Data Analyst Training: Microsoft Power BI at QLS Level 7 Diploma

Free Course 01: Google Data Studio

Free Course 02: Business Intelligence and Data Mining

Be competent in the foundational concepts of Microsoft Power BI, from installation to interface navigation.

Develop proficiency in data transformation techniques using the Query Editor.

Acquire advanced data transformation skills, including dynamic data operations with the M formula language.

Comprehend the intricacies of creating robust and efficient data models with diverse relationships.

Formulate compelling visual representations of data, exploring visuals like pie charts, treemaps, and matrix visuals.

Integrate Python within Power BI, enabling advanced data manipulation and machine learning capabilities.

Narrate compelling data-driven stories, leveraging effective visualisation and storytelling techniques.

Grasp the essentials of DAX for powerful data operations, from basic aggregations to context manipulations with CALCULATE.

Navigate and optimise the Power BI Cloud Service, ensuring seamless data presentations and platform sharing.

Implement advanced security measures, understanding row-level security for safeguarding sensitive data.

Welcome!

What is Power BI?

Download & Installing Power BI Desktop

Getting to know the interface

Mini Project: Transform Data

Mini Project: Visualise Data

Mini Project: Creating a Data Model

Course Outline: What will you learn in this course?

How to learn best with this course?

Creating our initial project file

Working with the attached project files

Exploring the Query Editor

Connecting to our data source

Editing rows

Changing data types

Replacing values

Close & Apply

Connecting to a CSV file

Connecting to a web page

Extracting characters

Splitting & merging columns

Creating conditional columns

Creating columns from examples

Merging Queries

Pivoting & Unpivoting

Appending Queries

Practice & Solution: Population table

The Fact-Dimension-Model

Practice: Load the dimension table

Organising our queries in groups

Entering data manually

Creating an index column

Workflow & more transformations

Module summary

Exercise 1 - Instruction

Exercise Solution

Advanced Editor - Best practices

Performance: References vs. Duplicating

Performance: Enable / Disable Load & Report Refresh

Group by

Mathematical Operations

Run R Script

Using Parameters to transform data dynamically

M formula language: Basics

M formula language: Values, Lists & Tables

M formula language: Functions

M formula language: More functions & steps

Exercise 2 - Instructions

Exercise 2 - Solution

Understanding the relationship

Create & edit relationships

One-to-many & one-to-one relationship

Many-to-many (m:n) relationship

Cross filter direction

Activate & deactivate relationships

Model summary

Exercise 3 Create Model

Exercise 3 Solution

Our first visual

The format tab

Understanding tables

Conditional formatting

The Pie Chart

All about the filtered visual

The filter pane for developers

Cross-filtering & editing interactions

Syncing slicers across pages

Creating drill-downs

Creating drill throughs

The treemap visual

The decomposition tree

Understanding the matrix visual

Editing pages

Buttons & Actions

Bookmarks to customise your report

Analytics and Forecasts with line charts

Working with custom visuals

Get data using R Script & R Script visual

Asking questions - Q&A visual

Wrap up - data visualisation

Python in Power BI - Plan of attack

Setting up Python for Power BI

Transforming data using Python

Creating visualisations using Python

Violin plots, pair plots & ridge plots using Python

Machine learning (Bayes Text Analyser) using Python

Performance & Troubleshooting

Introduction

Show Empathy & Identify the Requirement

Finding the Most Suitable KPIs

Choose an Effective Visualisation

Make Use of Natural Reading Patterns

Tell a Story Using Visual Cues

Avoid Chaos & Group Information

Warp Up - Storytelling with Data

Introduction

The project data

Measures vs. Calculated Columns

Automatically creating a date table in DAX

CALENDAR

Creating a complete date table with features

Creating a key measurement table

Aggregation functions

The different versions of COUNT

SUMX - Row-based calculations

Calculate - The basics

Changing the context with FILTER

All

All SELECTED

All Except

How to go on now?

Power BI Pro vs Premium & Signing up

Exploring the interface

Discovering your workspace

Connecting Power BI Desktop & Cloud

Understanding datasets & reports

Working on reports

Updating reports from Power BI Desktop

Creating and working with workspaces

Installing & using a data gateway

Get Quick Insights

Creating dashboards

Sharing our results through Apps

Power BI Mobile App

Creating the layout for the Mobile App

Wrap up - Power BI Cloud

Introduction

Creating a Row-Level Security

Row-Level Security in the Cloud

Row-Level Security & Data Model

Dynamic Row-Level Security

Dynamic Many-to-Many RLS

Hierarchical Row-Level Security

JSON & REST API

Setting up a local MySQL database

Connecting to a MySQL database in Power BI

Connecting to a SQL database (PostgreSQL)

Congratulations & next steps

The End

Aspiring data analysts eager to kickstart or advance their careers in the data visualization realm.

Business professionals wanting to harness data-driven insights for strategic decision-making.

Researchers and academicians aiming to represent complex data sets in easily digestible visuals.

IT specialists looking to expand their skill set into the booming field of data analytics and visualization.

Companies and teams aiming to upskill their members for improved data-driven operations and reporting.

Data Analyst: £25,000 - £40,000

Software Engineer: £28,000 - £55,000

Marketing Manager: £30,000 - £50,000

Civil Engineer: £27,000 - £52,000

Graphic Designer: £21,000 - £37,000

Solicitor: £30,000 - £60,000