classX_DS_Student_Handbook (1)

Central Education Board Overview

• Educational Body: Central Board of Secondary Education (CBSE), India

• Education Standards: Implementing curriculums to ensure student readiness with relevant skills

• Data Science Curriculum: Introduction of Data Science as a skill subject in Grade X

Acknowledgments

• Patrons:

  • Sh. Ramesh Pokhriyal 'Nishank', Minister of Human Resource Development

  • Sh. Dhotre Sanjay Shamrao, Minister of State for Human Resource Development

  • Ms. Anita Karwal, IAS, Secretary, Department of School Education and Literacy

• Advisory Roles:

  • Mr. Manuj Ahuja, IAS, Chairperson, CBSE

• Key Contributors:

  • Dr. Biswajit Saha, Dr. Joseph Emmanuel, Sh. Navtez Bal, and others from Microsoft Corporation India Pvt. Ltd.

About the Handbook

• Purpose: Equip students with foundational skills in Data Science for industry readiness

• Curriculum Structure:

  • 12-Hour Duration: Offered in classes VIII to XII

  • Concepts covered include:

    • Data Collection

    • Data Analysis

    • Ethics in Data Science

    • Applications of Data Science

• Methods: Combination of theoretical concepts and practical examples to develop critical thinking

Handbook Contents

• Statistical Concepts:

  • Introduction to data collections like subsets, two-way frequency tables, measures of central tendency (mean, median, mode), standard deviation, distributions, ethical considerations.

• Modules Include:

  • Statistics Fundamentals

  • Distribution Analysis

  • Pattern Recognition

  • Data Merging Techniques

  • Z-Score Applications

• Ethics: Framework for ethical guidelines in data analysis and data governance.

Use of Statistics in Data Science

• Statistics Essentials:

  • Understanding subsets and frequency tables for data organization.

• Two-way Table: Representation of data for multi-variable analysis.

• Measures of Central Tendency:

  • Mean: Average value of a data set.

  • Median: Middle value when sorted.

• Divergence Analysis:

  • Mean Absolute Deviation (MAD) and Standard Deviation to determine variability.

Importance of Ethics in Data Science

• Privacy and Governance: Guidelines ensure protection of individual data privacy and ethical use of data.

• Data Discarding: Proper techniques for handling data post-use, including shredding and deletion strategies.

Recap

• Learning Outcomes:

  • Comprehension of basic data science concepts, statistical analysis, and ethical implications.

  • Development of critical thinking and practical skills relevant to the field of data science.

References

• Comprehensive curriculum references are listed from educational and statistical authorities.