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M-SC in Big Data Analytics at St Aloysius College (Autonomous)
Dakshina Kannada, Karnataka
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About the Specialization
What is Big Data Analytics at St Aloysius College (Autonomous) Dakshina Kannada?
This M.Sc Big Data Analytics program at St. Aloysius (Deemed to be University) focuses on equipping students with expertise in managing, processing, and interpreting large datasets. It is highly relevant in the Indian industry, which is experiencing exponential data growth across sectors like e-commerce, finance, and healthcare, driving significant demand for skilled data professionals. The program''''s blend of theoretical foundations and practical application prepares students for diverse challenges in the data-driven world.
Who Should Apply?
This program is ideal for fresh graduates holding a B.Sc in Computer Science/BCA/B.Sc IT, B.Voc in Software Development/IT/Data Science, or B.E/B.Tech in CS/IS/IT/AI-ML/Data Science, seeking entry into the thriving field of big data. It also caters to working professionals aiming to upskill and transition into advanced data analytics roles, providing them with the necessary tools and methodologies to excel in data-intensive environments.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as Data Scientists, Big Data Engineers, Machine Learning Engineers, Business Intelligence Analysts, and Data Analysts. Entry-level salaries in India typically range from INR 4-8 LPA, growing significantly with experience. The program aligns with industry demands, preparing students for roles in both Indian startups and large MNCs, with potential for advanced professional certifications in data science and cloud platforms.
Student Success Practices
Foundation Stage
Master Programming & Database Fundamentals- (Semester 1-2)
Diligent practice in Python programming, focusing on data structures, algorithms, and key libraries like Pandas and NumPy. Simultaneously, gain strong proficiency in SQL for advanced database management and explore NoSQL concepts through practical exercises.
Tools & Resources
LeetCode, HackerRank, GeeksforGeeks, SQLZoo, Mode Analytics tutorials, MongoDB/Cassandra documentation
Career Connection
A strong foundation in programming and databases is essential for any data role, directly impacting problem-solving abilities in technical interviews and on-the-job data manipulation tasks.
Build a Strong Statistical & Machine Learning Base- (Semester 1-2)
Focus on understanding core concepts of probability, statistics, and foundational machine learning algorithms. Implement these algorithms from scratch in Python to deepen understanding beyond just using libraries.
Tools & Resources
Khan Academy for Statistics, Coursera/edX courses (e.g., Andrew Ng''''s Machine Learning), Scikit-learn documentation, Jupyter Notebooks
Career Connection
These fundamental skills are critical for interpreting data, building predictive models, and are frequently tested in data science aptitude rounds and technical discussions.
Engage in Exploratory Data Analysis (EDA) Projects- (Semester 1-2)
Actively participate in mini-projects focusing on Exploratory Data Analysis (EDA) using publicly available datasets. Practice data cleaning, transformation, and visualization to uncover insights and patterns.
Tools & Resources
Kaggle Datasets, UCI Machine Learning Repository, Tableau Public, Power BI Desktop, Matplotlib, Seaborn
Career Connection
EDA is a vital skill for any data professional, enabling them to understand data characteristics, identify patterns, and communicate preliminary findings effectively, a key aspect of real-world data projects.
Intermediate Stage
Deep Dive into Advanced ML and Big Data Ecosystems- (Semester 3)
Explore advanced machine learning concepts like Deep Learning and Reinforcement Learning, implementing models using frameworks such as TensorFlow or PyTorch. Concurrently, gain hands-on experience with the Hadoop ecosystem, Spark, and cloud platforms for handling large datasets.
Tools & Resources
TensorFlow/PyTorch tutorials, Apache Spark documentation, AWS/Azure/GCP free tier accounts, Databricks Community Edition
Career Connection
Expertise in advanced ML and Big Data technologies is highly sought after for roles like Machine Learning Engineer, Big Data Engineer, and Data Scientist, differentiating candidates in a competitive market.
Cultivate Data Visualization & Storytelling Skills- (Semester 3)
Develop strong proficiency in data visualization tools and practice creating compelling data stories. Focus on designing interactive dashboards and presentations that effectively communicate complex analytical insights to diverse audiences.
Tools & Resources
Tableau Desktop, Power BI, D3.js, Storytelling with Data by Cole Nussbaumer Knaflic, Behance for inspiration
Career Connection
Effective communication of insights is as crucial as the analysis itself. Strong visualization and storytelling skills are essential for roles in Business Intelligence, Data Analysis, and Data Science, directly influencing decision-making.
Pursue Industry Internships & Capstone Projects- (Semester 3-4)
Actively seek out and complete internships in data science or big data roles. Dedicate significant effort to the major project/dissertation, treating it as a real-world problem-solving endeavor with practical outcomes.
Tools & Resources
University placement cell, LinkedIn, Internshala, Company career pages, GitHub for project portfolio
Career Connection
Internships provide invaluable industry exposure and practical experience, often leading to pre-placement offers. The capstone project serves as a strong portfolio piece demonstrating advanced skills and problem-solving capabilities to potential employers.
Advanced Stage
Specialize and Build a Portfolio Project- (Semester 4)
Choose electives wisely based on career interests (e.g., IoT Analytics, Financial Analytics, Business Intelligence) and develop a comprehensive portfolio project demonstrating expertise in your chosen area. This project should solve a real-world problem and showcase advanced techniques.
Tools & Resources
Kaggle competitions, Open-source project contributions, Specific industry datasets, GitHub for version control and project hosting
Career Connection
A strong, specialized portfolio project is crucial for showcasing depth of knowledge and practical application, making candidates highly attractive for specialized data science or analytics roles.
Master Interview Skills and Networking- (Semester 4)
Practice technical and behavioral interview questions rigorously, focusing on case studies, algorithm problems, and explaining project work. Actively network with industry professionals through LinkedIn, alumni events, and conferences.
Tools & Resources
LeetCode, Pramp, Mock interviews with peers/mentors, LinkedIn networking, University career services workshops
Career Connection
Strong interview performance and a robust professional network significantly enhance job placement opportunities, opening doors to desired roles and companies within the Indian data industry.
Continuous Learning and Certification- (Semester 4 and beyond)
Identify key industry certifications relevant to your career path (e.g., AWS Certified Data Analytics, Azure Data Scientist Associate, Google Cloud Professional Data Engineer) and prepare for them. Stay updated with emerging technologies and industry trends through online courses and tech blogs.
Tools & Resources
Coursera, edX, Udemy, Official certification guides, Tech blogs (e.g., Towards Data Science), Industry webinars
Career Connection
Industry certifications validate specialized skills, increasing employability and often leading to higher starting salaries. Continuous learning ensures long-term career growth in the rapidly evolving data science domain.
Program Structure and Curriculum
Eligibility:
- B.Sc. in Computer Science/BCA/B.Sc. IT with a minimum of 50% marks in aggregate from any recognized university. OR B.Voc. in Software Development/IT/Data Science with a minimum of 50% marks in aggregate from any recognized university. OR B.E/B.Tech in CS/IS/IT/AI-ML/Data Science with a minimum of 50% marks in aggregate from any recognized university.
Duration: 2 years (4 Semesters)
Credits: 84 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| P23BTA101 | Foundations of Data Science | Core | 4 | Introduction to Data Science, Data Types and Collection, Data Preprocessing, Data Visualization, Exploratory Data Analysis, Ethics in Data Science |
| P23BTA102 | Advanced Database Management Systems | Core | 4 | Relational Model and SQL, Query Processing, Transaction Management, Concurrency Control, Database Security, NoSQL Databases |
| P23BTA103 | Probability and Statistics for Data Science | Core | 4 | Probability Theory, Random Variables and Distributions, Hypothesis Testing, Regression Analysis, Correlation, ANOVA |
| P23BTA104 | Programming with Python | Core | 4 | Python Fundamentals, Data Structures, Control Flow and Functions, Modules and Packages, Object-Oriented Programming, File I/O and Exception Handling |
| P23BTA105P | Lab: Advanced DBMS | Lab | 2 | SQL Queries and Commands, Stored Procedures and Triggers, Views and Joins, Database Design, NoSQL Database Operations |
| P23BTA106P | Lab: Python Programming | Lab | 2 | Python Data Structures Implementation, Functions and OOP in Python, File Handling, Data Manipulation using Pandas, Data Visualization using Matplotlib/Seaborn |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| P23BTA201 | Data Warehousing and Data Mining | Core | 4 | Data Warehouse Concepts, OLAP Operations, Data Preprocessing Techniques, Association Rule Mining, Classification Algorithms, Clustering Algorithms |
| P23BTA202 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Regression and Classification Models, Model Evaluation Metrics, Ensemble Methods |
| P23BTA203 | Big Data Technologies | Core | 4 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark Framework, Data Streaming Technologies, NoSQL Databases for Big Data, Cloud Computing for Big Data |
| P23BTA204 | Cloud Computing | Core | 4 | Cloud Computing Models, Service and Deployment Models, Virtualization Technology, Cloud Security Challenges, Cloud Storage Solutions, Major Cloud Platforms (AWS, Azure, GCP) |
| P23BTA205P | Lab: Data Mining | Lab | 2 | Data Preprocessing using Tools, Association Rule Mining Implementation, Classification Algorithm Practice, Clustering Algorithm Practice, Data Mining Tools (e.g., Weka, R/Python libraries) |
| P23BTA206P | Lab: Machine Learning | Lab | 2 | Supervised Learning Algorithms Implementation, Unsupervised Learning Algorithms Implementation, Model Training and Evaluation, Scikit-learn Library Usage, Introduction to Deep Learning Frameworks |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| P23BTA301 | Advanced Machine Learning | Core | 4 | Deep Learning Fundamentals, Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models, Reinforcement Learning Introduction |
| P23BTA302 | Data Visualization and Storytelling | Core | 4 | Principles of Data Visualization, Visualization Tools (Tableau, Power BI), Interactive Dashboards, Storytelling with Data, Infographics Design, Data Presentation Techniques |
| P23BTA303 | Elective I | Elective | 4 | Choice from: Natural Language Processing, Optimization Techniques for Data Science, Ethical AI and Data Governance |
| P23BTA303A | Natural Language Processing | Elective (Option for P23BTA303) | 4 | Text Preprocessing, NLP Tasks and Applications, Word Embeddings, Text Classification, Sentiment Analysis, Language Models |
| P23BTA303B | Optimization Techniques for Data Science | Elective (Option for P23BTA303) | 4 | Linear and Non-linear Programming, Gradient Descent Algorithms, Convex Optimization, Metaheuristics, Optimization in Machine Learning Models |
| P23BTA303C | Ethical AI and Data Governance | Elective (Option for P23BTA303) | 4 | AI Ethics Principles, Fairness, Accountability, Transparency, Data Privacy and Regulations (GDPR), AI Regulations and Policies, Data Security and Compliance |
| P23BTA304P | Lab: Advanced Machine Learning | Lab | 2 | Deep Learning Frameworks (TensorFlow/PyTorch), CNNs and RNNs Implementation, Transfer Learning Applications, Model Deployment Basics |
| P23BTA305P | Lab: Big Data Analytics | Lab | 2 | Hadoop HDFS Commands, MapReduce Programming, Spark RDDs and DataFrames, Spark Streaming for Real-time Data, Big Data Tool Integration |
| P23BTA306 | Research Methodology and Technical Writing | Core | 2 | Research Design and Problem Formulation, Data Collection and Analysis Methods, Scientific Writing Principles, Referencing and Citation Styles, Thesis Structure and Presentation Skills |
| P23BTA307S | Seminar | Project/Seminar | 2 | Technical Presentation Skills, Literature Review and Survey, Public Speaking, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| P23BTA401 | Elective II | Elective | 4 | Choice from: Internet of Things (IoT) Analytics, Financial Analytics, Health Informatics and Analytics |
| P23BTA401A | Internet of Things (IoT) Analytics | Elective (Option for P23BTA401) | 4 | IoT Architecture and Components, Sensor Data Collection and Processing, Edge and Fog Computing, IoT Data Streams and Real-time Analytics, IoT Security and Privacy, IoT Applications |
| P23BTA401B | Financial Analytics | Elective (Option for P23BTA401) | 4 | Financial Markets and Instruments, Time Series Analysis in Finance, Risk Management and Modeling, Portfolio Optimization, Algorithmic Trading Strategies, Predictive Modeling in Finance |
| P23BTA401C | Health Informatics and Analytics | Elective (Option for P23BTA401) | 4 | Healthcare Data Management, Electronic Health Records (EHR), Medical Imaging Analysis, Predictive Analytics in Medicine, Public Health Analytics, Privacy and Security in Healthcare |
| P23BTA402 | Elective III | Elective | 4 | Choice from: Business Intelligence and Analytics, Quantum Computing and Data Science, Reinforcement Learning |
| P23BTA402A | Business Intelligence and Analytics | Elective (Option for P23BTA402) | 4 | Business Intelligence Concepts, Data Integration and ETL, Reporting Tools and Dashboards, Data-driven Decision Making, Performance Management, BI Tools (e.g., Qlik Sense, Power BI) |
| P23BTA402B | Quantum Computing and Data Science | Elective (Option for P23BTA402) | 4 | Quantum Mechanics Basics, Qubits and Quantum Gates, Quantum Algorithms (Shor, Grover), Quantum Machine Learning, Quantum Cryptography, Quantum Computing Platforms |
| P23BTA402C | Reinforcement Learning | Elective (Option for P23BTA402) | 4 | Markov Decision Processes (MDPs), Q-Learning and SARSA, Deep Reinforcement Learning, Policy Gradients, Reward Functions Design, Exploration-Exploitation Trade-off |
| P23BTA403P | Internship | Project/Internship | 2 | Industry Exposure, Practical Skill Application, Project Management, Problem Solving in Real-world Settings, Professional Communication, Report Writing |
| P23BTA404PW | Major Project / Dissertation | Project/Dissertation | 8 | Problem Definition and Literature Review, Methodology Design and Implementation, Data Collection and Analysis, Experimental Design and Evaluation, Report Writing and Documentation, Thesis Defense and Presentation |
