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MCA in Data Science at Adarsh Institute of Management and Information Technology
Bengaluru, Karnataka
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About the Specialization
What is Data Science at Adarsh Institute of Management and Information Technology Bengaluru?
This Data Science program at ADARSH INSTITUTE OF MANAGEMENT AND INFORMATION TECHNOLOGY focuses on equipping students with advanced analytical and computational skills to extract insights from complex datasets. With India''''s burgeoning digital economy, the program emphasizes practical application of machine learning, big data technologies, and statistical modeling, preparing graduates for high-demand roles in data-driven industries. It integrates core theoretical knowledge with hands-on project experience, aligning with current industry needs, fostering a deep understanding of data-driven decision-making.
Who Should Apply?
This program is ideal for engineering or science graduates with a strong mathematical aptitude, particularly those with a BCA, B.Sc. (Computer Science/Maths/Stats), or B.Tech/BE background, seeking entry into the analytics domain. It also caters to early-career IT professionals looking to transition into specialized data science roles or upskill with modern techniques. Aspirants should possess a foundational understanding of programming and statistics, keen to leverage data for strategic decision-making in various sectors across India.
Why Choose This Course?
Graduates of this program can expect robust career paths as Data Scientists, Machine Learning Engineers, Data Analysts, Business Intelligence Developers, or Big Data Specialists in India. Entry-level salaries typically range from INR 4-8 lakhs per annum, escalating significantly with experience to INR 15-30 lakhs or more for senior roles. The program fosters critical thinking and problem-solving, opening avenues in Indian startups, large IT service companies, and global corporations with significant operations in India, facilitating continuous professional growth and impact.
Student Success Practices
Foundation Stage
Master Core Programming and Math- (Semester 1-2)
Dedicate significant time to mastering Python programming fundamentals and mathematical concepts (Linear Algebra, Calculus, Probability, Statistics). These are the bedrock of Data Science. Participate in coding challenges regularly to improve problem-solving skills.
Tools & Resources
HackerRank, LeetCode, Khan Academy, NPTEL online courses for Mathematics
Career Connection
A strong foundation ensures efficient algorithm implementation, deeper understanding of ML models, and successful navigation of technical interviews for data science roles.
Build a Strong Database Understanding- (Semester 1-2)
Beyond theoretical knowledge, practice extensively with SQL and NoSQL databases. Develop projects involving data storage, retrieval, and manipulation to understand real-world data management challenges and solutions.
Tools & Resources
MySQL Workbench, PostgreSQL, MongoDB Atlas, SQLZoo
Career Connection
Proficiency in databases is crucial for data engineers and analysts, enabling efficient data extraction and preparation, a primary skill sought by Indian companies.
Engage in Peer Learning and Study Groups- (Semester 1-2)
Form study groups with classmates to discuss complex topics, share insights, and collaboratively solve problems. Teaching concepts to others solidifies your understanding and fosters a supportive learning environment.
Tools & Resources
College library study rooms, WhatsApp groups, Microsoft Teams/Google Meet for virtual collaboration
Career Connection
Develops teamwork and communication skills, vital for collaborating in professional data science teams and project-based roles in Indian IT firms.
Intermediate Stage
Undertake Practical Machine Learning Projects- (Semester 3-4)
Apply theoretical knowledge of machine learning to real-world datasets. Work on mini-projects, participate in Kaggle competitions, and build a portfolio of diverse ML applications, focusing on data preprocessing, model selection, and evaluation.
Tools & Resources
Kaggle, Google Colab, Scikit-learn, TensorFlow/PyTorch (basics)
Career Connection
A strong project portfolio is critical for demonstrating practical skills to potential employers in India, showing ability to deliver end-to-end data science solutions.
Explore Big Data Ecosystems- (Semester 3-4)
Gain hands-on experience with big data technologies like Hadoop, Spark, and cloud platforms. Understand how to process, store, and analyze large volumes of data, which is increasingly common in Indian enterprises.
Tools & Resources
Apache Hadoop, Apache Spark, AWS EC2/S3 (free tier), Azure Data Lake
Career Connection
Positions in Big Data Engineering and Data Analytics are prevalent in India, requiring proficiency in these distributed computing frameworks for handling large-scale datasets.
Network with Industry Professionals- (Semester 3-4)
Attend industry workshops, seminars, and guest lectures organized by the college. Connect with alumni and professionals on platforms like LinkedIn to understand career trends, seek mentorship, and identify internship opportunities.
Tools & Resources
LinkedIn, College alumni network, Industry meetups in Bengaluru
Career Connection
Building a professional network in India can lead to valuable internship and job referrals, providing insights into the current hiring landscape and industry expectations.
Advanced Stage
Specialize and Deepen Knowledge- (Semester 4)
Choose a specific area within Data Science (e.g., Deep Learning, NLP, Computer Vision) and delve deeper through advanced courses, online certifications, and a significant final year project aligned with that specialization. Aim for impactful research or industry-grade solutions.
Tools & Resources
Coursera/edX advanced courses, NVIDIA Deep Learning Institute, Keras/PyTorch for specific tasks
Career Connection
Specialized knowledge makes you a more attractive candidate for niche roles in high-tech R&D centers, AI labs, and specialized product companies in India.
Focus on Communication and Storytelling with Data- (Semester 4)
Develop strong data visualization and communication skills. Practice presenting complex insights clearly and concisely through dashboards, reports, and presentations. This skill is critical for bridging the gap between technical teams and business stakeholders.
Tools & Resources
Tableau Public, Power BI Desktop, Microsoft PowerPoint/Google Slides, storytellingwithdata.com resources
Career Connection
Effective communication is paramount for data scientists to influence business decisions, leading to roles in data strategy and consulting within Indian and multinational companies.
Prepare Rigorously for Placements- (Semester 4)
Begin placement preparation early by practicing aptitude tests, technical interview questions (DSA, ML concepts, SQL), and mock interviews. Tailor your resume and cover letter to specific job descriptions and leverage the college''''s placement cell extensively.
Tools & Resources
GeeksforGeeks, Interviews/Coding Rounds on platforms like Pramp, College Placement Cell resources
Career Connection
Maximizes chances of securing desirable placements in top-tier companies in India, ensuring a smooth transition from academics to professional life with a competitive edge.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree (BCA/B.Sc/B.Com/BA with Mathematics at 10+2 or degree level) with minimum 50% aggregate (45% for reserved categories) from a recognized university. Valid entrance exam score (e.g., KMAT, PGCET, or similar).
Duration: 2 years / 4 semesters
Credits: 88 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MCA101 | Mathematical Foundations for Computer Applications | Core | 4 | Discrete Mathematics, Linear Algebra, Probability and Statistics, Graph Theory, Numerical Methods |
| 22MCA102 | Programming with Python | Core | 4 | Python Fundamentals, Data Structures in Python, Object-Oriented Programming, File Handling, Modules and Packages |
| 22MCA103 | Database Management Systems | Core | 4 | ER Modeling, Relational Model, SQL Queries, Normalization, Transaction Management |
| 22MCA104 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 22MCAL105 | Python Programming Lab | Lab | 2 | Python Scripting, Data Structure Implementation, OOP Concepts in Python, Database Connectivity, Basic Algorithm Implementation |
| 22MCAL106 | DBMS Lab | Lab | 2 | DDL and DML Commands, SQL Functions, Joins and Subqueries, View Creation, Procedure and Trigger Implementation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MCA201 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, Deadlocks, Concurrency Control |
| 22MCA202 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Network Security, Wireless Networks |
| 22MCA203 | Object-Oriented Analysis and Design using Java | Core | 4 | OOP Concepts, UML Diagrams, Design Patterns, Exception Handling, Multithreading |
| 22MCA204DS | Fundamentals of Data Science | Core-Specialization | 4 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Introduction to Machine Learning, Ethical Considerations |
| 22MCAL205 | Java Programming Lab | Lab | 2 | Java Programs, GUI Programming, Database Connectivity (JDBC), Web Applications, Object-Oriented Design Implementation |
| 22MCAL206DS | Data Science Lab | Lab-Specialization | 2 | Numpy and Pandas, Matplotlib and Seaborn, Data Preprocessing, Supervised Learning Basics, Unsupervised Learning Basics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MCA301DS | Machine Learning | Core-Specialization | 4 | Regression Models, Classification Algorithms, Clustering Techniques, Model Evaluation, Ensemble Methods |
| 22MCA302DS | Big Data Technologies | Core-Specialization | 4 | Hadoop Ecosystem, MapReduce, Spark Framework, NoSQL Databases, Stream Processing |
| 22MCA303 | Cloud Computing | Elective | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, Cloud Platforms (AWS/Azure/GCP) |
| 22MCA304DS | Data Visualization and Communication | Elective (Data Science) | 3 | Principles of Data Visualization, Tools (Tableau, Power BI), Interactive Dashboards, Storytelling with Data, Advanced Chart Types |
| 22MCAL305DS | Machine Learning Lab | Lab-Specialization | 2 | Scikit-learn Implementation, Model Training and Testing, Hyperparameter Tuning, Cross-Validation, Algorithm Comparison |
| 22MCAMP306 | Mini Project | Project | 3 | Problem Identification, Literature Survey, Design and Implementation, Testing and Evaluation, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MCA401DS | Deep Learning | Core-Specialization | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 22MCA402DS | Natural Language Processing | Elective (Data Science) | 3 | Text Preprocessing, Language Models, Named Entity Recognition, Sentiment Analysis, Machine Translation |
| 22MCA403 | Research Methodology and IPR | Core | 3 | Research Design, Data Collection Methods, Statistical Analysis, Report Writing, Intellectual Property Rights |
| 22MCAPR404 | Major Project / Dissertation | Project | 12 | Project Proposal, System Design, Implementation and Testing, Data Analysis, Technical Report and Presentation |
