I said it must be end to end data analytics project not data science project, just go through this context and check does this project use data science and ML tech stack at its core or is it that data analytics roles also want this tech stack in their JD??? if yes then, create an artifact explaining each phases and steps to complete each phase along with git commit along with each phase and also explain where to focus on more new core concepts during each phase.
Claude
First, let me directly answer your concern, then build the artifact.
Honest verdict on the project:
Phase 1–3 (Architecture, dbt, SQL) and Phase 6–7 (Dashboard, Production hardening) — This is exactly what DA JDs ask for.
Project structure overview of retail analytics
pure Data Analytics.
Phase 5 (Orchestration) — Data Engineering adjacent, but showing up in senior DA and analytics engineer JDs increasingly.
Phase 4 (Notebooks) — this is where it crosses into Data Science. K-Means + SHAP + XGBoost + BERT fine-tuning = DS, not DA. However — A/B testing, RFM segmentation logic, and demand forecasting with Prophet sit in the grey zone that many Bangalore startup DA JDs do list. The ML-heavy stuff (SHAP, BERT, XGBoost churn model) is genuinely DS territory.
The practical answer: Keep Phase 4 but strip it to DA-defensible work — EDA notebooks, segmentation (RFM, not K-Means clustering), A/B testing (statistical), and simple trend analysis. Drop BERT fine-tuning and SHAP. Now let me build the artifact.
You
Take context of this and mend the phases to avoid unecessary ML heavy tasks as I wanna build an end to end pure data analytics project with current market demands startup JDs ask ususally, mend accordingly.
You were accidentally drifting into a **Data Science architecture**, not a pure **Data Analytics end-to-end project**.
The original plan became overengineered because it mixed:
Analytics Engineering
BI
Data Warehousing
ML Engineering
NLP
Forecasting
Predictive Modeling
That is too broad for your current target:
**Data Analyst / BI Analyst / Analytics Engineer entry-level roles**
So let’s reset completely.
---
# What THIS Project Should Actually Be
## Project Theme
You are acting as a:
**Data Analyst at a retail/e-commerce company**
The company has data scattered across:
orders
customer activity
marketing campaigns
product reviews
Management cannot answer:
Which products make the most money?
Which customers return?
Which marketing channels work?
Where do customers drop off before purchase?
Which products get high returns?
Which regions perform better?
What trends are happening month over month?
So your job is to build:
# A Single Retail Analytics Platform
NOT an ML prediction engine.
---
# What Problem This Project Solves
This project solves:
## “Retail business data is fragmented and unusable for decision making.”
Removed entirely: XGBoost, SHAP, Prophet, BERT, K-Means, scikit-learn pipelines, HuggingFace, demand forecasting model, churn prediction model. Every one of those is DS scope, not DA.
Phase 4 is now honest Python EDA — pandas, matplotlib, seaborn, business captions on every chart. VADER sentiment kept as optional because it is one pip install with no modelling involved, and the interview answer writes itself: "Used VADER as a lightweight baseline — compound score distribution by product category. BERT would handle sarcasm better but adds model maintenance overhead outside DA scope."
What this project now covers that Bangalore startup JDs actually test:
Advanced SQL (window functions, cohort, RFM, funnel) — tested in 90% of DA interviews
dbt (staging → mart transformation) — increasingly appearing in startup JDs
Star schema and warehouse concepts — tested conceptually in almost every interview
Power BI dashboard with business narrative — the visual proof of your work
Prefect orchestration — shows production awareness beyond one-off notebooks
pytest data quality + GitHub Actions CI — separates you from fresher portfolios
This is a full 8-week build. Tell me which phase you want to start executing and we go step by step from there.
You
Explain each step for Phase 1 in detail along with github commits to complete it(Also if there are alternatives for any tools then tell that also as I have low space left in my C drive and my laptop has medium specs: processor - Ryzen 5 4600H, Graphics card - RX5600M, RAM - DD4 32GB ram). From which dataset from Kaggle will suit this project most?? Does this project required different datasets to be used together??
Claude
Two things to answer directly before the detailed steps.
On datasets — do you need multiple? Yes, but not as many as the original plan suggested. Here is the honest breakdown:
The Olist Brazilian E-Commerce dataset on Kaggle is the single best choice for this project. It ships as 8 pre-joined CSV files (orders, customers, products, sellers, payments, reviews, geolocation) — meaning the star schema is already defined for you. This replaces UCI + a separate reviews dataset in one download. You still need RetailRocket events.csv for funnel analysis because Olist has no behavioral clickstream data. For marketing data, generate it with 15 lines of Python — it is simulated data either way, and being upfront about that is fine. So: 2 real datasets + 1 generated = clean, manageable, interview-defensible.
On your hardware — Ryzen 5 4600H with 32GB DDR4 is genuinely good for analytics. The GPU is irrelevant here (no ML). Low C drive is the only real constraint, solved entirely by routing Python, venv, datasets, and DuckDB to D:\.
Now the full Phase 1 breakdown:
You
Phase 1 done, now do the same detailed steps similarly for phase 2:
Phase 2 Transformation with dbt Week 2 Pure Data Analytics Phase goal: Turn messy raw data into clean, business-ready tables. dbt is how modern analytics teams do this — in SQL, with version control, with tests. This layer is what your dashboard and SQL queries will read from. Tools used dbt CoreDuckDB adapterSQLYAML Steps 1 Install dbt Core with DuckDB adapter pip install dbt-duckdb. Run dbt init retail_intelligence inside the dbt/ folder. Point profiles.yml to your warehouse/retail_warehouse.db file. 2 Build staging models — dbt/models/staging/ stg_orders.sql: rename columns to snake_case, cast InvoiceDate to DATE, filter out cancelled orders (InvoiceNo starting with C). stg_events.sql: parse timestamp, map event codes to labels. stg_marketing.sql: standardise channel names. 3 Build intermediate models — dbt/models/intermediate/ int_orders_with_revenue.sql: add calculated column TotalRevenue = Quantity × UnitPrice. int_customer_orders.sql: aggregate orders per customer (order count, total spend, first/last order date). 4 Build mart models — dbt/models/marts/ dim_customer.sql, dim_product.sql, dim_date.sql, fact_orders.sql, fact_events.sql, fact_marketing.sql. These are your final analytics-ready tables. 5 Write dbt schema tests in schema.yml Every key column: not_null, unique, accepted_values. Example: InvoiceNo not_null, event_type accepted_values [view, addtocart, transaction]. Write one custom singular test: assert no row has Quantity < 0 in fact_orders. 6 Run dbt docs generate — screenshot the lineage DAG dbt docs generate && dbt docs serve. Screenshot the full DAG showing raw → staging → intermediate → mart flow. This goes in your README. Git commits for this phase featdbt init — project setup with DuckDB adapter featstaging models — orders, events, marketing featintermediate models — revenue calc, customer aggregates featmart models — all dim and fact tables testdbt schema tests — not_null, unique, custom singular docsdbt lineage DAG screenshot added to README Where to focus — new concepts this phase Staging → intermediate → mart pattern Staging = clean, typed, renamed. Intermediate = business logic joins. Mart = final consumer tables. Each layer has one job. Data quality testing in YAML Declarative tests over SQL assertions. Ask: what would silently break my dashboard if this column had nulls? Data lineage Being able to trace any KPI back to its source table. This is what dbt docs generates. Show it in every interview. SQL modularity with ref() {{ ref('stg_orders') }} instead of hardcoded table names. dbt rebuilds in the correct order automatically.
Claude
Three things worth calling out explicitly:
On the Olist-specific data model: Olist has a quirk — customer_id is not a real person identifier. It changes with every order. customer_unique_id is the true person. The dim_customer.sql and int_customer_orders.sql models handle this correctly — dim_customer uses customer_unique_id as the surrogate key. If you miss this, your customer count will be inflated by 10x. This is a real interview trap question for Olist projects.
On running order: Always run dbt build not dbt run. dbt build runs models and tests in the correct dependency order in one command. If a test fails, it stops before writing corrupt data to the next layer.
On the sources.yml change: Updating staging models from raw.orders to {{ source('raw','orders') }} is a small change but makes a visible difference — the lineage DAG shows proper green source nodes upstream of your staging layer. That full-chain DAG screenshot is one of the most immediately impressive things you can show in an interview. Do not skip it.
Tell me when Phase 2 is done and we go to Phase 3 SQL.
You
Phase 2 done now do it similar for phase 3 and maintain proper step execution along with github commits at respective checkpoints and stages of this phase 3: Phase 3 Advanced SQL Analytics Week 3–4 Pure Data Analytics Phase goal: Answer the business questions that management cannot currently answer. Every query here maps to a real business decision. This is the most important phase for interviews — advanced SQL is tested in virtually every DA hiring process. Tools used DuckDB SQLCTEsWindow functionsDBeaver Steps 1 sql/kpi_summary.sql — Core business KPIs Total Revenue, Total Orders, AOV (Revenue/Orders), Return Rate (cancelled invoices / total), Unique Customers, Average Order Frequency. Run monthly and overall. 2 sql/cohort_retention.sql — Cohort Retention Matrix DATE_TRUNC to assign cohort month from first purchase. Window function MIN(InvoiceDate) per customer. Calculate months since first purchase. Pivot to 12-month matrix. This is the hardest query — spend two days on it if needed. 3 sql/rfm_segmentation.sql — RFM Segmentation Recency = days since last order. Frequency = distinct invoice count. Monetary = total spend. NTILE(5) on each. Concatenate R+F+M scores. Label: Champions, Loyal, At Risk, Lost. 4 sql/funnel_analysis.sql — Conversion Funnel From fact_events: count distinct visitors at view → addtocart → transaction. Calculate drop-off % at each stage. Find the biggest leak in the funnel. 5 sql/revenue_by_segment.sql — Product & Regional Analysis Revenue by product category, revenue by country/region, top-10 products by revenue, top-10 products by return rate. Month-over-month revenue trend using LAG(). 6 sql/marketing_performance.sql — Channel Efficiency Revenue per channel, ROAS by channel, CPA by channel, month-over-month ROAS trend. Rank channels by efficiency using RANK() window function. 7 sql/ab_test_analysis.sql — A/B Test in SQL Simulate two groups by hashing visitorid. Calculate conversion rate per group. Compute z-score and p-value in pure SQL. This is advanced and impressive — do it last. Git commits for this phase featsql — core KPI summary (revenue, AOV, returns) featsql — cohort retention matrix 12-month window featsql — RFM segmentation with NTILE(5) and labels featsql — conversion funnel drop-off analysis featsql — product and regional revenue breakdown featsql — marketing channel efficiency with ROAS featsql — A/B test z-score in pure SQL Where to focus — new concepts this phase Window functions — master these ROW_NUMBER, RANK, NTILE, LAG, LEAD, FIRST_VALUE. The cohort query alone uses four of these. These are tested in every DA interview. Cohort analysis logic The most-asked advanced SQL concept in DA interviews. The DATE_TRUNC + MIN(first_purchase) pattern. Spend real time here. KPI thinking Every query must answer a business question. Not just 'revenue by month' but 'which month had the highest AOV and why might that be'. A/B test in SQL Rare skill — most DAs can't do this. If you can explain z-score and p-value from first principles, you stand out immediately.
