Get started with this Data Analytics Program and explore everything about Data Analytics lifecycle. Start your journey with the preparatory courses on Excel, Statistics and an introduction to Data Analytics along with SQL training.

Excel Formulas and Functions (Logical, Lookup, Statistical)

Capstone Project Requirement – Diploma in Data Analytics 1. Purpose of the Capstone Project The Capstone Project is a culminating, applied assessment that requires students to integrate and demonstrate the full range of knowledge and skills acquired throughout the Data Analytics diploma. Students will work on a real-world business or organizational scenario, applying industry-standard tools and methodologies to generate actionable insights and professional-quality deliverables. 2. Project Scenario (Real-World Context) Students must select or be assigned a project based on one of the following real-world domains: Retail & E-commerce Banking & Finance Healthcare & Public Health Marketing & Customer Analytics Operations & Supply Chain Education or Government Data Technology or SaaS Business Each project must be framed as a business problem, not a purely technical exercise. Example scenarios: Improving customer retention for a retail company Analyzing sales performance across regions Identifying operational inefficiencies in logistics Monitoring key performance indicators (KPIs) for management decision-making 3. Project Requirements A. Business Problem Definition Students must: Clearly define the organization and industry context Identify a specific, realistic business problem or opportunity State project objectives and key analytical questions Identify relevant stakeholders and expected business impact Deliverable: Written problem statement (1–2 pages) B. Data Collection and Preparation Students must: Acquire data from one or more sources: Relational databases CSV / Excel files Simulated APIs or public datasets Assess data quality and relevance Perform data cleaning (handling missing values, duplicates, inconsistencies) Document assumptions and limitations Tools: SQL and/or Python Excel (optional for initial inspection) Deliverable: Cleaned dataset Data preparation documentation C. ETL Workflow Implementation Students must: Design a basic ETL process: Extract data from source(s) Transform data for analysis and reporting Load data into an analysis-ready format Demonstrate understanding of data pipelines and data flow Ensure data accuracy and consistency Deliverable: ETL process description (diagram or written explanation) Transformed dataset used for analysis D. Data Analysis (SQL and/or Python) Students must: Perform exploratory data analysis (EDA) Apply appropriate aggregations and calculations Identify trends, patterns, and anomalies Generate meaningful insights aligned with the business problem Minimum requirements: Use SQL queries for structured analysis Use Python (Pandas, NumPy) for advanced manipulation or analysis Deliverable: SQL scripts and/or Python notebooks Analytical summary of findings E. Data Visualization and Dashboarding Students must: Design an interactive dashboard using Power BI or Tableau Apply data visualization best practices Create visuals that support business decision-making Include KPIs, filters, and clear labels Deliverable: Fully functional dashboard Screenshots or shared dashboard link (if applicable) F. Insights, Recommendations, and Business Value Students must: Translate analytical results into business insights Provide data-driven recommendations Explain how insights support decision-making Address potential limitations and future improvements Deliverable: Insight and recommendation section in final report G. Final Report and Presentation Students must submit: 1. Written Report Executive Summary Business Problem and Objectives Data Sources and Methodology Analysis and Key Findings Visualizations and Dashboard Explanation Recommendations and Conclusion Length: 15–25 pages (excluding appendices) 2. Oral Presentation 10–15 minutes professional presentation Use slides and dashboard demonstration Answer questions as if presenting to business stakeholders 4. Assessment Criteria (High-Level) The Capstone Project will be evaluated based on: Problem clarity and business relevance Data quality and preparation Correct use of SQL, Python, and ETL concepts Depth and accuracy of analysis Quality and effectiveness of visualizations Strength of insights and recommendations Professionalism of report and presentation 5. Academic Integrity Projects must be individual work unless otherwise approved Data sources must be properly cited Plagiarism or fabricated results will result in disqualification 6. Learning Outcomes Upon successful completion, students will demonstrate the ability to: Solve real business problems using data Apply industry-standard analytics tools Communicate insights effectively to non-technical audiences Deliver end-to-end analytics solutions