My Favorite Data Science Tech Stack Recipes

I specialize in optimizing customer acquisition, engagement strategies, and pricing models in highly competitive, fast-paced industries like Tech and Finance. In these environments, data-driven decision-making isn’t just an advantage—it’s a necessity. To stay ahead, businesses must rely on robust analytics frameworks that seamlessly integrate data extraction, transformation, modeling, and visualization to drive strategic insights.

Over the years, I’ve designed and maintained tailored dashboards and reports, turning raw data into actionable intelligence for marketing and sales teams. Whether it’s tracking lead generation, analyzing marketing attribution, forecasting sales, or refining customer segmentation, a well-structured data science tech stack is the foundation of effective analytics. Hence before launching any Business Intelligence project, I always ask myself key questions: Do I need to pull data from multiple sources? (CRM, Web Analytics, E-commerce …)? Should the report or dashboard be interactive or standardized? Does it also need to be auto-updated to eliminate manual effort?

The answers shape the choice of tools and workflows, which I categorize into three highly effective data science “recipes,” each optimized for different analytical needs: scalability with cloud-based analytics and cloud-based dashboarding [1], flexibility with cloud-based analytics and excel-based structured reporting [2], and autonomy with structured standalone analytics & excel-based tailored reporting [3]. Each recipe provides a step-by-step implementation guide, showing how to extract, transform, and visualize data efficiently.

1. Cloud-Based Analytics & Cloud-Based Dashboarding

Best for scalability: cloud-based automation, optimized initial processing, flexible and centralised final processing, scalable Business Intelligence, interactive self-service reporting.

Ingredients: Data warehouse + Power Query + Power BI

Step 1: Extract data from your CRM (SFDC, HubSpot) or Web Analytics platform (Google Analytics) and sync to the data warehouse (SQL RDBMS, Azure, Snowflake) by writing a python script using your CRM’s API (SFDC REST API Real-Time data Sync).
Step 2: Pre-process the data in the warehouse with SQL queries (clean, join, aggregate).
Step 3 (optional): Train any Machine Learning predicitve model in Jupyter Lab and deploy it by storing back the predictions in the data warehouse via your Python script. Obviously, re-process the data in your warehouse before moving to the next step.
Step 4: Load the structured data into Power Query via API and external excel data if necessary by direct connexion.
Step 5: Clean, merge, model (basic regressions and classifications) and make calculations on the data connecting a python script to Power Query or with M language directly into Power Query.
Step 6: Load the final data into the Power BI Model. You can perform additional DAX-based calculations directly inside Power BI.
Step 7: Build the dashboard with built-in visualizations (tables, matrices, bar charts, pie charts, funnel charts, waterfalls …), set up automatic refresh and display the transformed data in Power BI.

Configured and maintained tailored dashboards to: track lead generation, funnel performance and conversion across marketing campaigns; review multi-channel marketing attribution (last-touch, first-touch); track sales pipeline progression across multiple product lines; predict customer lifetime value, lead quality and engagement; compute lead and engagement scoring based on attribution scores set up from predictions.

2. Cloud-Based Analytics & Excel-Based Structured Reporting

Best for flexibility: cloud-based processing, optimized initial processing, Excel-based automation, structured Business Intelligence, standardized reporting (finance, sales, performances), print-ready structured reporting.

Ingredients: Data warehouse + Power Query + Power Pivot + Excel/VBA

Step 1: Extract data from your CRM (SFDC, HubSpot) or Web Analytics platform (Google Analytics) and sync to the data warehouse (SQL RDBMS, Azure, Snowflake) by writing a python script using your CRM’s API (SFDC REST API Real-Time data Sync).
Step 2: Pre-process the data in the warehouse with SQL queries (clean, join, aggregate).
Step 3 (optional): Train any Machine Learning predicitve model in Jupyter Lab and deploy it by storing back the predictions in the data warehouse via your Python script. Obviously, re-process the data in your warehouse before moving to the next step.
Step 4: Load the structured data into Power Query via API and external excel data if necessary by direct connexion.
Step 5: Clean and merge the data connecting a python script to Power Query or with M language directly into Power Query.
Step 6: Model (basic regressions and classifications) and perform DAX-based calculations on the data after loading the cleaned dataset into Power Pivot.
Step 7: Connect the Power Pivot model to Excel.
Step 8: Build the final report in Excel with PivotTables and charts.
Step 9: Automate the report printing & export to PDF/PPT using VBA.

Configured and maintained tailored reports to: build dynamic sales forecasting and financial models to improve target and budget planning accuracy; check if the customer segmentation is still relevant; visualize performance across a complex territory matrix for executive decision-makers that tend to request new features on a monthly bases.

3. Structured Standalone Analytics & Excel-Based Tailored Reporting

Best for autonomy: no cloud-based reliance except for export, data too large for Excel but too lightweight for full RDBMS, data too structured for Power Query, Excel-based automation, local-based processing, post-processing data of less than 10 000 items, tailored reporting (finance, sales, performances), print-ready reporting.

Ingredients: Data Warehouse + Microsoft Access + Excel/VBA

Step 1: Extract data from your CRM (like SFDC) and sync to Microsoft Access by writing a python script using your CRM’s API (SFDC REST API Real-Time data Sync).
Step 2: Pre-process the data in Access with SQL queries (clean, join, aggregate).
Step 3: Load the structured data of less than 10 000 unique items (or rows) into Excel directly via a MS Query connection to Access.
Step 4: Clean, merge, model (basic regressions and classifications) and make calculations on the data into Excel using formulas (SUMIFS, INDEX-MATCH …) or VBA.
Step 5: Build the final report in Excel with PivotTables and charts.
Step 6: Automate the report printing & export to PDF/PPT using VBA.

Configured and maintained tailored reports to: perform exploratory data analysis on competitive intelligence and customer research to identify customer segments; identify high-performing products; review marketing efficiency; list urgent calls to action for numerous sales and marketing managers across multiple product lines and business units based on lead scoring, engagement scoring and business priorities.