.png&w=1920&q=75)
B-SC in Data Science Mathematics at CHRIST (Deemed to be University)
Bengaluru, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Data Science, Mathematics at CHRIST (Deemed to be University) Bengaluru?
This Data Science program at CHRIST, Bengaluru, focuses on equipping students with a robust blend of mathematical, statistical, and computational skills essential for navigating the complex world of data. India''''s rapidly expanding digital economy and tech sector demand skilled data professionals, making this program highly relevant. It differentiates itself through a strong theoretical foundation coupled with extensive practical exposure, preparing students for cutting-edge roles.
Who Should Apply?
This program is ideal for fresh graduates with a strong analytical aptitude and a background in science with Mathematics/Statistics/Computer Science at the 10+2 level, seeking entry into high-demand data-centric careers. It also suits individuals passionate about problem-solving through data, aiming to contribute to India''''s growing AI and analytics landscape. Students with a keen interest in both the theoretical underpinnings of data and its practical applications will thrive.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths such as Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, or Data Analyst in various sectors. Entry-level salaries typically range from INR 4-8 LPA, growing significantly with experience to 15-30+ LPA. The program aligns with industry needs, fostering skills for professional certifications in cloud platforms and AI, and enabling growth trajectories in leading Indian and multinational companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python)- (undefined)
Dedicate consistent time to practice Python programming through daily coding challenges on platforms like HackerRank or LeetCode. Focus on understanding data structures and algorithms, which are foundational for advanced data science. Regularly review lab exercises and seek clarification for complex concepts from faculty and peers.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks Python tutorials, Official Python Documentation
Career Connection
Strong programming skills are non-negotiable for data science roles, forming the backbone for data manipulation, analysis, and model building. Proficiency here directly impacts interview performance for entry-level data analyst and junior data scientist positions.
Build a Strong Mathematical & Statistical Base- (undefined)
Actively engage with Calculus, Discrete Mathematics, Probability, and Statistics courses. Solve extra problems from recommended textbooks and online resources. Form study groups to discuss concepts and prepare for competitive programming challenges that often require mathematical logic.
Tools & Resources
Khan Academy, MIT OpenCourseware (Mathematics), NPTEL lectures, NCERT Mathematics for foundation
Career Connection
A solid understanding of math and statistics is crucial for comprehending machine learning algorithms, interpreting models, and performing robust data analysis. This academic rigor is highly valued in quantitative data science roles and research-oriented positions.
Initiate Data Exploration and Visualization Projects- (undefined)
Apply newly learned data visualization skills to real-world datasets available online. Start with simple projects, focusing on clear storytelling and effective visual communication. Utilize libraries like Matplotlib and Seaborn to create insightful plots for diverse datasets.
Tools & Resources
Kaggle datasets, Google Public Datasets, Tableau Public, Power BI Desktop
Career Connection
The ability to communicate insights visually is a core skill for any data professional. Early project work builds a portfolio, showcases practical application, and prepares students for roles requiring reporting and dashboard creation in Indian businesses.
Intermediate Stage
Engage in Applied Machine Learning Projects- (undefined)
Actively participate in Kaggle competitions or conceptualize personal projects involving real-world datasets. Focus on implementing various supervised and unsupervised learning algorithms and understanding model evaluation metrics. Document every step, from data preprocessing to model deployment.
Tools & Resources
Kaggle, Scikit-learn documentation, TensorFlow/Keras tutorials, GitHub for project showcase
Career Connection
Hands-on ML experience is critical for securing roles like Machine Learning Engineer or Data Scientist. Project experience demonstrates problem-solving ability and practical skills, making candidates stand out in the competitive Indian job market.
Network and Seek Industry Mentorship- (undefined)
Attend data science meetups, webinars, and conferences organized in Bengaluru or online. Connect with alumni and industry professionals on LinkedIn. Seek opportunities for informal mentorship to understand industry trends, career paths, and gain insights into relevant skills for the Indian market.
Tools & Resources
LinkedIn, Meetup.com (for local tech events), Industry-specific webinars, Alumni association portal
Career Connection
Networking is invaluable for internships and placements. Mentors can provide guidance, open doors to opportunities, and help navigate career challenges, significantly boosting a student''''s employability and growth prospects in India''''s tech hubs.
Develop Specialization through Electives and Certifications- (undefined)
Carefully choose Discipline Specific Electives (DSEs) that align with career interests (e.g., NLP, Computer Vision). Supplement coursework with online certifications from platforms like Coursera, edX, or NPTEL in areas like cloud computing (AWS/Azure Data Science) or advanced ML techniques. This adds depth to your profile.
Tools & Resources
Coursera, edX, NPTEL, AWS/Azure/GCP certifications, DataCamp
Career Connection
Specialized skills are highly sought after in the Indian industry. Certifications validate expertise beyond academic degrees, improving chances for targeted roles and higher compensation in specific data science domains.
Advanced Stage
Undertake a Comprehensive Capstone Project/Internship- (undefined)
Engage in a significant Data Science Project or a demanding industry internship in the final year. Aim to solve a real-world business problem, apply advanced ML/DL techniques, and demonstrate the full data science lifecycle. Focus on documenting the project thoroughly and presenting results effectively.
Tools & Resources
Jupyter Notebooks, Google Colab, Cloud platforms (AWS/Azure/GCP), Version control (Git/GitHub)
Career Connection
This is often the most critical component for placements. A well-executed project or impactful internship provides tangible proof of skills, experience in an industry setting, and a strong talking point in interviews, especially for top-tier companies in India.
Focus on Placement Preparation and Soft Skills- (undefined)
Actively participate in placement training sessions, mock interviews, and group discussions. Refine your resume and LinkedIn profile to highlight projects, skills, and certifications. Practice explaining complex technical concepts clearly and concisely, focusing on problem-solving approaches.
Tools & Resources
Career Services cell at CHRIST, Online interview platforms (Pramp, InterviewBit), Public speaking groups
Career Connection
Strong communication and soft skills, alongside technical prowess, are vital for converting interviews into job offers. Effective self-presentation and problem articulation are key differentiators in the Indian corporate landscape.
Explore Data Ethics and Responsible AI- (undefined)
Beyond coursework, delve into contemporary discussions and research on data privacy, algorithmic bias, and ethical AI development. Participate in debates or write short articles on these topics. Understand the regulatory landscape in India concerning data (e.g., DPDP Bill).
Tools & Resources
AI Ethics publications, Tech policy think tanks, Journal articles on data privacy, Legal news portals
Career Connection
An awareness of data ethics is increasingly important for responsible innovation and compliance. This demonstrates a mature understanding of the broader implications of data science, a highly valued trait for leadership roles and ethical technology companies in India.
Program Structure and Curriculum
Eligibility:
- A pass in the 10+2 examination or equivalent with a minimum of 50% aggregate marks in the Science stream with Mathematics/Statistics/Computer Science/Information Practice.
Duration: 3 years / 6 semesters
Credits: 156 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC131 | Foundation of Data Science | Core | 4 | Introduction to Data Science, Data Analytics Process, Data Collection and Cleaning, Exploratory Data Analysis, Data Science Applications |
| DSC132 | Programming in Python | Core | 4 | Python Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming, File Handling |
| DSC133 | Calculus | Core | 4 | Limits and Continuity, Differentiation, Applications of Derivatives, Integration, Multivariable Calculus |
| DSC134 | Discrete Mathematics | Core | 4 | Set Theory, Logic and Proofs, Combinatorics, Graph Theory, Recurrence Relations |
| DSC135 | Data Visualization | Core | 3 | Principles of Data Visualization, Types of Visualizations, Tools for Visualization, Interactive Dashboards, Storytelling with Data |
| DSC136 | Python Lab | Lab | 2 | Basic Python Programming, Data Manipulation with Pandas, Numerical Operations with NumPy, Function Implementation, Debugging and Testing |
| DSC137 | Data Visualization Lab | Lab | 2 | Plotting with Matplotlib, Statistical Graphics with Seaborn, Interactive Visualizations with Plotly, Dashboard Creation, Data Storytelling |
| ENG111 | English I | Core | 2 | Communication Skills, Reading Comprehension, Grammar and Vocabulary, Writing Skills, Public Speaking |
| AEC111 | Environmental Studies | Core | 1 | Natural Resources, Ecosystems, Environmental Pollution, Social Issues and Environment, Environmental Protection |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC231 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| DSC232 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Regression Analysis |
| DSC233 | Linear Algebra | Core | 4 | Matrices and Vectors, Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Applications in Data Science |
| DSC234 | Database Management Systems | Core | 3 | Database Concepts, Relational Model, SQL Queries, Database Design, NoSQL Databases |
| DSC235 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| DSC236 | Database Management Systems Lab | Lab | 2 | SQL Querying Practice, Database Schema Creation, Data Manipulation Language, Data Definition Language, Database Connectivity |
| ENG211 | English II | Core | 2 | Advanced Communication, Professional Writing, Presentation Skills, Group Discussion, Interview Skills |
| AEC211 | Indian Constitution | Core | 1 | Constitutional Framework, Fundamental Rights and Duties, Directive Principles, Union and State Government, Judiciary |
| SEC211 | Academic Research and Writing | Elective | 2 | Research Methodology, Literature Review, Academic Paper Structure, Referencing Styles, Ethical Research Practices |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC331 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Model Evaluation, Feature Engineering, Ensemble Methods |
| DSC332 | Numerical Methods | Core | 4 | Solution of Algebraic Equations, Interpolation, Numerical Differentiation, Numerical Integration, Solution of Differential Equations |
| DSC333 | Optimization Techniques | Core | 4 | Linear Programming, Simplex Method, Non-Linear Programming, Constrained Optimization, Metaheuristics |
| DSC334 | Operating Systems | Core | 3 | OS Concepts, Process Management, Memory Management, File Systems, Deadlocks |
| DSC335 | Machine Learning Lab | Lab | 2 | Regression Algorithms, Classification Algorithms, Clustering Algorithms, Dimensionality Reduction, Model Hyperparameter Tuning |
| DSC336 | Numerical Methods Lab | Lab | 2 | Root Finding Algorithms, Interpolation Techniques, Numerical Integration Methods, Solving ODEs numerically, Error Analysis |
| SEC311 | Web Programming | Elective | 2 | HTML and CSS, JavaScript Fundamentals, Front-end Frameworks, Backend Development Basics, API Integration |
| VAC311 | Value Added Course (Open to all programmes) | Elective | 2 | Skill Development, Personal Enrichment, Interdisciplinary Knowledge, Current Trends, Industry Relevance |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC431 | Deep Learning | Core | 4 | Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Deep Learning Frameworks |
| DSC432 | Statistical Inference | Core | 4 | Point Estimation, Interval Estimation, Parametric Tests, Non-Parametric Tests, Likelihood Theory |
| DSC433 | Time Series Analysis | Core | 4 | Time Series Components, ARIMA Models, Forecasting Techniques, Seasonality and Trend, State Space Models |
| DSC434 | Cloud Computing for Data Science | Core | 3 | Cloud Fundamentals, Cloud Service Models, Big Data on Cloud, Cloud Storage Solutions, Cloud Security |
| DSC435 | Deep Learning Lab | Lab | 2 | TensorFlow/Keras Implementation, Image Classification with CNNs, Sequence Modeling with RNNs, Model Training and Optimization, Deployment of Deep Learning Models |
| DSC436 | Time Series Analysis Lab | Lab | 2 | Time Series Data Preprocessing, ARIMA Model Building, Forecasting with Python/R, Seasonality Decomposition, Model Validation |
| SEC411 | R Programming | Elective | 2 | R Syntax and Data Structures, Data Import and Export, Data Manipulation with Dplyr, Statistical Modeling in R, Data Visualization with Ggplot2 |
| VAC411 | Value Added Course (Open to all programmes) | Elective | 2 | Cross-disciplinary Skills, Ethical Considerations, Entrepreneurial Mindset, Industry Best Practices, Communication Enhancement |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC531 | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, Distributed Computing, NoSQL Databases |
| DSC532 | Applied Econometrics | Core | 4 | Econometric Models, Regression Analysis, Time Series Econometrics, Panel Data Analysis, Forecasting in Economics |
| DSE531 | Discipline Specific Elective - I (e.g., Natural Language Processing, Computer Vision, Business Analytics, Financial Data Science, Actuarial Science) | Elective | 4 | Specialized Area Fundamentals, Advanced Algorithms, Industry Specific Tools, Problem Solving in Domain, Case Studies |
| DSE532 | Discipline Specific Elective - II (e.g., Natural Language Processing, Computer Vision, Business Analytics, Financial Data Science, Actuarial Science) | Elective | 4 | Advanced Concepts, Research Methodologies, Project Implementation, Domain-specific Applications, Emerging Trends |
| DSC533 | Big Data Analytics Lab | Lab | 2 | HDFS Operations, MapReduce Programming, Spark Data Processing, Hive and Pig Scripting, NoSQL Database Operations |
| DSC534 | DSE Lab | Lab | 2 | Practical Application of DSE, Tool Usage for Elective, Project-based Learning, Data Analysis in Domain, Model Implementation |
| GE511 | Generic Elective - I (From School of Sciences/Humanities/Social Sciences) | Elective | 3 | Interdisciplinary Studies, General Knowledge, Critical Thinking, Cross-Cultural Awareness, Holistic Development |
| GE512 | Generic Elective - II (From School of Sciences/Humanities/Social Sciences) | Elective | 3 | Elective Subject Fundamentals, Contextual Applications, Problem-solving, Analytical Skills, Creative Expression |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC631 | Data Science Project | Project | 6 | Project Planning, Data Collection and Analysis, Model Development, Results Interpretation, Technical Report Writing |
| DSC632 | Data Ethics and Governance | Core | 4 | Ethical Principles in Data Science, Data Privacy and Security, Regulatory Compliance, Bias in AI, Responsible AI Development |
| DSE631 | Discipline Specific Elective - III | Elective | 4 | Advanced Topics in Elective, Research and Development, Case Studies and Solutions, Emerging Technologies, Domain-specific Challenges |
| DSE632 | Discipline Specific Elective - IV | Elective | 4 | Specialized Tools and Techniques, Advanced Problem Solving, Industry Applications, Innovation and Trends, Capstone Project Preparation |
| DSC633 | DSE Lab | Lab | 2 | Advanced DSE Implementations, Experimentation and Analysis, Tool Proficiency, Problem-solving scenarios, Result Visualization |
| VAC611 | Value Added Course (Open to all programmes) | Elective | 2 | Professional Development, Current Affairs, Social Responsibility, Skill Enhancement, Holistic Growth |
| INT611 | Internship | Project | 4 | Industry Exposure, Practical Skill Application, Professional Networking, Report Writing, Real-world Problem Solving |
