Portfolio Case Study: Data Engineering, Analytics & Data Science β Dashboard Metrics, KPI Calcs, SQL Reporting Queries
β±οΈ Length: 1.8 total hours
β 5.00/5 rating
π₯ 1,086 students
π December 2025 update
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- Course Overview
- Comprehensive Data Scrubbing Framework: This course provides a deep dive into the essential process of data cleaning within an e-commerce context, focusing on the transition from messy, raw transactional logs to a refined, structured format ready for high-level business analysis.
- E-commerce Data Specificity: Participants will engage with datasets that mirror real-world digital retail environments, including customer profiles, order histories, and product inventories that suffer from common structural inconsistencies and data entry errors.
- Project-Based Portfolio Development: Unlike theoretical courses, this module is structured as a professional case study, allowing students to build a tangible project that demonstrates their ability to solve complex data engineering problems to potential employers or clients.
- Lifecycle of Data Engineering: The curriculum walks through the entire lifecycle of a data project, starting from initial data profiling and identification of anomalies to the final stage of generating clean, query-able tables for downstream analytics.
- Strategic Metric Preparation: A significant portion of the course is dedicated to preparing datasets specifically for KPI calculations, ensuring that the underlying data quality supports accurate reporting for metrics like Customer Lifetime Value (LTV) and Monthly Recurring Revenue (MRR).
- Requirements / Prerequisites
- Fundamental SQL Competency: Learners should possess a foundational understanding of SQL syntax, including the ability to perform basic SELECT statements, filter data using WHERE clauses, and execute simple table joins across relational schemas.
- Analytic Mindset: A prerequisite for success in this course is the ability to look at “dirty” data and hypothesize where errors might originate, such as duplicate entries, incorrectly formatted dates, or logically inconsistent null values.
- Database Environment Access: While the course is self-contained, having access to a SQL environmentβsuch as PostgreSQL, MySQL, or a cloud-based warehouse like BigQueryβis recommended for students who wish to follow along with the hands-on exercises in real-time.
- Interest in E-commerce Business Logic: Understanding basic retail concepts, such as SKU management, return rates, and shipping statuses, will help learners better grasp why certain data cleaning steps are prioritized over others in a commercial setting.
- Skills Covered / Tools Used
- Advanced SQL Cleaning Functions: Students will master the use of specialized SQL functions such as COALESCE for handling null values, CAST and CONVERT for data type synchronization, and TRIM/REGEXP for cleaning up messy string data and email formats.
- CTE and Subquery Mastery: The course emphasizes the use of Common Table Expressions (CTEs) to break down complex cleaning logic into modular, readable, and maintainable steps, which is a hallmark of senior-level data engineering.
- Data Profiling and Auditing: Participants will learn techniques to audit their data before and after cleaning, utilizing aggregate functions and window functions to detect outliers and verify that no critical information was lost during the transformation process.
- Automated KPI Logic: Beyond simple cleaning, the course covers the logic required to calculate e-commerce KPIs directly within SQL queries, including Average Order Value (AOV) and churn rates, ensuring the data is dashboard-ready.
- Performance Optimization: Instruction includes tips on how to write efficient SQL that minimizes computational load, which is critical when dealing with large-scale e-commerce datasets containing millions of individual transaction rows.
- Benefits / Outcomes
- High-Impact Portfolio Piece: Upon completion, students will have a sophisticated SQL project that they can showcase on GitHub or LinkedIn, proving they can handle the “unsexy” but vital work of data preparation that occupies 80% of a data scientist’s time.
- Enhanced Data Integrity Knowledge: Learners will develop a professional-grade intuition for data integrity, enabling them to identify systemic flaws in data collection pipelines and propose architectural solutions to prevent future data corruption.
- Marketable Technical Proficiency: The skills gained bridge the gap between basic SQL knowledge and professional data engineering, making students more competitive for roles such as Data Analyst, Analytics Engineer, or Business Intelligence Developer.
- Dashboard-Ready Datasets: Graduates will be able to produce clean, flattened tables that can be plugged directly into visualization tools like Tableau or Power BI without the need for further transformation within those tools.
- Efficiency in Reporting: By learning to automate the cleaning and KPI calculation process in SQL, participants will be able to reduce the manual overhead associated with weekly and monthly business reporting cycles in their current or future roles.
- PROS
- Time-Efficient Learning: The course packs a massive amount of practical knowledge into less than two hours, making it ideal for busy professionals looking to upskill quickly without unnecessary fluff.
- Exceptional Student Satisfaction: Boasting a perfect 5.00/5 rating, the course content is highly vetted by over a thousand students, ensuring clarity and educational quality.
- Up-to-Date Content: With the December 2025 update, the methodologies and SQL standards taught are current with the latest industry trends and cloud database capabilities.
- Focus on Real-World Application: By shunning sterile, perfect datasets for “messy” e-commerce data, the course prepares students for the actual challenges they will face in a corporate data environment.
- CONS
- Advanced Pace: Due to the concise nature of the course, absolute beginners who have never seen a SQL query may find the speed of the cleaning transformations challenging without prior introductory study.
Learning Tracks: English,IT & Software,Other IT & Software
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