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M-SC in Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
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About the Specialization
What is Data Science at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This M.Sc. Data Science program at Koneru Lakshmaiah Education Foundation focuses on equipping students with advanced theoretical knowledge and practical skills in data analysis, machine learning, and big data technologies. The curriculum is designed to meet the growing demand for skilled data scientists in the Indian industry, covering key differentiators like deep learning, cloud computing for data science, and specialized electives relevant to modern data-driven enterprises.
Who Should Apply?
This program is ideal for fresh graduates with a background in Computer Science, IT, or related disciplines seeking entry into the booming data science field. It also caters to working professionals looking to upskill their analytical capabilities or career changers transitioning into roles such as Data Scientist, Machine Learning Engineer, or Data Analyst in various Indian industries.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths in IT services, e-commerce, banking, healthcare, and manufacturing sectors. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals commanding significantly higher packages. The program fosters growth trajectories into lead data scientist, AI/ML architect, or data consultant roles, aligning with industry demand for expertise in advanced analytics.
Student Success Practices
Foundation Stage
Master Programming & Mathematical Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly understand Python programming and the mathematical foundations (linear algebra, calculus, probability) that underpin data science. Utilize online platforms like HackerRank, LeetCode, and GeeksforGeeks for consistent coding practice and problem-solving, alongside reviewing core math concepts via Khan Academy or NPTEL lectures.
Tools & Resources
Python Official Documentation, NumPy/Pandas Guides, Khan Academy, NPTEL, HackerRank, GeeksforGeeks
Career Connection
A strong foundation in these areas is crucial for success in interviews for data scientist or machine learning engineer roles and for building complex analytical models.
Active Participation in Labs and Problem-Solving- (Semester 1-2)
Actively engage in all laboratory sessions, treating them as opportunities to apply theoretical knowledge to practical scenarios. Form study groups with peers to discuss challenging problems, debug code collaboratively, and share different approaches to solutions. This enhances critical thinking and teamwork skills.
Tools & Resources
Jupyter Notebook, Google Colab, GitHub, Peer Study Groups
Career Connection
Practical application skills are highly valued by employers in India, demonstrating your ability to translate theory into actionable insights and robust solutions.
Build a Strong Project Portfolio Early- (Semester 1-2)
Start building a personal portfolio of small data science projects from the first year. Even simple projects applying concepts from Python, data structures, and visualization can showcase your foundational skills. Publish these projects on GitHub and document your approach, code, and findings clearly.
Tools & Resources
GitHub, Kaggle Datasets, Medium for project documentation
Career Connection
An early project portfolio is a significant advantage for internships and entry-level placements, providing tangible evidence of your capabilities and initiative.
Intermediate Stage
Engage in Applied Machine Learning & Big Data Projects- (Semester 3)
Focus on applying Machine Learning and Big Data technologies to real-world datasets. Participate in hackathons, Kaggle competitions, or college-level data challenges. This practical experience is vital for understanding data preprocessing, model building, and deploying solutions in a large-scale context.
Tools & Resources
Kaggle, DataHack, Google Cloud Platform/AWS Free Tier, Apache Spark
Career Connection
Hands-on experience with complex projects improves problem-solving abilities and makes you a stronger candidate for specialized roles in ML and Big Data.
Seek Internships and Industry Exposure- (Semester 3)
Actively seek and undertake internships with Indian startups, tech companies, or data analytics firms. Internships provide invaluable exposure to industry practices, company culture, and networking opportunities. Even unpaid internships offer learning experiences that are highly beneficial.
Tools & Resources
LinkedIn, Internshala, College Placement Cell, Naukri.com
Career Connection
Internships are often a direct pathway to pre-placement offers and provide a practical understanding of how theoretical knowledge is applied in a professional setting.
Specialize through Electives and Advanced Learning- (Semester 3)
Strategically choose your professional electives based on your career interests (e.g., NLP, Computer Vision, Business Intelligence). Supplement classroom learning with MOOCs from platforms like Coursera, edX, or Udemy to gain deeper expertise in chosen niches and stay updated with emerging technologies.
Tools & Resources
Coursera, edX, Udemy, Specialized Research Papers (arXiv)
Career Connection
Specialized skills differentiate you in a competitive job market, enabling you to target specific roles and command better compensation in your area of expertise.
Advanced Stage
Undertake a Comprehensive Capstone Project- (Semester 3-4)
Invest deeply in your final year project (Phase I and II), aiming to solve a significant problem or develop an innovative solution. Focus on end-to-end implementation, rigorous testing, and clear documentation. This project should be the highlight of your portfolio, demonstrating your full skill set.
Tools & Resources
Git/GitHub, Jira/Trello for project management, Cloud platforms (AWS/Azure/GCP)
Career Connection
A well-executed capstone project can serve as a powerful talking point in interviews, showcasing your ability to deliver a complete data science solution.
Intensive Placement Preparation & Mock Interviews- (Semester 4)
Begin intensive preparation for placements well in advance. Focus on revamping your resume, practicing aptitude tests, and participating in numerous mock interviews, both technical and HR. Understand common data science interview questions, case studies, and behavioral aspects specific to Indian companies.
Tools & Resources
Glassdoor, GeeksforGeeks Interview Corner, LeetCode, Professional Career Counsellors
Career Connection
Thorough preparation significantly increases your chances of securing placements with leading companies and negotiating better offers.
Network and Stay Updated with Industry Trends- (Semester 3-4)
Attend industry conferences, webinars, and workshops related to data science and AI. Connect with alumni and professionals on platforms like LinkedIn. Staying updated with the latest trends, tools, and research in India''''s tech landscape is crucial for long-term career growth and adaptability.
Tools & Resources
LinkedIn, Meetup groups, Data Science blogs, Tech Conferences in India (e.g., GIDS)
Career Connection
Networking opens doors to new opportunities, mentorship, and helps in understanding evolving market demands, which is essential for career advancement.
Program Structure and Curriculum
Eligibility:
- B.Tech. in Computer Science & Engineering / Information Technology / M.C.A. / M.Sc. (Computer Science) or any other equivalent degree with minimum of 60% aggregate marks.
Duration: 2 years (4 semesters)
Credits: 61 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS1001 | Mathematical Foundations for Data Science | Core | 4 | Probability & Statistics, Random Variables & Distributions, Regression & Correlation, Linear Algebra, Multivariable Calculus |
| 22DS1002 | Python for Data Science | Core | 4 | Python Language Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming, NumPy and Pandas for Data Manipulation |
| 22DS1003 | Data Structures and Algorithms | Core | 4 | Abstract Data Types, Linear Data Structures (Stacks, Queues, Linked Lists), Non-Linear Data Structures (Trees, Graphs), Searching and Sorting Algorithms, Hashing Techniques |
| 22DS1004 | Data Visualization | Core | 3 | Introduction to Data Visualization, Visualization Techniques, Tools and Libraries (Matplotlib, Seaborn), Interactive Visualization, Data Storytelling |
| 22DS1005 | Database Management Systems | Core | 4 | DBMS Concepts and Architecture, Relational Model and Algebra, SQL Queries and Operations, Transaction Management, Database Security and Backup |
| 22DS1051 | Python for Data Science Lab | Lab | 1 | Python Environment Setup, Data Types and Structures Exercises, Control Flow and Functions Practice, NumPy Array Operations, Pandas Dataframe Manipulation |
| 22DS1052 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linear Data Structures, Implementation of Non-Linear Data Structures, Searching Algorithms, Sorting Algorithms, Hashing Implementation |
| 22DS1053 | Data Visualization Lab | Lab | 1 | Basic Plotting with Matplotlib, Advanced Matplotlib Visualizations, Statistical Plots with Seaborn, Interactive Data Visualization, Dashboard Creation Tools |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS1006 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning Algorithms (Regression, Classification), Unsupervised Learning Algorithms (Clustering), Model Evaluation and Selection, Ensemble Methods |
| 22DS1007 | Big Data Technologies | Core | 4 | Introduction to Big Data Concepts, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark Framework, NoSQL Databases (MongoDB, Cassandra), Data Processing with Hive and Pig |
| 22DS1008 | Cloud Computing for Data Science | Core | 3 | Cloud Computing Basics, Service Models (IaaS, PaaS, SaaS), Deployment Models, Cloud Data Services (AWS S3, Azure Data Lake), Security and Compliance in Cloud |
| 22DS1009 | Deep Learning | Core | 4 | Introduction to Deep Learning and Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow, PyTorch), Transfer Learning and Optimization |
| 22DS10E1 | Professional Elective I | Elective | 3 | Natural Language Processing, Computer Vision, Reinforcement Learning |
| 22DS1054 | Machine Learning Lab | Lab | 1 | Linear and Logistic Regression Implementation, Decision Trees and SVM Practice, Clustering Algorithms Implementation, Model Selection and Hyperparameter Tuning, Ensemble Methods Application |
| 22DS1055 | Big Data Technologies Lab | Lab | 1 | Hadoop HDFS Commands and MapReduce Programs, Apache Pig and Hive Queries, Spark RDD and DataFrame Operations, NoSQL Database CRUD Operations, Building Simple Big Data Pipelines |
| 22DS1056 | Deep Learning Lab | Lab | 1 | Neural Network Implementation from Scratch, Convolutional Neural Networks for Image Data, Recurrent Neural Networks for Sequence Data, Transfer Learning with Pre-trained Models, Hyperparameter Optimization for Deep Models |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS20E2 | Professional Elective II | Elective | 3 | Data Governance and Ethics, Time Series Analysis, Explainable AI |
| 22DS20E3 | Professional Elective III | Elective | 3 | Business Intelligence, IoT Analytics, Edge Analytics |
| 22DS20P1 | Project Work - Phase I | Project | 6 | Problem Identification and Formulation, Literature Survey and State-of-Art Analysis, Methodology Design and Planning, Initial System Design and Architecture, Feasibility Study and Tool Selection |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS20P2 | Project Work - Phase II | Project | 6 | System Development and Implementation, Testing and Validation, Result Analysis and Interpretation, Report Writing and Documentation, Final Presentation and Viva Voce |
