Power BI Desktop Introduction

Anyone can become a data analyst, using the Power BI tool.

Learning Basic Data Preparation and Data Cleaning:

Data Collection: Understanding how to gather and collect data from various sources, such as databases, spreadsheets, or external systems.
Data Cleaning: Learning how to identify and handle missing or inconsistent data, including techniques for data imputation and validation.
Data Transformation: Exploring methods to reshape and reformat data to make it suitable for analysis and visualisation. This includes tasks like pivoting, aggregating, and merging datasets.
Data Quality Assurance: Ensuring data accuracy and reliability by applying data validation rules and addressing anomalies.
Data Integration: Combining data from multiple sources to create a unified dataset for analysis and reporting.
Data Profiling: Profiling data to gain insights into its characteristics, such as data distributions, patterns, and outliers.

Using DAX Functions and Metrics for Dashboards:

DAX (Data Analysis Expressions): Learning the fundamentals of DAX, a powerful formula language used for data analysis in tools like Microsoft Power BI and Excel.
Creating Metrics: Understanding how to define and calculate key performance indicators (KPIs) and metrics relevant to your specific business or analytical objectives.
Aggregating Data: Using DAX functions to aggregate and summarise data, which is essential for creating meaningful visualisations in dashboards.
Time Intelligence Functions: Exploring DAX time intelligence functions to analyse data trends over time, such as year-to-date calculations and moving averages.
Data Modeling: Developing data models that optimize data relationships for dashboard creation, including defining hierarchies and relationships between tables.
Visualization: Designing and creating visually appealing and interactive dashboards using tools like Power BI, Tableau, or Excel. This includes selecting appropriate charts, graphs, and widgets for data representation.
Dashboard Interactivity: Implementing interactive features like slicers, filters, and drill-through functionality to allow users to explore and analyse data dynamically.
Performance Optimization: Optimising dashboard performance for efficient data loading and responsiveness, particularly when dealing with large datasets.

What is Power BI?
What are the benefits of Power BI?
Creating reports and dashboards with Microsoft Power BI
Importing Data
Datasets and connecting data
Reading and Editing Data
Creating graphs and visualisations
Managing Schema and Relating Tables

Power BI Business Intelligence

Creating calculations, metrics and KPI’s
Data Analysis Expressions
DAX Syntax
DAX Functions
Query Editing

Presenting using Power BI

Logical and Insightful Data Analytics Presentation
Rules and Limitations of Data
Calculated Columns
Shaping Data
Transferring your Data to the Cloud
Presenting in Cloud
Visualising Data