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M-SC in General at University of Petroleum and Energy Studies
Dehradun, Uttarakhand
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About the Specialization
What is General at University of Petroleum and Energy Studies Dehradun?
This M.Sc. Data Science program at UPES focuses on equipping students with advanced statistical, computational, and analytical skills. It emphasizes practical application of machine learning, deep learning, and big data technologies to solve real-world problems. The curriculum is designed to meet the growing demand for skilled data scientists in the rapidly evolving Indian digital economy and global markets.
Who Should Apply?
This program is ideal for fresh graduates with a background in engineering, science, or computer applications seeking entry into the data science field. It also suits working professionals looking to upskill in analytics or transition to data-centric roles. Candidates with strong quantitative aptitude and a desire to work with complex datasets will find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as Data Scientists, Machine Learning Engineers, Data Analysts, or AI Specialists across various sectors. Entry-level salaries typically range from INR 5-8 lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry demand for professionals capable of driving data-driven decision-making and innovation.
Student Success Practices
Foundation Stage
Master Core Programming and Statistics- (Semester 1-2)
Dedicate significant time to Python programming (NumPy, Pandas) and statistical concepts, as these form the bedrock of data science. Practice daily coding challenges and revisit statistical theorems to build a strong analytical foundation.
Tools & Resources
HackerRank, LeetCode, Kaggle, GeeksforGeeks, Khan Academy Statistics
Career Connection
A strong foundation ensures proficiency in core technical interviews and complex project execution, making you a more attractive candidate for entry-level data science roles.
Active Participation in Peer Learning Groups- (Semester 1-2)
Form study groups to discuss complex topics, share code, and collaboratively solve problems. Teaching peers reinforces your understanding and exposes you to different problem-solving approaches.
Tools & Resources
WhatsApp groups, Discord servers, Google Meet for discussions
Career Connection
Enhances communication skills and teamwork, crucial for collaborative data science projects in corporate environments and for clearing group discussion rounds during placements.
Engage with Introductory Data Science Projects- (Semester 1-2)
Start working on small, self-contained data science projects using publicly available datasets. Focus on end-to-end implementation from data cleaning to basic model building and visualization.
Tools & Resources
Kaggle Datasets, UCI Machine Learning Repository, GitHub for version control
Career Connection
Builds a practical portfolio, demonstrates initiative, and provides tangible examples of your skills to showcase during internships and job applications, especially for Indian startups and SMEs.
Intermediate Stage
Specialized Skill Development through Electives- (Semester 3)
Carefully select electives that align with your career interests (e.g., NLP, Computer Vision, MLOps). Deep dive into these chosen areas through specialized online courses and projects.
Tools & Resources
Coursera (DeepLearning.AI), Udemy, NPTEL for advanced topics, GitHub for niche projects
Career Connection
Develops a unique skill set sought after by companies looking for specialized roles, giving you a competitive edge in focused job markets within India (e.g., NLP engineer in tech, MLOps engineer in product companies).
Seek Industry Exposure via Internships and Workshops- (Semester 3-4 (Summer break after Sem 2, during Sem 3))
Actively apply for internships (paid or unpaid) in data science roles during summer breaks or semester breaks. Attend industry workshops, webinars, and conferences to network and understand current trends.
Tools & Resources
LinkedIn, Internshala, Naukri.com, College placement cell, Meetup events
Career Connection
Gains invaluable real-world experience, builds professional networks, and often leads to pre-placement offers (PPOs), significantly boosting your chances for a successful career start in India.
Contribute to Open Source or Personal Portfolios- (Semester 2-3)
Contribute to open-source data science projects or build and maintain a strong personal portfolio on GitHub. This demonstrates your ability to work on larger projects and collaborate effectively.
Tools & Resources
GitHub, Bitbucket, Personal blog/website to showcase projects
Career Connection
Showcases practical skills, problem-solving abilities, and commitment to the field, making you stand out to recruiters and hiring managers in Indian tech companies and startups.
Advanced Stage
Intensive Capstone Project and Dissertation Focus- (Semester 3-4)
Approach the Capstone Project and Dissertation as a real-world problem-solving exercise. Aim for innovative solutions, meticulous documentation, and impactful results. Consider publishing a research paper.
Tools & Resources
Academic databases (IEEE Xplore, ACM Digital Library), Jupyter Notebooks, Google Colab, LaTeX
Career Connection
A well-executed project demonstrates mastery, critical thinking, and research capabilities, which are highly valued by R&D departments, advanced analytics teams, and academic roles in India.
Targeted Placement Preparation and Mock Interviews- (Semester 4)
Begin rigorous preparation for placements by practicing aptitude tests, technical interviews (coding, ML concepts), and HR rounds. Participate in mock interviews with faculty, alumni, or peers.
Tools & Resources
Glassdoor, GeeksforGeeks for interview prep, Pramp (peer interview platform), College placement cell resources
Career Connection
Maximizes your chances of securing placements with top-tier companies in India. Thorough preparation instills confidence and hones interview skills essential for competitive job markets.
Develop Leadership and Mentorship Skills- (Semester 3-4)
Mentor junior students, lead team projects, or organize data science-related events on campus. Focus on developing soft skills such as leadership, negotiation, and cross-functional communication.
Tools & Resources
Student clubs, College fests, Mentorship programs
Career Connection
Prepares you for leadership roles and team management responsibilities in the long run. These skills are highly valued for career growth beyond entry-level positions in the Indian corporate sector.
Program Structure and Curriculum
Eligibility:
- Minimum 50% marks in Class X, XII and B.Tech. / B.Sc. / B.Sc. (Hons.) / BCA / equivalent. Selection criteria based on Entrance Examination.
Duration: 4 semesters / 2 years
Credits: 93 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MSC1001 | Linear Algebra for Data Science | Core | 4 | Vector Spaces and Subspaces, Linear Transformations and Matrices, Matrix Decomposition (LU, QR, SVD), Eigenvalues and Eigenvectors, Applications in Data Science and Machine Learning |
| MSC1002 | Advanced Statistics for Data Science | Core | 4 | Probability Theory and Distributions, Hypothesis Testing and Confidence Intervals, ANOVA and Chi-Square Tests, Regression Analysis and Correlation, Non-parametric Methods and Time Series Fundamentals |
| MSC1003 | Python Programming for Data Science | Core | 4 | Python Fundamentals and Data Structures, NumPy for Numerical Computing, Pandas for Data Manipulation and Analysis, Data Visualization with Matplotlib and Seaborn, Functions, Modules, and Object-Oriented Programming |
| MSC1004 | Database Management Systems for Data Science | Core | 4 | Relational Database Concepts and SQL, NoSQL Databases (MongoDB, Cassandra), Database Design and Normalization, Data Warehousing and OLAP, Data Extraction and ETL Processes |
| MSC1005 | Research Methodology | Core | 3 | Research Design and Problem Formulation, Data Collection Methods (Quantitative & Qualitative), Sampling Techniques, Statistical Analysis for Research, Report Writing and Presentation |
| MSC1006 | Professional Communication | Core | 2 | Verbal and Non-verbal Communication, Written Communication (Reports, Emails), Presentation Skills and Public Speaking, Interpersonal Communication and Teamwork, Professional Ethics and Etiquette |
| MSC1007 | Python Programming Lab | Lab | 2 | Python programming exercises, Data manipulation with Pandas, Data visualization tasks, Implementation of basic algorithms, Debugging and error handling |
| MSC1008 | Database Lab | Lab | 2 | SQL query practice, Database design and implementation, NoSQL database operations, Data retrieval and manipulation, Database connectivity |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MSC2001 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Hyperparameter Tuning, Ensemble Methods (Bagging, Boosting), Introduction to Neural Networks |
| MSC2002 | Big Data Technologies | Core | 4 | Hadoop Ecosystem (HDFS, MapReduce), Apache Spark for Data Processing, Data Ingestion and Streaming (Kafka, Flume), Distributed Data Storage (Hive, HBase), Cloud-based Big Data Services |
| MSC2003 | Deep Learning | Core | 4 | Artificial Neural Networks Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Backpropagation and Optimization Techniques, Transfer Learning and Fine-tuning |
| MSC2004 | Data Visualization | Core | 4 | Principles of Data Visualization, Data Storytelling and Infographics, Tools: Tableau, PowerBI, D3.js, Interactive Visualizations and Dashboards, Geospatial and Network Visualizations |
| MSC2005 | Data Science Elective I | Elective | 3 | Natural Language Processing (NLP), Computer Vision, Time Series Analysis, Reinforcement Learning |
| MSC2006 | Machine Learning Lab | Lab | 2 | Implementation of ML algorithms, Model training and evaluation, Hyperparameter tuning experiments, Predictive modeling projects, Feature engineering techniques |
| MSC2007 | Big Data Lab | Lab | 2 | Hadoop and Spark ecosystem practice, Distributed data processing tasks, Data ingestion and storage solutions, MapReduce programming, Cluster management basics |
| MSC2008 | Deep Learning Lab | Lab | 2 | Neural network implementation, Image classification using CNNs, Sequence modeling with RNNs, Model optimization and regularization, Transfer learning applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MSC3001 | Cloud Computing for Data Science | Core | 4 | Cloud Platforms (AWS, Azure, GCP) Overview, Cloud Storage and Databases (S3, RDS, BigQuery), Serverless Computing (Lambda, Functions), Containerization (Docker, Kubernetes), Data Science Services in Cloud (SageMaker, AutoML) |
| MSC3002 | Data Governance and Ethics | Core | 3 | Data Privacy and Protection Regulations (GDPR, India''''s DPA), Data Security and Compliance, Ethical AI Principles and Bias Mitigation, Data Quality Management, Responsible Data Handling Practices |
| MSC3003 | Capstone Project - Part 1 | Project | 6 | Problem Definition and Scope, Literature Review and Gap Analysis, Methodology Design and Data Collection Strategy, Preliminary Data Analysis and Cleaning, Proposal Writing and Presentation |
| MSC3004 | Data Science Elective II | Elective | 3 | Advanced NLP, Generative AI, MLOps, Business Intelligence |
| MSC3005 | Data Science Elective III | Elective | 3 | IoT Analytics, Financial Analytics, Geospatial Data Science, Healthcare Analytics |
| MSC3006 | Cloud Computing Lab | Lab | 2 | Cloud service deployment practice, Working with cloud data storage solutions, Developing serverless functions, Implementing cloud security configurations, Monitoring and managing cloud resources |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MSC4001 | Capstone Project - Part 2 | Project | 10 | Model Development and Implementation, Extensive Experimentation and Testing, Results Analysis and Interpretation, Documentation of Project Findings, Final Presentation and Defense |
| MSC4002 | Dissertation/Industrial Training/Internship | Project | 12 | Undertaking an industry-specific project, In-depth research and thesis writing, Gaining professional experience, Application of learned data science concepts, Mentored work in a corporate or research setting |
