Credit | 0 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Government Structure, Judiciary and Electoral System, Constitutional Amendments |
| DS2203 | Data Mining Lab | Lab | 1.5 | Data Preprocessing using Python/R, Implementing Association Rules, Classification Algorithms (Decision Trees, Naive Bayes), Clustering Algorithms (K-Means, Hierarchical), Using Data Mining Tools (Weka, Scikit-learn) |
| CS2204 | Operating Systems Lab | Lab | 1.5 | Shell Scripting, Process Management Commands, CPU Scheduling Algorithms Implementation, Memory Management Techniques, Deadlock Avoidance Algorithms |
| DS2204 | Advanced Python Programming Lab | Lab | 1.5 | Data Analysis with Advanced Pandas, Web Scraping with Beautiful Soup, Building Simple Web Applications, Database Connectivity in Python, Multithreading and Asynchronous Programming |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS3101 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Ensemble Methods (Bagging, Boosting), Bias-Variance Trade-off |
| DS3102 | Big Data Technologies | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases, Data Warehousing and Data Lakes |
| DS3103 | Artificial Intelligence | Core | 3 | AI Agents and Intelligent Systems, Problem Solving by Search (informed/uninformed), Knowledge Representation and Reasoning, Expert Systems, Introduction to Machine Learning |
| DS3104 | Professional Elective - I (Computer Vision) | Elective | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Image Segmentation, Object Recognition, Deep Learning for Computer Vision |
| OE3101 | Open Elective - I | Elective | 3 | |
| DS3105 | Machine Learning Lab | Lab | 1.5 | Linear and Logistic Regression Implementation, Decision Tree and SVM Algorithms, K-Means Clustering, Model Selection and Hyperparameter Tuning, Using Scikit-learn and TensorFlow/Keras |
| DS3106 | Big Data Technologies Lab | Lab | 1.5 | Hadoop HDFS Operations, MapReduce Programming, Spark RDD and DataFrame Operations, Hive/Pig Latin Queries, Introduction to NoSQL (MongoDB/Cassandra) |
| HS3101 | Universal Human Values | Core | 1.5 | Understanding Harmony in Self, Harmony in Family and Society, Harmony in Nature and Existence, Professional Ethics, Holistic Development |
| DS3107 | Skill Oriented Course - I (Web Technologies Lab) | Lab | 1.5 | HTML5 and CSS3, JavaScript Fundamentals, DOM Manipulation, Introduction to Front-end Frameworks (e.g., Bootstrap), Backend Basics with Node.js/Python |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS3201 | Deep Learning | Core | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow, PyTorch), Optimization and Regularization Techniques |
| DS3202 | Data Visualization | Core | 3 | Principles of Effective Data Visualization, Types of Visualizations, Tools for Visualization (Tableau, Power BI, D3.js), Interactive Dashboards, Storytelling with Data |
| DS3203 | Professional Elective - II (Natural Language Processing) | Elective | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Syntactic and Semantic Analysis, Sentiment Analysis, Text Generation and Machine Translation |
| DS3206 | Professional Elective - III (Cloud Computing for Data Science) | Elective | 3 | Cloud Computing Paradigms (IaaS, PaaS, SaaS), Cloud Providers (AWS, Azure, GCP) Basics, Cloud Storage and Databases, Serverless Computing for Data Workflows, Deployment of ML Models on Cloud |
| OE3201 | Open Elective - II | Elective | 3 | |
| DS3209 | Deep Learning Lab | Lab | 1.5 | Building ANNs for Classification, Implementing CNNs for Image Recognition, RNNs for Sequence Data, Transfer Learning Techniques, Hyperparameter Optimization |
| DS3210 | Data Visualization Lab | Lab | 1.5 | Creating Static and Interactive Plots with Matplotlib/Seaborn, Building Dashboards with Tableau/Power BI, Geospatial Data Visualization, Time Series Visualizations, Customizing Visualizations |
| DS3211 | Skill Oriented Course - II (Mobile App Development) | Lab | 1.5 | Android Studio Fundamentals, UI/UX Design for Mobile Apps, Activities and Intents, Data Storage (SQLite, Shared Preferences), Connecting to APIs |
| DS3212 | Mini Project | Project | 1.5 | Problem Identification and Scoping, Literature Survey, System Design and Implementation, Testing and Evaluation, Project Report and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS4101 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning (DQN, A2C), Exploration vs Exploitation |
| DS4102 | Data Engineering | Core | 3 | Data Pipelines and ETL Processes, Data Warehousing Concepts, Data Lake Architecture, Stream Processing (Kafka, Flink), Data Governance and Security |
| DS4103 | Professional Elective - IV (Time Series Analysis and Forecasting) | Elective | 3 | Time Series Components (Trend, Seasonality), Stationarity and ARIMA Models, Exponential Smoothing Methods, Forecasting Techniques, Deep Learning for Time Series |
| OE4101 | Open Elective - III | Elective | 3 | |
| HS4101 | Professional Ethics & IPR | Core | 1.5 | Ethical Theories and Professionalism, Cyber Ethics and Data Privacy, Intellectual Property Rights (Patents, Copyrights), Trade Secrets and Trademarks, Professional Code of Conduct |
| DS4106 | Reinforcement Learning Lab | Lab | 1.5 | Implementing Q-Learning for simple environments, SARSA Algorithm, Policy Gradient Methods, Using OpenAI Gym for RL simulations, Deep Q-Network implementation |
| DS4107 | Data Engineering Lab | Lab | 1.5 | Building ETL pipelines with Python, Working with Apache Nifi/Airflow, Implementing Stream Processing with Kafka, Data Quality and Validation, Data Lake Storage and Management |
| DS4108 | Skill Oriented Course - III (DevOps for Data Science) | Lab | 1.5 | Introduction to DevOps for ML, Containerization with Docker, Orchestration with Kubernetes, Continuous Integration/Continuous Deployment (CI/CD), Monitoring and Logging for ML Workflows |
| DS4109 | Internship (2 Months) | Internship | 3 | Industry Exposure, Practical Application of Skills, Problem Solving in Real-world Scenarios, Report Writing and Presentation, Professional Networking |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS4201 | Professional Elective - V (IoT Analytics) | Elective | 3 | IoT Architecture and Protocols, Sensor Data Acquisition and Processing, Edge and Fog Computing, Machine Learning on IoT Data, IoT Data Security and Privacy |
| DS4204 | Professional Elective - VI (Explainable AI (XAI)) | Elective | 3 | Interpretability vs Explainability, Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Model-agnostic Explanations, Interpreting Deep Learning Models |
| OE4201 | Open Elective - IV | Elective | 3 | |
| DS4207 | Major Project | Project | 7.5 | In-depth Problem Formulation, Advanced System Design and Architecture, Implementation with Latest Technologies, Rigorous Testing and Performance Evaluation, Comprehensive Project Report and Viva-Voce |
