+3 UG 5th Semester Data Science & Data Management Syllabus PDF, Notes & MCQs

 

+3 UG 5th Semester SEC – Fundamentals of Data Science & Data Management: Syllabus, Notes & MCQs

The +3 UG 5th Semester SEC paper, Fundamentals of Data Science & Data Management, introduces commerce students to the growing importance of data and technology in the business world in modern era .

In today's digital age, businesses depend on information to manage accounts, understand customers, maintain financial records and make better decisions. Keeping these changes in mind, data science and data management have become useful areas Whether we make an online payment, order food, use Google Maps or shop online, we generate data through these customer action & other  activities.

 This is why Fundamentals of Data Science & Data Management is an important subject for +3 UG 5th Semester Commerce graduates student.

Why Is Data Science Added to the Odisha Commerce Syllabus?

Traditionally, commerce education has focused on accounting, finance, economics and business management. However, modern businesses increasingly use digital tools and data to perform   from different sources from their daily activities. like YouTube, Facebook ,Instagram and Snapchart.

This subject helps Odisha +3 Commerce students understand how information is collected, stored, organised and analysed for business purposes.

It also develops basic analytical thinking and introduces students to the role of technology in banking, accounting, marketing and business management.

What Will Students Learn?

Through this subject, students can develop a basic understanding of:

• Meaning and fundamentals of Data Science.
• Types and sources of data.
• Data collection and processing.
• Introduction to Data Management.
• Database concepts and data organisation.
• Data security and privacy.
• Applications of data science in business.

What Is Data Science?

Data Science is the process of collecting, studying and analysing information to find useful results. It helps people and businesses make better decisions.

For example, when we use Google Maps, it analyses traffic information to suggest a faster route. Similarly, shopping applications suggest products based on our previous searches and purchases. These are common examples of data being used in everyday life.

Importance of Data Science & Data Management in Commerce

For commerce students, this subject connects business knowledge with modern technology. Some practical examples include:

• Banking: Banks use transaction data to maintain customer accounts and identify unusual activities.
• Shopping: Online shopping platforms study customer preferences to recommend products.
• Accounting: Businesses organise financial records to calculate income, expenses and profit.
• Marketing: Companies analyse customer interests to plan advertisements.
• Education: Colleges maintain attendance, marks and student registration details.

Importance for Commerce Students

Learning this subject can help students understand how businesses use information for planning and decision-making. It also provides basic digital knowledge that may be useful in banking, accounting, finance, marketing and business administration.

The objective is not necessarily to make every commerce student a computer expert, but to introduce them to the practical importance of data in modern business.

SEC-2 (COMMERECE ): Fundamentals of Data Science & Data Management – Short Syllabus

The Fundamentals of Data Science & Data Management is a Skill Enhancement Course (SEC-2) designed to introduce students to data collection, analysis, visualisation, machine learning and data management. The subject helps commerce students understand how data and technology are used in modern business activitie

Unit 1: Foundations of Data Science

Important topics for examination:

1. Meaning, definition and scope of Data Science.
2. History and evolution of Data Science.
3. Applications of Data Science in daily life and business.
4. Types of Data: Structured, Semi-structured and Unstructured.
5. Sources and methods of data collection.
6. Data quality and data pre-processing.
7. Introduction to Python programming.
8. Basics of Pandas and NumPy.
9. Introduction to Statistics for Data Science.

Unit 2: Data Analysis and Visualization

Important topics for examination:

1. Meaning and importance of Exploratory Data Analysis (EDA).
2. Data visualization techniques.
3. Introduction to Matplotlib and Seaborn.
4. Summary statistics and data interpretation.
5. Identifying patterns and relationships in data.
6. Data cleaning and data wrangling.
7. Handling missing values and outliers.
8. Data transformation and feature engineering.
9. Introduction to Tableau and Power BI.
10. Data storytelling and communication of results.

Unit 3: Machine Learning Fundamentals

Important topics for examination:

1. Meaning and introduction to Machine Learning.
2. Types of Machine Learning.
3. Supervised and Unsupervised Learning.
4. Regression and Classification.
5. Model evaluation and selection.
6. Introduction to Deep Learning.
7. Basics of Neural Networks.
8. CNN and RNN.
9. Applications of Machine Learning in business and commerce.

Unit 4: Advanced Topics in Data Science – Big Data, Data Ethics & Privacy

Unit 4 of the +3 UG 5th Semester SEC-2 paper, Fundamentals of Data Science & Data Management, introduces students to advanced concepts such as Big Data, distributed computing, data security and privacy.

These topics explain how modern organisations manage large amounts of information and protect it from misuse.

1. Big Data

Big Data refers to extremely large and complex datasets that traditional data-processing methods may find difficult to handle.

Example: Online shopping websites generate huge amounts of information through customer searches, orders, payments and product reviews.

Important Characteristics of Big Data (5 Vs)

1. Volume: Large quantity of data.
2. Velocity: Speed at which data is generated.
3. Variety: Different forms of data.
4. Veracity: Accuracy and reliability of data.
5. Value: Useful information obtained from data.

2. Big Data Technologies

Some important technologies used to manage and process Big Data are:

• Hadoop: A framework used for storing and processing large datasets across multiple computers.
• Apache Spark: A technology used for fast and large-scale data processing.

3. Batch Processing and Real-Time Processing

• Batch Processing: Data is collected and processed in groups at a particular time. Example: Preparing a monthly bank transaction report.
• Real-Time Processing: Data is processed immediately as it is generated. Example: Receiving an instant notification after making a digital payment.

4. Scalable Data Storage and Processing

Scalable data storage means increasing storage capacity according to the growing volume of data.

Example: An e-commerce company increases its cloud storage capacity as its customer and transaction records grow.

5. Data Ethics and Privacy

Data ethics refers to the responsible and fair use of information.

Important points include:

• Protecting personal information.
• Taking permission before collecting sensitive data.
• Avoiding misuse of customer information.
• Maintaining data accuracy.
• Preventing unauthorised access.

6. GDPR and Data Privacy Regulations

GDPR stands for General Data Protection Regulation. It is a European Union data protection law that provides rules for handling personal information and protecting individuals' privacy.

It is important for students to understand how organisations should collect, store and use personal data responsibly.

7. Data Security and Compliance

Data security means protecting information from theft, loss, damage and unauthorised access.

Common methods include:

• Password protection.
• Data encryption.
• Regular data backups.
• Access control.
• Security updates.

Important Questions for Examination

1. What is Big Data? Explain its characteristics.
2. What are the 5 Vs of Big Data?
3. What is Hadoop?
4. Explain Apache Spark.
5. Differentiate between Batch Processing and Real-Time Processing.
6. What is scalable data storage?
7. What is Data Ethics?
8. What is GDPR?
9. Explain the importance of Data Privacy.
10. What are the common methods of Data Security? 

📥 Download SEC-2 UNIT4 BIG DATA NOTES PDF

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Frequently Asked Questions (FAQs)

1. What is Data Science in simple words?

Data Science is the process of collecting, analysing and understanding data to find useful information and make better decisions.

2. What is Data Management with an example?

Data Management means collecting, storing, organising and protecting information. For example, maintaining students' attendance, marks and registration records in a college database.

3. Why is Data Science important for commerce students?

It helps commerce students understand how businesses use data for accounting, banking, marketing, financial planning and business decisions.

4. What are the daily life applications of Data Science?

Data Science is used in Google Maps, online shopping recommendations, digital banking, social media and online payment systems.

5. What is the difference between Data Science and Data Management?

Data Science focuses on analysing information to discover useful insights, whereas Data Management focuses on properly storing, organising and maintaining information.

Blogger tip: Add these five FAQs after your article's conclusion. They are concise, relevant and easy for +3 Commerce students to understand.

Conclusion

Fundamentals of Data Science & Data Management is a useful addition to commerce education because it connects traditional business knowledge with modern technology for know the basic idea regarding marketing, campaign and start-up. For Odisha +3 Commerce students, understanding this subject can provide a foundation for developing practical skills that are increasingly relevant in today's professional environment. for more information join edukosal Data Science & Data Management .soon +3 UG 5th Semester examinations can use subject-wise notes, important questions and MCQs to improve their understanding and revision.