Complete Power Bi Course With Augmented Analytics & Auto Ml

Data Transformation | DAX | Data Models | Simple, Complex & AI Visuals | Forecasting, Anomaly & Sentiment Analytics

What you'll learn

Power BI - Basic & Advanced

How to use DAX

How to create and publish visuals using Power BI

Machine Learning Concepts

How to build AI - ML Models without writing a single line of code

How to transform data

How to build a word cloud without any coding

What is autoML

How to join tables

How to identify anomalies/outliers in your dataset

How to restrict the access of your reports and dashboards

Requirements

None.

Knowledge of excel would be advantageous

Description

Recent Updates:Aug 2022: Added a video lecture on Clustering and SegmentationJuly 2022: Updated the course with using DAX functions like Calculate and LookupJune 2022: Added a case study on using Python (programming language) in PowerBI environmentMay 2022: Added a case study on Benford Law (Benford law is used to detect fraud)April 2022: Added a case study on Ageing Analysis–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––Course Description:In the last 50 years, the world of reporting, analytics and business intelligence (BI) has seen many of evolutions. The notable ones are the rise of self service BI and augmented analytics. Businesses are no longer content with descriptive or diagnostic analytics. The expectation for prescriptive and predictive analytics has become the new normal. Machine learning and Artificial Intelligence technology has also evolved significantly in the last decade and the notable evolution is the rise of Auto ML - the no code machine learning approaches. Auto ML has significantly democratized predictive analytics. End users can predict the future outcomes of businesses in a few (mouse) clicks.Power BI epitomizes the recent trends in business intelligence (BI) and augmented analytics for interactive, easy to use and self-serve dashboards & auto ML capabilities.This is a comprehensive course on Power BI covering the following:Data TransformationDAXData ModelsSimple & complex visualsAI enabled visualsAuto ML: Concepts and no code approaches to forecast futureThere are no pre-requisites for this course, although knowledge of excel would be advantageous.There are actually 2 courses (Power BI and AI - ML) in this course and both are covered in great detail. Whether your objective is to learn power bi or machine learning or both, this course will deliver the goods for you.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Descriptive Vs Diagnostics Vs Prescriptive Vs Predictive Analytics

Lecture 3 Traditional BI Vs Self Service BI Vs Augmented BI

Lecture 4 PowerBI

Lecture 5 What is Auto ML

Lecture 6 Assignment

Section 2: PowerBI Download and Understanding the tool

Lecture 7 Power BI Download

Lecture 8 Introducing the Power BI tool

Section 3: Loading and Transforming data

Lecture 9 Loading the data

Lecture 10 Data Transformation 1

Lecture 11 Data Transformation 2: Null, Merging columns, Extracting info, Groupby

Lecture 12 Data Transformation 3: Null, Pivot & Unpivot

Section 4: Visualization in PowerBI

Lecture 13 Text Box

Lecture 14 Card Visual

Lecture 15 Stacked Column Chart

Lecture 16 Stacked Bar Chart

Lecture 17 Multi Row Card

Lecture 18 Tree Map

Lecture 19 Pie Chart

Lecture 20 Dual Axis Chart (Line and Stacked Column Chart)

Lecture 21 Ribbon Chart

Lecture 22 Slicer

Lecture 23 Map Visual

Lecture 24 Page Tool Tip

Lecture 25 Funnel Chart

Section 5: DAX

Lecture 26 Introduction to DAX

Lecture 27 Understanding different types of DAX functions

Lecture 28 Gage Chart

Lecture 29 Conditional Column

Lecture 30 Format Date Columns and Use of Max

Lecture 31 Year to date (YTD)

Lecture 32 Calculate

Lecture 33 Lookup

Section 6: AI Enabled Visuals

Lecture 34 What If Analysis

Lecture 35 Key Influencers

Lecture 36 Q and A

Section 7: Tables: Relationships and Joining Tables

Lecture 37 Introduction and 1 to 1 Relationships

Lecture 38 1 to Many Relationships

Lecture 39 Different types of Join

Section 8: Publishing the report

Lecture 40 Publishing the report

Section 9: Row Level Security

Lecture 41 Row Level Security

Section 10: Case Studies

Lecture 42 Benford Law (Useful in fraud detection)

Lecture 43 Ageing Analysis

Lecture 44 Using Python in PowerBI environment and creating conditional scatter plots

Section 11: AI Concepts

Lecture 45 Dependent Vs Independent Variable

Lecture 46 ML Concepts

Lecture 47 Accuracy in Regression and Classification

Lecture 48 Regression and Classification Concepts

Section 12: Regression and Classification in Power BI

Lecture 49 Simple Linear Regression in PowerBI

Lecture 50 Multiple Linear Regression in Power BI

Lecture 51 Classification in Power BI

Section 13: Time Series Forecasting and Anomaly Detection Using Power BI

Lecture 52 Forecasting and Anomalies Concepts

Lecture 53 Time Series Forecasting Using Power BI

Lecture 54 Anomaly Detection Using Power BI

Section 14: NLP

Lecture 55 What is NLP (Natural Language Processing)

Lecture 56 NLP Concepts

Lecture 57 Wordcloud Using Power BI

Section 15: Clusters & Magic Quadrant in PowerBI: Unsupervised Learning: Customer Segments

Lecture 58 Clusters & Magic Quadrant in PowerBI: Unsupervised Learning: Customer Segments

Students,Experienced professionals,Those interested in building Business Intelligence Visuals & Dashboards,Machine Learning enthusiasts,Those who are curious whether it is possible to build ML model without coding,Executives who want to learn AI in more detail but don't want to do coding