Power BI and Python Course

Lesson 1 • Setting Up Your Development Environment

Install Power BI Desktop and Python, then configure paths and dependencies. This foundation ensures every subsequent exercise runs without environment errors.

Lesson 2 • Power BI Interface and Core Concepts

Navigate the Power BI Desktop interface and understand reports, datasets, and workspaces. Familiarity with the UI accelerates all later data modeling work.

Lesson 3 • Python Fundamentals for Data Work

Cover Python syntax, data types, and control flow relevant to analytics tasks. These skills underpin every Python script used inside Power BI.

Lesson 1 • Running Python Scripts as Data Sources

Execute Python scripts directly inside Power BI to generate or transform source data. This bridges both tools into a unified ingestion pipeline.

Lesson 2 • Consuming REST APIs and Web Data

Fetch data from REST APIs using Python requests and Power BI web connectors. Students gain skills to access live, dynamic data unavailable in static files.

Lesson 3 • Loading Data with Python and pandas

Use pandas to read CSV, Excel, JSON, and SQL sources into DataFrames. This mirrors Power BI ingestion and enables Python-side preprocessing.

Lesson 1 • Reshaping and Aggregating Data

Pivot, melt, group, and aggregate data in both pandas and Power Query. Reshaping skills are essential for building the structured tables Power BI models require.

Lesson 2 • Power Query Editor Essentials

Use Power Query to filter, rename, split, and merge columns without code. These GUI-based skills handle the majority of routine cleaning tasks.

Lesson 3 • Advanced Power Query with M Language

Write M expressions to automate complex transformations beyond the GUI. Custom M functions extend Power Query to handle dynamic, parameterized logic.

Lesson 1 • Optimizing the Data Model

Reduce model size and improve query speed through column removal, aggregations, and storage modes. Optimization ensures dashboards remain responsive at enterprise data volumes.

Lesson 2 • Introduction to DAX

Write calculated columns and basic measures using DAX syntax and evaluation context. DAX is the primary language for all Power BI analytical calculations.

Lesson 3 • Intermediate DAX Patterns

Apply CALCULATE, FILTER, and time-intelligence functions to build business KPIs. These patterns cover the majority of real-world analytical requirements.

Lesson 1 • Formatting and Design Principles

Apply consistent colour, typography, and layout to produce professional reports. Design discipline reduces cognitive load and increases stakeholder trust.

Lesson 2 • Choosing the Right Visual Type

Match data characteristics to bar, line, scatter, map, and table visuals. Correct visual selection is the foundation of honest, readable data communication.

Lesson 3 • Python Visuals Inside Power BI

Render Matplotlib and Seaborn charts as Power BI visuals using the Python visual object. This unlocks statistical and custom chart types unavailable natively.

Lesson 1 • Descriptive Statistics with Python

Compute summary statistics, distributions, and correlations using pandas and NumPy. These metrics form the baseline for all deeper analytical work.

Lesson 2 • Hypothesis Testing Fundamentals

Apply t-tests, chi-square tests, and ANOVA using SciPy to validate business hypotheses. Statistical testing provides evidence-based justification for decisions.

Lesson 3 • Surfacing Python Analytics in Power BI

Pass Python-computed statistics and model outputs back into Power BI reports. This closes the loop between Python analysis and business-facing dashboards.

Lesson 1 • Embedding Predictions in Power BI

Load saved scikit-learn models inside Power BI Python scripts to score new data. Predictions become filterable columns in live reports without re-training.

Lesson 2 • Classification and Regression Models

Train decision trees, random forests, and linear models using scikit-learn pipelines. These algorithms address the most common predictive business problems.

Lesson 3 • Machine Learning Workflow Overview

Understand the end-to-end ML pipeline from data prep to model deployment. A clear workflow prevents common mistakes in feature engineering and evaluation.

Lesson 1 • Row-Level Security and Access Control

Define RLS roles in DAX and assign users to restrict data visibility by role. Security rules ensure each user sees only the data they are authorized to access.

Lesson 2 • Automating Workflows with Power Automate

Trigger Power Automate flows from Power BI alerts and data-driven conditions. Automation reduces manual monitoring and accelerates response to business events.

Lesson 3 • Data Lineage and Documentation

Use the lineage view and endorsement features to document dataset provenance. Clear lineage reduces errors when multiple teams share and build on the same datasets.