This training is designed for people who want to do AI and Machine Learning (ML) inside Power BI. In this training, you will learn different ways to use Cognitive services, easy to use AI, AI in data preparation, and R languages for the aim of machine learning, visualization, data cleaning in Power BI.
In this two-days training, you will learn some main concepts of machine learning. You will learn some basics of AI and Machine Learning and How to use it in Power BI visualization, Power Query (data wrangler part of Power BI), and how to use it for creating custom visual. Also, the training will cover some of the main algorithms for machine learning such as Predictive analytics (Decision-Tree, Decision Forest, KNN, SVM), Descriptive analysis (Clustering, Market Basket Analysis) and Forecasting (Time Series). Finally, the pre-build advanced analytics visual in Power BI Marketplace will be explained and how to use them will be clarified.
At the end of this training, you will be able to use AI and Machine learning in your day to day Power BI solution and get the Power BI report to the next level with this skill.
Who this training is for?
Anyone who is building Power BI reports, dashboards and solutions to solve reporting and analytical challenges. If you are business analysts in the finance or HR team, or a developer in the BI team, or a data analysts who have been tasked to do Power BI report, or someone who wants to change their career path towards the realm of Power BI, this training is for you. All the learnings from this course will help you build and enrich solutions straight away after the course.
The Delivery method
The course is full of hands-on examples. You are expected to bring your laptop with Power BI Desktop installed on it. We will go through each example together, talk about what is the challenge we are trying to solve, what are the ways to solve it, what is the best method, and how to solve it using that method. This is not a lecture-only course. All the learnings are fully practical and through live examples.
What will you receive after completing the course?
All attendees will have access to all the materials of the course, all the datasets, Power BI sample files, handouts, etc. You will get a certificate of completion. You will have the chance to meet Reza and Leila through three days and ask whatever questions you have, even if the topic is outside of the course subject. And most importantly; you will leave the course knowing how to face analytical and reporting challenges and solve them using Power BI in a practical way.
The training includes but not limited to the following topics.
- Introduction to AI in Power BI
- Machine Learning Concepts
- AI Visuals in Power BI
- AI/ML in Power BI Desktop
- AI/ML in Dataflow
- Introduction to R
- Use R in Power BI Report for Visualization
- Use R in Power Query
- Predictive Analytics in Power BI
- Descriptive Analytics in Power BI
- Time series with R in Power BI
1: Introduction to AI in Power BI
Using AI is a norm this day in developing applications and analyzing the data. Easy AI is not a dream anymore, that means everyone, regardless of how they are familiar with AI and ML concepts, can use AI in their analysis. The audience will learn different options fo the AI and ML for the different roles.
- AI/ML for Analyst
- AI/ML for Business Intelligence developer
- AI/ML for Data Scientist
2: Machine Learning Concepts
The audience will get familiar with AI and ML concepts and what architecture and opportunity they have. Some introduction to ML approaches from predictive, descriptive and prescriptive analytics will be provided. The audience also gets familiar with prebuild and Custom AI tools in Microsoft stacks such as cognitive service and Azure Ml Service.
In this module audience will learn:
- What is Descriptive Analytics
- What is Predictive Analytics
- What is Forecasting
- What Languages exist for Machine Learning and what is the main difference
3: AI Visuals in Power BI
In this section an introduction to the AI and Machine Learning (ML) visuals in Power BI will be provided, audience will learn the main concepts and the history of using AI and ML in Power BI desktop. Audience will learn what is the use case scenario of each visual, and what type of analytical questions can be answered using that visual, and how to understand the details and configurations of that visual.
- Introduction to AI and Machine Learning
- Key Influencer Visual
- Q&A Visual
- Decomposition Tree Visual
4: AI/ML in Power BI Desktop
The audience get familiar with No code and Low code approach for using AI/ML in Power Desktop
- Text Analytics for Premium capacity
- Text Analytics with Power Query and Cognitive Service
- Azure Machine Learning integration for Premium capacity
- Other cognitive services like anomaly detection in Power BI Desktop
5: AI/ML in Dataflow
Doing machine learning in Power BI Service is a new feature in Power BI. DatafFlow is a power query editor in power BI service that allows the user to import data from some cloud service and on-premises such as Azure Data Lake, Azure SQL, SQL Server, CSV and so forth. There is a possibility to clean the data using some Power Query features as well. Now, there is a possibility to apply some prebuilt AI and custom AI on data in Dataflow. In this session, the process of how to apply text analytics APIs (Cognitive service) such as sentiment analysis, keyword extraction and language detection will be presented. Moreover, the process of how to use our custom AI model in Azure ML in data flow will be demonstrated
6: Introduction to R
R is a statistical language that has been used for many years for the aim of machine learning, statistical analytics, data wrangling, data visualization and so forth. There is a possibility to embed R codes inside Power BI to create more smart applications. In this module, we will go through the basics of R language and introduce some of the main R functions and commands such as statistical summary, package concepts, read data from SQL Server, Azure and so forth, visualization command, loop, and so forth. You will see some demos and introduction about:
- Introduction to R Language: What is R?
- RStudio; The First Experience
- Install RStudio
- RStudio Environment
- R basic Command
- Data Structure such as Data Frame, Vector, and Lists
- Import Data from the local machine, SQL Server, Azure Data Lake and so forth.
- Check the Statistical Summary of data
- Check the Structure Summary of Data
- Use existing Package and how to Install New Packages
- Create Basic Chart such as Histogram and Box Plot for better Understanding data
- And some other useful commands
7: Use R in Power BI Report for Visualization
Power BI consists of different components such as Power BI visualization, Power Query, Power BI services and so forth. Power BI visualization has lots of interesting and useful chart to visual data and creates business reports. However, customer need can be varied; each customer may need different visual that not available in Power BI visualization pan. In this module
- How to set up R in Power BI visualization will be explained
- How to use R visual in Power BI will be shown
- R editor in Power BI will be explained, and all feature such as setting, how to debug code in RStudio, and how to run will be explained.
- How to draw some basic charts like Histogram, Boxplot, table (gride) will be shown.
- The audience will see how to use the ggplot2 package for drawing the more complex visualization
- They will learn how to create a table chart with four, and five dimensions
- The audience will learn how to create different bar chart, polar chart by writing simple R codes
- How to create a colorful chart in Power BI using R will be explained
8: Use R in Power Query
Power Query is one of the main important components in Power BI that is used for data cleaning and wrangling. Power Query is a comprehensive component for extracting data from different locations, clean the data and load it (ETL). The main language behind the scene of Power Query is M. In this module, you will see how we can use R scripts in Power query for storing data in local machine or other devices, creating loops, normalizing the data, and Machine Learning.
The content that you will learn in this module includes but not limited to;
- How to access the R editor and how to use it
- How to store the dataset from Power Query in a local machine
- How to access the R editor in Power query and how to run the R scripts
- How to use R to apply a loop on a dataset/column
- How to expand the regular expression language
- How to access the R codes in Power query advanced editor
9: Predictive Analytics in Power BI
Some explanation on what predictive analytics is will be provided. The main algorithms for classification and regression will be introduced. The main concepts of some algorithms for classification and regression such as Decision tree, KNN, linear and non-linear regression will be explained.
The content that you will learn in this module includes but not limited to;
- What is predictive analytics and what algorithms we use for predictive analytics
- Main concepts for the decision tree, regression, and KNN will be provided
- How to use decision tree algorithms (with related R codes) for the aim of classification in Power Query will be explained
- How to use regression algorithm for predicting a value in power Query will be shown
- How to parametrize the algorithm in Power Query for future use will be explained
10: Descriptive Analytics in Power BI
In this module, a brief explanation of what is descriptive analytics, what is clustering, what is market basket analytics will be provided. Audience will learn
- Basic concepts for clustering and market basket analytics
- How to write R scripts and what function has been used
- How to do Clustering in Power BI visualization and Power Query
11: Time series with R in Power BI
Forecasting is a popular and useful trend in many industries. Time series is an algorithm that has been used for forecasting the trend, pattern and future value for their sales, profit and so forth. In this module below item will be presented
- What is Time series and what are the main concepts behind it
- How to write R scripts for time series decomposition, forecasting using exponential smoothing and so forth.
- How to do forecasting in Power BI report and Power Query