.png&w=1920&q=75)
M-TECH in Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
.png&w=1920&q=75)
About the Specialization
What is Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This M.Tech Data Science program at Koneru Lakshmaiah Education Foundation focuses on equipping students with advanced skills in data analysis, machine learning, and big data technologies. It addresses the burgeoning demand for skilled data professionals in the Indian market, offering a robust curriculum that blends theoretical foundations with practical applications. The program aims to create industry-ready experts capable of solving complex data-driven problems.
Who Should Apply?
This program is ideal for engineering graduates, especially those with B.E./B.Tech in CSE/IT/ECE/EEE, or MCA/M.Sc.(CS/IT)/Data Science, who aspire to build a career in the rapidly evolving data science domain. It caters to fresh graduates seeking entry into analytics roles and working professionals looking to upskill in AI/ML, data engineering, or advanced data analysis, enabling them to transition into leadership positions.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as Data Scientists, Machine Learning Engineers, Data Analysts, and Big Data Architects, with entry-level salaries typically ranging from INR 6-10 LPA, growing significantly with experience. The rigorous curriculum prepares students for roles in IT services, e-commerce, banking, healthcare, and telecom, aligning with industry-recognized certifications in AI/ML and cloud platforms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to Python programming with libraries like NumPy, Pandas, and Scikit-learn. Focus on writing efficient code, understanding data structures, and implementing basic algorithms required for machine learning. Participate in coding challenges on platforms to hone problem-solving skills.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, Python documentation, Anaconda
Career Connection
Strong programming skills are foundational for any data science role, crucial for data manipulation, model building, and deploying solutions in industry, directly impacting employability and interview performance.
Build Strong Mathematical & Statistical Basics- (Semester 1-2)
Consistently review and strengthen concepts in linear algebra, calculus, probability, and statistics. Understand the mathematical intuition behind machine learning algorithms rather than just using libraries. Utilize online courses and textbooks to clarify concepts and practice numerical problems.
Tools & Resources
Khan Academy, NPTEL courses, Deep Learning by Goodfellow et al., Probability and Statistics for Engineers by Miller and Freund
Career Connection
A solid theoretical understanding differentiates a data scientist from a mere tool-user, enabling advanced problem-solving, algorithm selection, and interpretation of model results, critical for innovative roles.
Engage in Data Science Communities- (Semester 1-2)
Actively participate in online data science communities and forums like Kaggle, Analytics Vidhya, and Stack Overflow. Discuss concepts, ask questions, and contribute to discussions. Attend local meetups or virtual webinars to network with peers and industry experts, fostering a collaborative learning environment.
Tools & Resources
Kaggle, LinkedIn Groups, Data Science Central, Meetup.com
Career Connection
Networking opens doors to internships, mentorships, and job opportunities, while community engagement keeps skills sharp and abreast of industry trends, highly valued by recruiters.
Intermediate Stage
Apply Knowledge through Mini-Projects- (Semester 2-3)
Beyond lab exercises, undertake independent or group mini-projects using real-world datasets. Focus on end-to-end project cycles: data collection, cleaning, EDA, model building, and visualization. Document your process thoroughly and build a portfolio on platforms like GitHub.
Tools & Resources
GitHub, Kaggle datasets, UCI Machine Learning Repository, Google Colab
Career Connection
A strong project portfolio demonstrates practical skills and problem-solving abilities to potential employers, making you a more attractive candidate for internships and full-time roles in India''''s competitive tech landscape.
Gain Hands-on with Big Data & Cloud Platforms- (Semester 2-3)
Explore and implement concepts of Big Data technologies like Hadoop and Spark, and gain practical experience with cloud platforms such as AWS, Azure, or GCP. Understand how to deploy data science models and manage large datasets in a cloud environment.
Tools & Resources
AWS Educate, Azure for Students, Google Cloud Free Tier, Apache Hadoop documentation, Spark documentation
Career Connection
Proficiency in Big Data and cloud technologies is a high-demand skill in Indian IT firms, enabling roles in data engineering, MLOps, and scalable analytics solutions.
Pursue Relevant Internships- (Semester 3)
Actively seek out internships in data science, machine learning engineering, or data analytics roles within Indian companies or MNCs with local operations. This provides invaluable industry exposure, allows application of academic learning, and helps build a professional network.
Tools & Resources
Internshala, LinkedIn Jobs, Company career pages, College placement cell
Career Connection
Internships are often direct pathways to pre-placement offers, provide real-world project experience, and significantly enhance resumes for full-time opportunities post-graduation in the Indian market.
Advanced Stage
Specialize and Deepen Expertise- (Semester 3-4)
Choose electives and project topics that align with your career interests (e.g., NLP, Computer Vision, Recommender Systems). Deep dive into advanced concepts, research papers, and cutting-edge techniques in your chosen sub-field. Aim to present research or contribute to open-source projects.
Tools & Resources
ArXiv, Google Scholar, IEEE Xplore, specific research communities in your chosen domain
Career Connection
Specialization makes you a valuable asset for niche roles and advanced R&D positions, offering higher earning potential and more challenging work in specialized Indian tech firms and startups.
Focus on Major Project and Thesis- (Semester 3-4)
Devote substantial effort to the Major Project, ensuring it addresses a significant real-world problem or contributes novel insights. Document the project thoroughly as a thesis, emphasizing methodology, results, and implications. Prepare for a strong viva voce presentation.
Tools & Resources
LaTeX, Grammarly, institutional research guidelines, academic mentors
Career Connection
A well-executed and documented major project serves as the ultimate demonstration of your skills, critical for showcasing abilities to potential employers and for academic progression if desired.
Prepare for Placements and Interviews- (Semester 4)
Regularly practice technical interview questions, revise core data science concepts, and work on behavioral interview skills. Participate in mock interviews, refine your resume and LinkedIn profile, and prepare compelling case studies from your projects.
Tools & Resources
InterviewBit, LeetCode for DS/Algo, Glassdoor, LinkedIn, college placement workshops
Career Connection
Dedicated and strategic placement preparation is paramount for securing desirable job offers from top companies in India, maximizing your chances for a successful career launch.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in CSE/IT/ECE/EEE or equivalent/MCA/M.Sc.(CS/IT)/Data Science with minimum 60% marks or equivalent CGPA.
Duration: 2 years (4 semesters)
Credits: 70 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20MT1001 | Mathematical Foundations for Data Science | Core | 4 | Linear Algebra, Calculus and Optimization, Probability and Random Variables, Statistical Inference, Hypothesis Testing |
| 20MT1002 | Advanced Data Structures & Algorithms | Core | 4 | Algorithm Analysis, Hashing Techniques, Trees and Heaps, Graph Algorithms, Dynamic Programming |
| 20MT1003 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation and Validation, Ensemble Methods |
| 20MT1004 | Data Preparation & Feature Engineering | Core | 4 | Data Collection and Acquisition, Data Cleaning and Preprocessing, Data Transformation, Feature Selection, Feature Construction |
| 20MT10L1 | Advanced Data Structures & Algorithms Lab | Lab | 2 | Implementation of Trees, Graph Traversal Algorithms, Hashing Implementations, Dynamic Programming Problems, Search and Sort Algorithms |
| 20MT10L2 | Machine Learning Lab | Lab | 2 | Python for ML, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Evaluation Metrics, Data Preprocessing Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20MT2001 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, Hive and Pig, NoSQL Databases |
| 20MT2002 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders and GANs, Deep Learning Frameworks |
| 20MT2003 | Data Visualization | Core | 4 | Principles of Visualization, Exploratory Data Analysis, Data Storytelling, Tableau/Power BI, D3.js for Web Visualization |
| 20MT2E01 | Natural Language Processing (Example Elective) | Elective | 4 | Text Preprocessing, Word Embeddings, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Models |
| 20MT2E07 | Recommender Systems (Example Elective) | Elective | 4 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommender Systems, Matrix Factorization, Evaluation Metrics |
| 20MT20L1 | Big Data Technologies Lab | Lab | 2 | Hadoop Ecosystem Setup, MapReduce Programming, Apache Spark Implementations, HiveQL Queries, Data Ingestion Tools |
| 20MT20L2 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras Programming, CNN Model Implementation, RNN and LSTM Networks, Transfer Learning Techniques, Hyperparameter Tuning |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20MT3E01 | Cloud Computing for Data Science (Example Elective) | Elective | 4 | Cloud Architectures, AWS/Azure/GCP Services, Distributed Data Processing, Serverless Computing, Cloud Data Security |
| 20MT30MJ | Major Project Phase - I | Project | 8 | Problem Identification, Literature Survey, Methodology Design, Initial Data Collection, Preliminary Implementation |
| 20MT30IN | Internship | Project | 4 | Industry Exposure, Real-world Project Implementation, Skill Application, Technical Report Writing, Professional Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20MT40MJ | Major Project Phase - II | Project | 16 | Advanced System Design, Comprehensive Implementation, Testing and Evaluation, Results Analysis and Discussion, Thesis and Dissertation Writing |
