Data Science 12
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Welcome!
Data Science for CBSE Class 12 is a comprehensive 100-mark subject encompassing Data Governance, Exploratory Data Analysis, Classification Algorithms, Regression Algorithms, and Unsupervised Learning, along with Employability Skills. Assessment comprises a 50-mark theory exam and a 50-mark practical exam to evaluate students’ knowledge and skills.
Prerequisites
Prerequisites for Data Science in CBSE Class 12 include a strong foundation in mathematics, particularly statistics and probability, basic programming knowledge (preferably in Python or R), and familiarity with data handling concepts. A logical mindset and analytical skills are also beneficial.
Learning Objectives
The learning objectives of Data Science for CBSE Class 12 include understanding data governance, conducting exploratory data analysis, applying classification and regression algorithms, exploring unsupervised learning techniques, and developing practical skills in data manipulation and visualization for real-world applications
Course Overview
- Data Governance 8 Marks
- Understanding data governance and its importance
- Data quality management and data stewardship
- Compliance with data regulations (e.g., GDPR, CCPA)
- Data privacy, security, and ethical considerations
- Frameworks and best practices for effective data governance
- Exploratory Data Analysis 7 Marks
- Introduction to exploratory data analysis (EDA)
- Techniques for data visualization (e.g., histograms, box plots, scatter plots)
- Identifying patterns, trends, and anomalies in data
- Using statistical summaries and descriptive statistics
- Tools and libraries for EDA (e.g., Pandas, Matplotlib)
- Classification Algorithms – I 6 Marks
- Introduction to classification algorithms
- Understanding the concept of supervised learning
- Key classification techniques (e.g., Decision Trees, K-Nearest Neighbors)
- Evaluating classification models (confusion matrix, accuracy, precision, recall)
- Hands-on implementation of classification algorithms
- Classification Algorithms – II 4 Marks
- Advanced classification techniques (e.g., Random Forests, Support Vector Machines)
- Hyperparameter tuning and model optimization
- Handling imbalanced datasets
- Feature selection and importance in classification tasks
- Regression Algorithms – I 4 Marks
- Introduction to regression analysis and its applications
- Simple linear regression concepts and calculations
- Evaluating regression models (R-squared, adjusted R-squared)
- Assumptions of linear regression and their importance
- Implementing simple linear regression in practice
- Regression Algorithms – II 4 Marks
- Multiple linear regression and its applications
- Polynomial regression and when to use it
- Evaluating and improving regression models
- Introduction to regularization techniques (Ridge and Lasso regression)
- Unsupervised Learning 8 marks
- Overview of unsupervised learning and its significance
- Key techniques: Clustering (e.g., K-Means, Hierarchical Clustering)
- Dimensionality reduction techniques (e.g., PCA)
- Applications of unsupervised learning in real-world scenarios
- Implementing clustering algorithms using Python libraries
8 EMPLOYABILITY SKILLS 10 Marks
- Communication Skills
- Self-management Skills
- Information and Communication Technology Skills
- Entrepreneurial Skills
- Green Skills
9 Practical 50 Marks
Practical File/ Student Portfolio 20 Marks
Practical Examination 20 Marks
Viva Voce 10 Marks
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Data Analyst using Python
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Placement assistance offered for a successful career.
Membership
Membership provided until the final examination.
Personalized Attention
Personalized attention provided to each student.
Get Course Certificate
Certificate awarded upon completion of the course.
Monthly Tests
Regular monthly test series for progress evaluation.
Latest CBSE Syllabus
Training modules aligned with the latest CBSE syllabus.
Frequently Asked Questions
Data Science involves analyzing and interpreting complex data to make informed decisions using statistical methods, algorithms, and programming.
Python and R are commonly used, and a basic understanding of them is essential.
Data Governance, Exploratory Data Analysis, Classification and Regression Algorithms, and Unsupervised Learning.
50-mark theory and 50-mark practical exam.
Yes, a good grasp of statistics and probability is required.
It builds foundational skills for careers in data science, AI, and analytics.
