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

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Tags: Complete, Power, Bi, Course, Augmented

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