Advanced Power BI

Get Started. It's Free
or sign up with your email address
Advanced Power BI by Mind Map: Advanced Power BI

1. Advanced Dashboard Scenario

1.1. Step #0: Design

1.1.1. Design Concepts

1.1.1.1. Dashboard Metrics

1.1.1.1.1. OKR Examples: OKR Format

1.1.1.1.2. Klancar: An Excellent Beginner's Guide To Information Architecture

1.1.1.1.3. OKR Examples: Metric Choices

1.1.1.1.4. Tibco: What is Data Science

1.1.1.2. Dashboard Types

1.1.1.2.1. Datapine: Make Sure You Know The Difference Between Strategic, Analytical, Operational And Tactical Dashboards

1.1.1.3. Dashboard Guides

1.1.1.3.1. Logi Analytics: Dashboard Design Fundamentals

1.1.1.3.2. UX Planet: 10 Rules for Better Dashboard Design

1.1.1.3.3. Information is Beautiful: What Makes a Good Visualisation?

1.1.1.3.4. Juice Analytics: A Guide to Creating Dashboards People Love to Use

1.1.1.3.5. Few: Why Do Most Dashboards Fail?

1.1.2. Design Questions

1.1.2.1. Agile Analytics - HR

1.1.3. Design Tasks

1.1.3.1. EnterpriseDNA: Power BI Data Model For Advanced Scenario Analysis Using DAX

1.2. Step #1: Query

1.2.1. Query Concepts

1.2.1.1. Microsoft: Data Refresh in Power BI

1.2.1.2. Microsoft: Managing Datasets

1.2.1.3. Microsoft: Big Data Architectural Patterns

1.2.1.4. Microsoft: Excel and Power BI

1.2.2. Query Tasks

1.2.2.1. Microsoft: Import Mode

1.2.3. Query Questions

1.2.3.1. Microsoft: Connecting to Data with Power BI Online

1.2.4. Additional Query Tasks

1.2.4.1. Exchange Rates API

1.2.4.2. Microsoft: Dealing with Records and Tables in Queries

1.2.4.3. WHO Health Indicators Database

1.2.4.4. WHO ODATA Service

1.2.4.5. Microsoft: Folder Connector

1.3. Step #2: Wrangle

1.3.1. Wrangle Concepts

1.3.1.1. W3: Data Normalisation

1.3.1.2. Trifacta: What is Data Wrangling?

1.3.1.3. Talend: What is Data Preparation?

1.3.2. Wrangle Tasks

1.3.2.1. Microsoft: Power Query Editor

1.3.3. Additional Wrangle Tasks

1.3.3.1. Microsoft: Wrangling Guide

1.3.3.2. GII: Global Innovation Index

1.3.3.3. Microsoft: M Query Operators

1.3.3.4. Radcad: Basics of M Query

1.3.3.5. Microsoft: Power Query M Guide

1.3.3.6. Radacad: M Query or DAX?

1.3.4. Wrangle Questions

1.3.4.1. Microsoft: Managing Data Streams with Date Filters

1.4. Step #3: Model

1.4.1. Model Concepts

1.4.1.1. Radacad: Best Practice

1.4.1.2. Medium: 10 Best Database Design Practices

1.4.1.3. Talend: Data Model Design and Best Practices

1.4.1.4. Microsoft: Star Schema Design

1.4.1.5. Microsoft: Understand Star Schema and the Importance for Power BI

1.4.1.6. IBM: Setting Keys

1.4.1.7. Xplenty: 6 Database Schema Designs and How to Use Them

1.4.2. Model Tasks

1.4.2.1. Microsoft: Create and Manage Relationships in Power BI Desktop

1.4.2.2. Microsoft: Model Relationships in Power BI Desktop

1.4.3. Additional Model Tasks

1.4.3.1. Microsoft: Merge and Append

1.4.3.2. Radacad: Merge and Append in Power BI

1.4.4. Model Questions

1.4.5. More Model Questions

1.5. Step #4: Measures

1.5.1. Measures Concepts

1.5.1.1. Microsoft: Measures in Power BI

1.5.1.2. Microsoft: DAX Cheatsheet

1.5.2. Metrics Tasks

1.5.3. Additional Measures Tasks

1.6. Step #5: Visualise

1.6.1. Visualise Concepts

1.6.1.1. SQL BI: Power BI Visualisations Reference

1.6.1.2. UX Matters: 10 Rules for Better Dashboard Design

1.6.1.3. National Geographic: The Underappreciated Man Behind the “Best Graphic Ever Produced”

1.6.1.4. M Bounthavong: Communicating Data Effectively with Data Visualizations

1.6.1.5. Interaction Design: Guidelines for Good Visual Information Representations

1.6.1.6. UX Design: The Practicality of Nielsen’s 10 Usability Heuristics

1.6.1.7. IDEO: Design Kit

1.6.2. Visualise Tasks

1.6.3. Additional Visualise Tasks

1.6.3.1. Radacad: Change the Column or Measure Value in a Power BI Visual by Selection of the Slicer: Parameter Table Pattern

1.6.3.2. Four Moo: Using Slicers to Change Measures

1.6.3.3. Absent Data: WhatIf Parameter

1.6.3.4. Microsoft: Swap Axis by Slicer

1.7. Step #6: Publish

1.7.1. Publish Concepts

1.7.1.1. Microsoft: Power BI Dashboards

1.7.1.2. Roman Pilcher: What is Product Management

1.7.1.3. Microsoft: Publish from Power BI Desktop

1.7.1.4. Microsoft: Apps in Power BI

1.7.1.5. Microsoft: Share in Power BI

1.7.1.6. Microsoft: Share in Power BI

1.7.1.7. Radacad: Power BI Sharing Comparison

1.7.2. Publish Tasks

1.7.2.1. Microsoft: Collaborating on Assets

1.7.2.2. Microsoft: Sharing Assets

1.8. Step #7: Manage

1.8.1. Manage Concepts

1.8.1.1. Pilcher: What is Product Management?

1.8.1.2. Medium: Venture Design Framework

1.8.1.3. SAP: Product Lifecycle Management

1.8.2. Management Tasks

1.8.2.1. Microsoft: Row Level Security

1.8.2.2. Microsoft: Power BI Datasets

2. Predictive Analytics

2.1. Concept: Time Series Forecasts

2.1.1. Microsoft: Forecasting Tool

2.1.2. ESRI: Time Series Forecasting 101

2.1.3. Towards Data Science: The Complete Guide to Time Series Analysis and Forecasting

2.1.4. Machine Learning Mastery: What Is Time Series Forecasting?

2.1.5. How to Decompose Time Series Data into Trend and Seasonality

2.1.6. Trend, Seasonality, Moving Average, Auto Regressive Model : My Journey to Time Series Data with Interactive Code

2.1.7. Choosing The Right Forecasting Technique

2.1.8. What is the difference between a causal model and a time series model?

2.2. Task: Time Series Forecast

2.2.1. Creating Forward Forecasts in Power BI Using DAX

2.2.2. How To Create A Forward Forecast In Power BI: Advanced Forecasting Techniques

2.2.3. CORR Illustration

2.2.4. Deckler: Correlation, Seasonality and Forecasting

2.3. Supervised Machine Learning

2.3.1. Introduction to Machine Learning

2.3.1.1. Logistic Regression

2.3.1.2. Machine Learning Overview

2.3.1.3. Algorithms of Machine Learning

2.3.1.4. Oracle: Types of Machine Learning and Top 10 Algorithms Everyone Should Know

2.3.1.5. Explainable AI: The data scientists’ new challenge

2.3.2. Regression Examples

2.3.2.1. Deloitte Predictive Maintenance

2.3.2.2. Input-Output Prediction

2.3.2.3. SMU Default Prediction

2.3.3. Regression in DAX

2.3.3.1. XXLBI: Simple Linear Regression in DAX

2.3.3.2. Microsoft: Simple Linear Regression

2.3.3.3. Kaggle: Walmart Recruiting - Store Sales Forecasting

2.3.4. Machine Learning Process

2.3.4.1. What is Data Science?

2.3.4.2. Supervised Machine Learning

2.3.4.3. A Brief Overview of AutoML

2.3.4.4. A Quick Guide to How Machines Learn

2.3.4.5. Uber Michaelangelo

2.3.4.6. What is Supervised Learning?

2.3.4.7. Predicting Employee Attrition with Machine Learning

2.3.4.8. Dealing with Interaction Effects in Regression

2.3.4.9. Interaction Effect in Multiple Regression

2.3.4.10. Bias-Variance Tradeoff

2.3.4.11. The Case of Overfitting

2.3.4.12. Avoiding Overfitting - when we used all of our data to "train" the model, making too focussed on that data alone!

2.3.4.13. Supervised Learning Workflow

2.3.4.14. Supervised Learning Family

2.3.4.15. Machine Learning Model Selection

2.3.4.16. ML Model Performance Metrics

2.3.4.17. Ridge Regression

2.3.4.18. Data Standardisation

2.3.4.19. SMOTE

2.3.4.20. Indicio ML Workflow

2.3.5. KNIME and Alternatives

2.3.5.1. KNIME

2.3.5.2. Towards Data Science: Deep Learning with minimum Coding

2.3.5.3. Gartner Quadrant: Data Science and Machine Learning

2.3.5.4. Tableau Prep

2.3.5.5. Azure Data Factory

2.3.5.6. Alteryx

2.3.5.7. Rapidminer

2.3.5.8. EasyMorph

2.3.5.9. Power BI Dataflows

2.3.5.10. Apache Airflow

2.3.5.11. Apache Kafka]

2.3.5.12. AWS Kinesis

2.3.6. Random Forest

2.3.6.1. IBM Random Forest

2.3.6.2. Entropy and Information Gain

2.3.6.3. Random Forest Overview

2.3.6.4. R-Squared > 0.75

2.4. Tasks: Machine Learning

2.4.1. Absent Data: Python Machine Learning in Power BI

2.4.2. Reading and Writing to Databases

2.4.2.1. ...more...

2.4.2.1.1. Pipeline Challenge

2.4.3. Using the Power BI Integration Nodes

2.4.4. Using the KNIME Scheduler

2.4.5. Predicting Employee Attrition with Machine Learning

2.4.6. Regression in KNIME

2.4.7. Analytics Vidhya: Building your First Machine Learning Model using KNIME

2.5. hr

2.6. Machine Learning Questions

3. Enhanced Insight with DAX

3.1. Enhanced Visual Analytics Concepts

3.1.1. Visual Data Science

3.1.1.1. 10 rules for better dashboard design

3.1.1.2. Stephen Few: show insight, simplify, clarify, and generate density

3.1.1.3. How to Lie with Statistics: Stand Your Ground and Gun Deaths

3.1.1.4. Alberto Cairo: tell stories, enable the user to take their own path

3.1.1.5. John Snow's data journalism: the cholera map that changed the world

3.1.1.6. Edward Tufte: show the data, not the ducks, do not mislead...

3.1.1.7. Four Principal Challenges

3.1.2. T-Test

3.1.2.1. Apptimize: KAYAK’S Most Interesting A/B Test

3.1.2.2. Biology for Life: T-Test

3.1.2.3. p Value

3.1.3. Distribution Curves

3.1.3.1. Descriptive Statistics

3.1.3.2. Combating Coronavirus Misinformation

3.1.3.3. Journal of Accountancy: Using Benford’s Law to Detect Fraud

3.1.4. Relationships

3.1.4.1. Hypothesise the Model

3.1.4.2. Confirm the Model

3.1.4.3. Validate the Concept

3.1.4.4. Linearisation of Non-Linear Relationships

3.1.4.5. How to Linearise a Curved Data Plot

3.1.4.6. Types of Linear Relationship

3.1.4.7. A Brief History of the Scatter Plot - Data Visualization’s Greatest Invention

3.1.4.8. Scatter Plot

3.2. DAX Fundamentals Tasks

3.2.1. Create Calculated Tables in Power BI Desktop

3.2.2. Scenarios of Using Calculated Tables in Power BI

3.2.3. DAX Operators

3.2.4. Use Variables to Improve Your DAX Formulas

3.2.5. DAX Whitespace

3.2.6. EnterpriseDNA: Virtual Tables Inside Iterating Functions In Power BI

3.2.7. Radacad: Using Iterators in DAX

3.2.8. Deckler: For and While Loops in DAX

3.2.9. Microsoft: Model Relationships in Power BI Desktop

3.2.10. Microsoft: DAX Function Reference

3.2.11. DAX Guide

3.3. DAX Visual Analytics Tasks

3.3.1. Benford Test

3.3.2. T-Test Equation

3.3.2.1. ...key...

3.3.2.1.1. x = mean of group values

3.3.2.1.2. n = number of group values

3.3.2.1.3. s = standard deviation of group values

3.3.2.1.4. t = the t statistic

3.3.2.1.5. 1 and 2 = group labels

3.3.3. p Value

3.3.4. CORR Illustration

3.3.5. Gapminder World Poster

3.3.6. Simple Linear Regression in DAX

3.4. DAX Scripting Concepts

3.4.1. Pseudocode

3.4.1.1. Noteworthy: How to Write Pseudocode: A Beginner’s Guide

3.4.1.2. Viking Code School: The Elements of Pseudocode

3.4.1.3. TechGeekBuzz: How to Write Pseudocode?

3.4.1.4. Pseudocode 101

3.4.2. DAX Patterns

3.4.2.1. Microsoft: Use DAX in Power BI Desktop

3.4.2.2. Addend: DAX Functions Cheat Sheet

3.4.2.3. Microsoft Power BI Community

3.4.2.4. DAX Patterns

3.4.3. DAX Scripting Examples

3.4.3.1. Microsoft: Days of Supply

3.4.3.2. Microsoft: HR Analytics

3.5. DAX Scripting Tasks

3.5.1. Pseudocode Process

3.5.1.1. 1. State the Goal

3.5.1.2. 2. Initialise the Variables

3.5.1.3. 3. Scaffold a Linear or Iterator Format

3.5.1.4. 4. Identify the Statements

3.5.1.5. 5. Define the Sequence

3.5.1.6. 6. Incorporate the Statement Conditions

3.5.1.7. 7. Specify the Output Expression

3.5.1.8. 8. Reflect, Refine, Optimise

3.5.1.9. 9. Translate to DAX

3.5.2. Moving Average

3.5.2.1. Microsoft: Moving Average

3.5.2.2. Pestle Analysis: Time Series Analysis: Definition, Benefits, Models

3.5.2.3. Towards Data Science: Forecasting in Power BI

3.5.3. New and Returning Customers

3.5.3.1. New and Returning Customers

4. Text Analytics

4.1. Text Analytics Concepts

4.1.1. Sentiment Analysis

4.1.1.1. Sentiment Analysis: A Definitive Guide

4.1.1.2. What is Sentiment Analysis and How Does it Work?

4.1.2. Topic Modelling

4.1.2.1. Wikipedia on Topic Models

4.1.2.2. Topic Modeling: An Introduction

4.1.2.3. Topic Discovery and Clustering

4.1.3. Use Cases

4.1.3.1. Customer Care

4.1.3.2. Microsoft Using Sentiment Analysis to Predict Product Reviews

4.1.3.3. Trading Signals

4.1.3.4. Document and Contract Review

4.1.3.5. MonkeyLearn - Chewy Trustpilot Reviews

4.1.3.6. VADER Sentiment Analysis in Algorithmic Trading

4.1.3.7. Text Analytics: 5 Examples To Open Your Eyes To Your Own Opportunities

4.1.4. NLP

4.1.4.1. Lexalytics on NLP

4.1.4.2. Tibco on Text Mining

4.1.4.3. Data Science Central on NLP

4.1.4.4. Tibco on Text Analytics

4.2. Sentiment Analysis with VADER Tasks

4.2.1. Power BI and Python

4.2.1.1. Power BI and Python

4.2.1.2. Python in Power Query Editor

4.2.2. WinPython

4.2.2.1. Installing new packages for WinPython

4.2.3. VADER and Power BI

4.2.3.1. VADER Scoring Methodology

4.2.3.2. Sentiment Analysis in Power BI

4.2.3.3. Simplifying Sentiment Analysis using VADER in Python (on Social Media Text)

4.2.3.4. VADER Sentiment Analysis in Algorithmic Trading

4.2.4. Other Python Packages

4.2.4.1. Sentiment Analysis In Power BI

4.3. Modifying VADER Questions

4.4. Topic Modelling with jsLDA Tasks

4.5. KNIME Concepts

4.5.1. Modern Analytics Architecture with Azure Databricks

4.5.2. KNIME

4.5.3. Data Science Presents Tech Hurdles

4.5.4. The Emerging Big Data Architectural Pattern

4.5.5. Requirements and Limitations of Python Packages

4.5.6. Text Analytics in Microsoft Azure and Power BI

4.5.7. Choosing a natural language processing technology in Azure

4.5.8. Using the new KNIME Deep Learning - Keras Integration to Predict Cancer Type from Histopathology Slide Images

4.6. KNIME and Power BI Tasks

4.6.1. KNIME General

4.6.1.1. KNIME Interface

4.6.1.2. KNIME Example Workflows

4.6.1.3. KNIME Power BI Integration User Guide

4.6.2. LDA

4.6.2.1. Topic Modeling — LDA Mallet Implementation in Python — Part 1

4.6.2.2. Topic Modeling in Python: Latent Dirichlet Allocation (LDA)

4.6.2.3. Intuitive Guide to Latent Dirichlet Allocation

4.6.3. NLP in KNIME

4.6.3.1. Stanford Tagger: Tag Interpretation

4.6.3.2. IS Literature Mining with Topic Detection (LDA)

4.6.4. Topic Models in Python

4.6.4.1. Topic Modeling and Sentiment Analysis on Amazon Alexa Reviews

4.6.4.2. Topic Modeling and Latent Dirichlet Allocation (LDA) in Python

4.6.4.3. Topic Modeling in Power BI using PyCaret

4.7. Topic and Sentiment Questions

4.8. e856d1b8f83d4586ba558335a9ab9b7a

5. Course Materials

5.1. Map Link

5.1.1. Course Page

5.1.2. Copy the Map

5.2. Datasets Link

5.3. Tools

5.3.1. Download Power BI Desktop

5.3.2. Create a Power BI Online Account

5.3.3. Download WinPython

5.3.4. Download KNIME

6. Python Visualisation

6.1. The Four Types of Visual Insight

6.2. Matplotlib Library

6.2.1. Matplotlib Cheat Sheets

6.2.2. Matplotlib Library

6.3. Seaborn Library

6.3.1. Seaborn Cheatsheet

6.4. Python Preparation Tasks

6.4.1. MoonPoint: Installing new packages for WinPython

6.4.2. Microsoft: Power BI and Python

6.4.3. Microsoft: Requirements and Limitations of Python Packages

6.5. Python Visualisation Tasks

6.5.1. Microsoft: Create Power BI Visuals by Using Python

6.5.2. Absent Data: Using Python Visuals in Power BI

6.5.3. Datasets

6.5.3.1. Gapminder

6.5.3.2. US Covid Datasets

6.5.4. Seaborn

6.5.4.1. Pair Plot

6.5.4.2. Grouped Boxplots

6.5.4.3. Plotting with Categorical Data

6.5.4.4. Box Plot

6.5.5. Matplotlib

6.5.5.1. Scatter

6.5.5.2. Python Projects: Scatter Plot With Matplotlib

6.5.5.3. Sample Plots in Matplotlib

7. Foundational Patterns

7.1. Core Patterns

7.1.1. The Structure of Analytics Challenges

7.1.2. The Data Science Workflow

7.1.3. The Success Factors of Information Design

7.1.4. The Four Analytics Objectives

7.1.5. The Four Types of Visual Insight

7.1.6. The Product Management Framework

7.1.7. The Ten Red Flags of Failure

7.2. Scaling Machine Learning at Uber

7.2.1. Strategy

7.2.2. Teams

7.2.3. Science

7.2.4. Process