B.Sc – Data Science
Overview
Data Science is one of the most alluring Programme that draws students across the Globe. MCC is a forerunner to offer a four-year Bachelor’s degree in Data Science which is an amalgamation of Mathematics, Statistics and Specialized Programming skills. The data science Programme is designed to incorporate in- depth understanding of the key technologies in data science and business analytics such as Data Mining, Data Retrieval, Machine Learning, Visualization techniques, Predictive Modeling and Statistics. Students will gain hands-on exposure to Data and Analysis across domains that include Finance, Retail and CPG, using an active learning model comprising live sessions from Industry professionals, practical labs, and projects mentored by corporates.
Program Matrix
Program Outcomes
- PO1: Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.
- PO2: Apply mathematical and statistical principles to the analysis of data and analyse very large data sets in the context of real world problems.
- PO3: Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.
Matrix
| Sem | Course Code | Paper Title | Hrs/week | Marks | Cerdits | Semester (Total Credits) | ||
|---|---|---|---|---|---|---|---|---|
| CIA | ESE | Total | Subjects | |||||
| I | Language L1 | 3 | 20 | 80 | 100 | 3 | 23 | |
| Language L2 | 3 | 20 | 80 | 100 | 3 | |||
| Python Programming | 4 | 20 | 80 | 100 | 3 | |||
| Descriptive Statistics | 4 | 20 | 80 | 100 | 3 | |||
| Mathematical Foundation for Data Science-I | 4 | 20 | 80 | 100 | 3 | |||
| Python Programming Lab | 3 | 10 | 40 | 50 | 2 | |||
| Descriptive Statistics EXCEL Lab | 3 | 10 | 40 | 50 | 2 | |||
| Descriptive Statistics EXCEL Lab | 3 | 10 | 40 | 50 | 2 | |||
| Mathematical Foundation for Data Science-I Lab | 3 | 10 | 40 | 50 | 2 | |||
| Compulsory – 1 | Constitutional Values – I | 2 | 10 | 40 | 50 | 2 | ||
| Total | 700 | |||||||
| II | Language L1 | 3 | 20 | 80 | 100 | 3 | 23 | |
| Language L2 | 3 | 20 | 80 | 100 | 3 | |||
| Mathematical Foundations for Data Science-II | 4 | 20 | 80 | 100 | 3 | |||
| Data Structures | 4 | 20 | 80 | 100 | 3 | |||
| Probability and Probability Distributions | 4 | 20 | 80 | 100 | 3 | |||
| Data Structures Lab | 3 | 10 | 40 | 50 | 2 | |||
| Probability and Probability Distributions using R and EXCEL Lab | 3 | 10 | 40 | 50 | 2 | |||
| Distributions using R and EXCEL Lab | 3 | 10 | 40 | 50 | 2 | |||
| Mathematical Foundation for Data Science-II Lab | 3 | 10 | 40 | 50 | 2 | |||
| Compulsory – 2 | Constitutional Values – II | 2 | 10 | 40 | 50 | 2 | ||
| Compulsory -3 | Environmental Studies | 2 | 10 | 40 | 50 | 2 | ||
| III | Language L1 | 3 | 20 | 80 | 100 | 3 | 23 | |
| Language L2 | 3 | 20 | 80 | 100 | 3 | |||
| Database Management Systems | 4 | 20 | 80 | 100 | 3 | |||
| Object Oriented Programming Using Java | 4 | 20 | 80 | 100 | 3 | |||
| Statistical Methods for Decision Making | 4 | 20 | 80 | 100 | 3 | |||
| Java Programming Lab | 3 | 10 | 40 | 50 | 2 | |||
| Statistical Methods for Decision Making Lab | 3 | 10 | 40 | 50 | 2 | |||
| Elective I | Digital Analytics Categorical Data Analysis | 2 | 10 | 40 | 50 | 2 | ||
| IV | Language L1 | 3 | 20 | 80 | 100 | 3 | 25 | |
| Language L2 | 3 | 20 | 80 | 100 | 3 | |||
| Data Warehousing and Data Mining | 4 | 20 | 80 | 100 | 3 | |||
| Predictive Analytics -I | 4 | 20 | 80 | 100 | 3 | |||
| Optimization Techniques | 4 | 20 | 80 | 100 | 5 | |||
| Data Warehousing and Data Mining Lab | 3 | 10 | 40 | 50 | 2 | |||
| Predictive Analytics -I Lab | 3 | 10 | 40 | 50 | 2 | |||
| Elective II | Data Security and Privacy Block Chain Technology | 2 | 10 | 40 | 50 | 2 | ||
| SEC | Exploratory Data Analysis Using Excel | 2 | 10 | 40 | 50 | 2 | ||
B.Sc Data Science Course Matrix(NEP for the Batch 2023-24)
| Sem | Course Code | Paper Title | Hrs/week | Marks | Cerdits | Semester (Total Credits) | ||
|---|---|---|---|---|---|---|---|---|
| CIA | ESE | Total | Subjects | |||||
| V | DSC | MAchine Learning | 4 | 40 | 60 | 100 | 4 | 24 |
| DSC | Big Data Analytics-II | 4 | 40 | 60 | 100 | 4 | ||
| DSC | Predictive Analytics – II | 4 | 40 | 60 | 100 | 4 | ||
| DSE | Multivariate Data Analysis with Statistical Software | 3 | 40 | 60 | 100 | 3 | ||
| Machine Learning Lab | 4 | 25 | 25 | 50 | 2 | |||
| Predictive Analytics – II Lab | 4 | 25 | 25 | 50 | 2 | |||
| Vocational Course- | Advanced Excel Techniques | 3 | 40 | 60 | 100 | 3 | ||
| SEC – IV | Data Security and Privacy | 2 | – | 50 | 50 | 2 | ||
| SEC | Exploratory Data Analysis Using Excel | 2 | 10 | 40 | 50 | 2 | ||
| VI | DSC | Cloud Computing for Data Analytics | 4 | 40 | 60 | 100 | 4 | 24 |
| DSC | Data Visualization | 4 | 40 | 60 | 100 | 4 | ||
| DSC | Exploratory Data Analysis | 4 | 40 | 60 | 100 | 4 | ||
| DSE – II | Block Chain Technology Internet of Things | 3 | 40 | 60 | 100 | 3 | ||
| Cloud Computing Lab | 4 | 25 | 25 | 50 | 2 | |||
| Data Analytics and Visualization Lab | 4 | 25 | 25 | 50 | 2 | |||
| Vocational Course- | Augmented Reality & Virtual Reality | 3 | 40 | 60 | 100 | 3 | ||
| SEC – V | Project | 2 | – | 50 | 50 | 2 | ||
Career Prospects
- Data Scientist
- Data Analyst
- Data Engineer
- Data Architect
- Business Intelligence Analyst
- Statistician
- Machine Learning Engineer