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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma

Branch: Computer Engineering / Computer Science &amp; Engineering Subject Code: DI05000161 Subject Name:  Business Analytics

w. e. f. Academic Year:

2026-27

Semester:

5 th

Category of the Course:

MOPEC

| Prerequisite:   | Basic knowledge of computers, spreadsheets (MS Excel), and fundamental concepts  of data handling.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Rationale:      | In  modern  organizations,  large  volumes  of  data  are  generated  from  business  activities  such  as  sales,  marketing,  finance,  and  customer  interactions.  Business  Analytics helps transform this data into meaningful insights that support effective  decision-making and improve organizational performance. This course introduces  students to the fundamental concepts of business analytics and data visualization  using  tools  such  as  Microsoft  Excel  and  Power  BI.  Students  will  learn  basic  techniques  for  data  preparation,  analysis,  and  dashboard  creation  to  interpret  business  trends  and  performance.  The  course  also  provides  an  introductory  understanding of Artificial Intelligence applications in business analytics, helping  students develop analytical thinking and practical skills required for entry-level roles  in data analysis and business intelligence. |

## Course Outcome:

After Completion of the Course, the student will be able to:

|   No | Course Outcomes                                                                           | RBT Level   |
|------|-------------------------------------------------------------------------------------------|-------------|
|   01 | Identify key concepts and components of business analytics in  organizational contexts    | Understand  |
|   02 | Organize business data and perform data preprocessing techniques for  analysis.           | Understand  |
|   03 | Apply statistical techniques and business metrics for business data  interpretation.      | Apply       |
|   04 | Apply data visualization techniques to represent business data using Excel  and Power BI. | Apply       |

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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma

Branch: Computer Engineering / Computer Science &amp; Engineering Subject Code: DI05000161 Subject Name:  Business Analytics

## Teaching and Examination Scheme:

| Teaching Scheme  (in Hours)   | Teaching Scheme  (in Hours)   | Teaching Scheme  (in Hours)   | Total Credits  L+T+ (PR/2)   | Total Credits  L+T+ (PR/2)   | Assessment Pattern and Marks   | Assessment Pattern and Marks   | Assessment Pattern and Marks   | Total   |
|-------------------------------|-------------------------------|-------------------------------|------------------------------|------------------------------|--------------------------------|--------------------------------|--------------------------------|---------|
| L                             | T                             | PR                            | C                            | Theory                       | Theory                         | Tutorial / Practical           | Tutorial / Practical           | Marks   |
|                               |                               |                               |                              | ESE (E)                      | PA(M)                          | PA(I)                          | ESE (V)                        |         |
| 2                             | 0                             | 2                             | 3                            | 70                           | 30                             | 20                             | 30                             | 150     |

## Course Content:

| Unit  No.   | Content                                                                                                                                                                                                                                                                                                                                                                                                              |   No. of  Hours |   % of  Weighta ge |
|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|--------------------|
| 1           | Fundamentals of Business Analytics ●  Introduction to Business Analytics  ●  Evolution of Business Analytics  ●  Business Analytics vs Data Analytics vs Data Science  ●  Types of Analytics: Descriptive, Diagnostic, Predictive,  Prescriptive  ●  Business Analytics Lifecycle  ●  Components of Business Analytics System  ●  Applications of Business Analytics in Banking, Retail,  Healthcare, and E-commerce |              05 |                 16 |
|             | Business Data and Data Management ●  Types of Data: Structured, Semi-structured, Unstructured  ●  Sources of Business Data:  o Internal  Data  Sources:  sales  records,  customer  databases,  transaction data, ERP systems  o External  Data  Sources:  market  research,  social  media,  government datasets, third-party data                                                                                  |              07 |                 22 |

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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma Branch: Computer Engineering / Computer Science &amp; Engineering

Subject Code: DI05000161 Subject Name:  Business Analytics

|    | ●  Data Collection Methods: Surveys and questionnaires,  Observations, Transaction/Financial records, Online data  sources: Social media platforms, E-commerce websites,  Government open data portals, Web analytics tools, Online  survey platforms. Sensors: IoT devices, RFID systems, GPS  trackers, Smart meters, Wearable devices  ●  Data Quality Issues in Business Data  o Missing data  o Duplicate data  o Inconsistent data  o Inaccurate data  ●  Data  Cleaning  and  Data  Preparation  techniques:  Handling  missing data (deletion, imputation), removing duplicate records,  handling  inconsistent  data  (standardization,  validation  rules),  data  filtering  and  sorting,  data  type  correction  and  formatting,  outlier detection and treatment, data normalization and scaling,  data encoding (categorical to numerical)  ●  Data  Transformation  and  Data  Integration  techniques:  Data  aggregation  and  summarization,  data  normalization  and  standardization,  data  discretization  (binning),  feature  construction  and  transformation,  data  merging  and  joining  (multiple sources)  ●  Concept of Big Data in Business Analytics  ●  Data Warehouse: Definition and Characteristics  ETL Process: Extract, Transform, Load   |    |    |
|----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|----|
|  3 | Business Data Analysis and Metrics ●  Introduction to Business Data Analysis  ●  Descriptive Statistics: Mean, Median, Mode, Standard Deviation  ●  Trend Analysis  ●  Correlation Analysis  ●  Business Metrics and KPIs: Revenue growth rate, profit margin,  customer acquisition cost (CAC), customer lifetime value (CLV),                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | 06 | 22 |

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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma

Branch: Computer Engineering / Computer Science &amp; Engineering Subject Code: DI05000161 Subject Name:  Business Analytics

|    | customer  retention  rate,  churn  rate,  conversion  rate,  return  on  investment  (ROI),  sales  growth,  average  order  value  (AOV),  inventory turnover ratio, market share  ●  Role of Analytics in Business Decision Making  ●  Data-Driven Strategic Planning                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |    |    |
|----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|----|
|  4 | Data Visualization and Power BI ●  Introduction to Data Visualization and its Importance in  Business Analytics  ●  Principles of Effective Data Visualization  ●  Charts and Graphs in Business Analytics (Conceptual  Study)  o Bar  Chart:  concept,  structure,  advantages,  and  business  applications  o Line Chart: concept, use in trend analysis and time-series  data  o Pie  Chart:  concept,  representing  proportions  and  percentage distribution  o Histogram:  concept,  representation  of  frequency  distribution  o Scatter  Plot:  concept,  identifying  relationships  and  correlation between variables  ●  Selection of Appropriate Chart Types  o When to use different charts  o Comparison of chart types for business data  ●  Introduction to Microsoft Power BI  ●  Components of Power BI: Power BI Desktop, Power BI Service,  Power BI Mobile  ●  Power BI Architecture  ●  Data Sources in Power BI  ●  Data Import and Data Transformation  ●  Creating Visualizations | 07 | 22 |

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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma

Branch: Computer Engineering / Computer Science &amp; Engineering Subject Code: DI05000161 Subject Name:  Business Analytics

|    | ●  Dashboards and Reports                                                                                                                                                                                                                                                                                                                                                                                                         |    |     |
|----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|-----|
| 5  | Applications of Business Analytics and AI ●  Business Analytics in Decision Making  ●  Financial Analytics and Supply Chain Analytics  ●  Introduction to Artificial Intelligence in Business Analytics  ●  Role of AI in Business Decision Making  ●  AI Applications: Recommendation Systems, Customer  Behaviour Analysis, Fraud Detection, Chatbots  ●  Ethical Issues in AI and Data Analytics  ●  Data Privacy and Security | 05 |  18 |
|    | Total                                                                                                                                                                                                                                                                                                                                                                                                                             | 30 | 100 |

## Suggested Specification Table with Marks (Theory):

## Distribution of Theory Marks (in %)

|   R Level |   U Level |   A Level |   N Level |   E Level |   C Level |
|-----------|-----------|-----------|-----------|-----------|-----------|
|        10 |        60 |        30 |        00 |         0 |         0 |

Where R: Remember; U: Understanding; A: Application, N: Analyze and E: Evaluate C: Create (as per Revised Bloom's Taxonomy)

## References/Suggested Learning Resources:

## (a) Books:

1. Business Analytics - James Evans, Pearson
2. Data Science for Business - Foster Provost &amp; Tom Fawcett, O'Reilly
3. Business Intelligence Guidebook: From Data Integration to Analytics - Rick Sherman
4. Data Analytics Made Accessible - Anil Maheshwari
5. Data Analysis with Microsoft Power BI - Brian Larson, O'Reilly

## (b) Open-source software and website:

Open-Source Software

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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma

## Branch: Computer Engineering / Computer Science &amp; Engineering

Subject Code:

DI05000161

Subject Name:  Business Analytics

1. Python (Anaconda Distribution / Jupyter Notebook) - For data analysis and visualization.
2. NumPy and Pandas Libraries - For numerical computation and data manipulation.
3. Matplotlib / Seaborn - For data visualization in Python.
4. KNIME Analytics Platform - Open-source tool for data analytics and workflow-based analysis.
5. Orange Data Mining - Visual programming tool for data mining and machine learning.
6. LibreOffice Calc - Open-source alternative to MS Excel for spreadsheet analysis.

## Websites / Learning Platforms

1. https://powerbi.microsoft.com
2. https://www.kaggle.com
3. https://www.tutorialspoint.com/power\_bi/index.htm
4. https://learn.microsoft.com/en-us/training/powerplatform/power-bi

## Suggested Course Practical List

1. Explore business datasets and perform basic data analysis using Microsoft Excel.
2. Use Excel functions to calculate mean, median, standard deviation and correlation.
3. Create Pivot Tables and Pivot Charts for sales data analysis.
4. Visualize business data using Excel charts (Bar, Line, Pie).
5. Import a dataset into Power BI and perform basic data transformation.
6. Create visualizations in Power BI (bar chart, pie chart, line chart).
7. Create an interactive dashboard in Power BI for sales or marketing data.
8. Analyze customer behavior dataset using Python.
9. Perform KPI visualization using Power BI.
10. Create financial performance dashboard using Power BI.
11. Create filters and slicers in Power BI to enable interactive data analysis.
12. Analyze a dataset related to supply chain or marketing analytics using Power BI.
13. Perform sales forecasting in Power BI using time-series forecasting on monthly sales data.
14. Build a complete business analytics report combining Excel, Python and Power BI outputs.
15. Publish a Power BI report to Power BI Service and share the dashboard for business reporting.

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## GUJARAT TECHNOLOGICAL UNIVERSITY

Program Name: Diploma Engineering Level: Diploma

Branch: Computer Engineering / Computer Science &amp; Engineering Subject Code: DI05000161 Subject Name:  Business Analytics

## List of Laboratory/Learning Resources Required:

1. Computer system with a minimum of 8 GB RAM
2. Operating system: Windows / Linux
3. Microsoft Excel
4. Microsoft Power BI Desktop
5. Python environment (Anaconda / Jupyter Notebook / Google Colab)
6. Internet access for datasets

## Suggested Activities for Students:

1. Mini project on Business Analytics using Power BI.
2. Analyze real-world datasets (sales, marketing, finance).
3. Prepare presentation on AI applications in business analytics.
4. Participate in data analytics competitions or hackathons.
5. Encourage students to enroll in MOOCs related to data analytics and business intelligence