Focus: More complex datasets, deeper logic, Databricks workflows, SQL modeling, automation, storytelling, professional‑level capstone.

 

Week 1 — Databricks Foundations & Advanced Orientation

Session A — Databricks Orientation & Setup

Students step into a real analytics environment and learn how modern data teams work.

• What analytics careers look like

• Databricks workspace tour (Catalog, Volumes, Repos, SQL Warehouses)

• First Python notebook in Databricks

• Variables, f‑strings, simple functions

• Upload medium‑sized dataset (300–1,000 rows)

Portfolio Artifact: Structured Databricks notebook

Homework: Create a function that prints formatted output using f‑strings

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Session B — First Python Notebook (Professional Workflow)

• Expressions + logic

• Clean notebook structure (markdown sections, comments)

• Mini‑project: Personal Data Profile

• Save notebook to workspace

Portfolio Artifact: First Python notebook

Homework: Add two new fields to the Personal Data Profile

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Week 2 — Python Logic, Functions & Data Structures

Session A — Lists, Dictionaries, Loops, Functions

Students build reusable logic patterns used in real analytics scripts.

• Lists

• Dictionaries

• For loops

• If/else logic

• Intro to functions (parameters + return values)

Portfolio Artifact: Logic + functions notebook

Homework: Build a function that analyzes a list of items

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Session B — Mini Project: Teen Insights Analyzer

• Use loops + functions

• Process structured data

• Print formatted insights

• Save results to Delta Lake

Portfolio Artifact: Python script with functions

Homework: Add one new insight to the analyzer

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Week 3 — Python + Pandas for Real Data (Advanced)

Session A — Pandas Cleaning + Delta Lake

Students work with real datasets and modern storage patterns.

• Import CSV into Databricks Volumes

• head(), info(), describe()

• Clean data (drop NA, rename columns, filter rows)

• Write cleaned data to Delta Lake

• Explore table in Catalog

Portfolio Artifact: Cleaned Delta table

Homework: Clean a second dataset + write to Delta

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Session B — Visualization + Analysis

• Matplotlib + Seaborn

• 3–4 charts (histogram, boxplot, scatter, heatmap)

• Mini‑analysis of a real dataset (Spotify, gaming, finance, sports)

Portfolio Artifact: Notebook with cleaned data + charts

Homework: Create 3 additional charts

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Week 4 — SQL Fundamentals + Python Integration

Session A — SQL Basics in Databricks

• SELECT

• WHERE

• ORDER BY

• Query Delta tables in SQL Warehouse

• Explore schemas + tables in Catalog

Portfolio Artifact: SQL basics notebook

Homework: 15 SQL queries on the provided dataset

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Session B — SQL + Python Integration

• Run SQL inside Python using spark.sql()

• Convert SQL results to pandas

• Export results to CSV for Tableau

• Build a simple Python‑SQL workflow

Portfolio Artifact: SQL query report + Python integration notebook Homework: Write 3 insights from SQL results

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Week 5 — Intermediate SQL + Real Data Modeling

Session A — Joins + Aggregates + Modeling

Students learn the SQL patterns used in real analytics teams.

• INNER JOIN

• LEFT JOIN

• GROUP BY

• COUNT, SUM, AVG

• Data modeling basics (staging → clean → model tables)

• Create new Delta tables from SQL logic

Portfolio Artifact: Modeled Delta table

Homework: Answer 5 real questions using SQL joins

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Session B — Data Story Project

• Choose a real question

• Run SQL joins + aggregates

• Export results

• Write structured insight summary in notebook markdown

Portfolio Artifact: Data Story Using Joins

Homework: Draft capstone dataset selection

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Week 6 — Tableau Visualization Principles (Advanced)

Session A — Visualization Theory + Databricks Connection

• Chart selection

• Color theory

• Layout + clarity

• Connect Tableau → Databricks SQL Warehouse

• Build first worksheet

Portfolio Artifact: First Tableau worksheet

Homework: Improve one chart using feedback

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Session B — Build Visualizations

• Bar chart

• Line chart

• Scatter plot

• Heatmap or dual‑axis chart

• Peer review + refinement

Portfolio Artifact: Four polished Tableau charts

Homework: Add a calculated field to one chart

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Week 7 — Dashboard Building + Storytelling

Session A — Dashboard Design

• Layout

• Filters

• Interactivity

• Storytelling principles

• KPI selection

• Multi‑sheet dashboard structure

Portfolio Artifact: Dashboard draft

Homework: Capstone outline + dataset selection

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Session B — Build Dashboard

• Assemble full dashboard

• Publish to Tableau Public

• Share link

• Finalize capstone outline

Portfolio Artifact: Published Tableau dashboard

Homework: Prepare capstone presentation

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Week 8 — Capstone Showcase (Professional Level)

Session A — Capstone Build

Students combine everything they’ve learned into a professional‑grade project.

• Python analysis

• SQL modeling + queries

• Tableau dashboard

• Write summary + insights

• Prepare presentation

Portfolio Artifact: Full capstone project (Python + SQL + Tableau)
 

Session B — Presentations

• 5–7 minute capstone presentation

• Optional parent showcase

• Certificates + celebration

Portfolio Artifact: Final capstone + dashboard link