In-depth Notes on Business Analytics Course Syllabus

Acknowledgements

This handbook on Business Analytics is designed to be helpful for both informed readers and aspiring data analysts. Acknowledgements are made to M/s. MKM Publishers Pvt. Ltd for their support and cooperation.

Course Information

Course Title & Code

  • Business Analytics (DSC-6.1)

Credit Distribution

  • Credits: 2 (Lecture), 0 (Tutorial), 2 (Practical/Practice)

Eligibility Criteria

  • B.Com(H): Pass in Class XII with Mathematics/Accountancy

  • B.Com: Pass in Class XII

  • B.Com(H): Studied Business Statistics (DSC-4.1)

  • B.Com: Studied Business Statistics (DSC-3.1)

Learning Objectives

  • Introduce business intelligence and analytics processes.

  • Analyze data using statistical tools for predictive models and business decision making.

Learning Outcomes

  1. Proficient use of spreadsheets for data computation and analysis.

  2. Capability to analyze data with pivot tables and charts.

  3. Use R packages for data analysis and interpretation.

  4. Compare linear regression models between spreadsheets and R.

  5. Conduct textual data analysis using R.

Course Units

Unit 1: Introduction (3 hours)

  • Concepts: Data and data science, types of data, Big Data and characteristics, challenges in data analytics.

Unit 2: Data Preparation, Summarization, and Visualization using Spreadsheet (6 hours)

  • Topics include data cleaning, analysis techniques, and visualization tools like scatter plots, pivot tables, etc.

Unit 3: Getting Started with R (6 hours)

  • Basics of R: Commands, packages, structures, installation, importing data.

Unit 4: Descriptive Statistics Using R (6 hours)

  • Includes data visualization and statistical measures for analysis.

Unit 5: Predictive and Textual Analytics (9 hours)

  • Focus on regression models, textual data significance, and analysis techniques in R.

Practical Exercises (60 hours)

  1. Spreadsheet use for basic analytics and data cleansing.

  2. Implement R for datasets manipulation and analysis.

  3. Linear regression with analytical interpretation.

  4. Textual data analysis and reporting.

Contents Summary

Unit 1: Introduction to Data Analytics

  • Key Concepts: Definitions of data and data types, analytics classification (descriptive, diagnostic, predictive, prescriptive, cognitive), Big Data characteristics, and analytics applications.

  • Exercises: Engage with actionable insights through case studies exploring customer understanding, marketing impacts, and operational maneuvers.

Unit 2: Data Preparation & Visualization

  • Techniques: Clean data, handle missing values, statistical analysis using spreadsheets, modern visualization methods supporting analytics.

Useful Tips for Learning Data Analytics

  • Familiarize yourself with tools: Excel, R, and data visualization software.

  • Understand the differences between types of analytics (descriptive vs. predictive).

  • Actively practice coding in R and manipulating datasets to enhance familiarity with programming.

Challenges in Business Data Analytics

  1. Data Quality: Ensuring accuracy and completeness for reliable decision-making.

  2. Integration Issues: Combining data from diverse sources effectively.

  3. Skill Gap: Importance of skilled analysts to interpret complex data.

  4. Technology: Keeping up with evolving data analytics tools.

Future Trends in Data Analytics

  • Increased integration of AI to automate data-driven decision-making.

  • Focus on real-time analytics for immediate responses to business challenges.

Conclusion

Business Analytics is essential for modern organizations to optimize operations, understand market dynamics, and enhance customer satisfaction. It is crucial to leverage data accurately and efficiently to achieve competitive advantages.