Course Title: Power BI and Analytics
Length: 2 days
Prerequisite: This course is a beginner to advanced level course. You don’t need any prerequisites for attending in this course.
Instructor: Dr. Leila Etaati
You’ve heard about the machine learning and R recently. R become a language for data analysis and machine learning. R also can be used for visualization widely. Microsoft power BI as a self-service BI tool helps people to easily extract data from different sources, apply data transformation and do data wrangling, and finally load and visual data in an appropriate way. Recently, Microsoft enabled users to use R codes and visuals inside the power BI which is very beneficial for Data Scientists, Analysts, and BI developers.
At this training, you will learn:
Day 1. Basics of R and R Visualization inside Power BI, and Basics of Machine Learning
In the first day, you will be familiar with the R language basics, the important of R packages for data cleaning, data manipulation, and data visualization. Also, you will learn how to embeded some of the R visualizations in Power BI. At the end of the day we will move to machine learning component. This day includes, but not limited to contents below;
- Installing R studio, and setting up R in power BI,
- Understanding main data structures in R such as Data frame, Vector and List.
- Exploring and Understanding Data in R (summary, str, head, tail, so forth).
- Learning the main concepts of basic statistics. Learning how they can be helpful, statistics operations like: mean, median, standard division, and so forth will be discussed.
- Getting familiar with the main packages like dplyer for data cleaning and manipulating. Functions like filter() for Comparisons, Logical operators, Missing values. Or function “select”.
- Learning how to use some of the main packages in R such as ggplot2 to visualize data. Learning how to visualise data with aim of data comparison (among items and over times), relationship between variables (two or more variables), data distributions (few data point, large data, two or three variables), and data composition (static or changing over time).
- Creating a ggplot, Aesthetic mappings, Facets, Geometric objects, Statistical transformations, Position adjustments, Coordinate systems.
- How to create R visuals, and interactive R visual in Power BI.
- How to use R for drawing maps and embedded charts in it
- Draw 5 dimension in single chart
- Introduction to Machine Learning process and concepts.
- How to write R codes in Power BI for transformation, and also creating new queries
- How to write Simple R code for correlation analysis in R
- How to create R visuals in Power BI
- Do data transformation in Power BI with R functions
- How to create interactive R reports in Power BI
- How classification algorithm like KNN works. and its relevant code in R.
- KNN concepts
- Write the KNN in R code
- How to evaluate the result
- How to improve the result by changing related parameters
Day 2. Machine Learning inside Power BI
The main aim of days 2 is to learn some of the main machine learning algorithms, and understand how they work, and how they can solve different type of real life problems. You will be familiar with these algorithms and their syntax in R, and how to use them in Power BI. Examples of Predictive analytics, Descriptive analytics, and prescriptive analytics will be covered in this day of training. This day includes, but not limited to contents below;
- Decision Tree concepts and its relevant R codes
- What is decision tree, the concepts and how it works.
- Introduction to different type of decision tree packages that are more common such as rpart and C5.
- How to change some of paraments of Rpart and C5, how to draw and customize a decision tree
- Learn how to evaluate result and improve the model.
- See how to have a decision tree visualization in Power BI.
- How to use decision tree in Power BI for prediction
- Associative Rules
- Learning the main concepts behind the Associative rules.
- Where to use it, and how it brings insight for users.
- Writing Associative rule codes in R.
- Learning concepts such as support, confidence and Lift.
- Learning how Apriori packages and its algorithm works.
- How to evaluate the association rules result.
- How to display it in Power BI.
- How to improve the results.
- How to use power BI visualization to better show the associative rules
- Neural Network algorithm
- The concepts behind it and how it works.
- The ksvm() function.
- Evaluate the model and how to improve the performance.
- Regression algorithm
- Concept behind the regression.
- Predict numeric data (e.g. using linear regression) the function used to do the linear regression.
- How to create a model that support the nonlinear models.
- Multiple regression, logistic regression and so forth.
- How to evaluate the results.
- Recommendation (content-based filtering and collaborative filtering).
- What is recommendation, and what is content filtering and collaborating filtering.
- How to implement content filtering and collaborative filtering using R.
- Example of recommending new items to a customer. First Using clustering algorithms (K-mean) for clustering the current customers based on their purchased behaviour. Then employing a multiple classification algorithm such as KNN to predict a new customer cluster, which lead to recommend the items to him.
Instructor: Dr. Leila Etaati
Dr. Leila Etaati gained her PhD in University of Auckland. She is world well-known speaker in Machine Learning and Analytics topics, and spoke in world’s best international conferences in Data Platform topics, such as; PASS Summits, PASS Rally, SQL Nexus, Microsoft Ignite, and so on. She has more than 10 years experience in Data Mining and Analytics. She is also Microsoft Most Valuable Professional (MVP) because of her dedication on Microsoft Analytics and Machine Learning technologies. She writes blog posts in RADACAD and also publishes YouTube videos in our channel. She also is an invited lecturer in universities such as University of Auckland, and Unitec, and some other universities. She worked in many industries including banking financial, power and utility, manufacturing, tourism, and so on.
For group discounts of 3+ people from same company, please contact us.
Cancellation up to 5 weeks before the event: fully refund minus administration fee ($50) and credit-card processing fees (if applicable).
Cancellation from 5 weeks to 2 weeks before the event: 50% cancellation charge, 50% refund
Cancellation from 2 weeks before the event: 100% cancellation charge, 0% refunded.
In case that cancellation happens from RADACAD you will be offered to choose either full refund or transfer to another date.
Transfer fee to another event date* (up to 2 weeks before event): Transfer fee $125
Transfer fee to another event date* (from 2 weeks to 1 day before event): Transfer fee $400
Transfer fee at the day of event*: US$650
*transfer can be done only once, and it can be only transferred to another date not later than 6 months from the original event.
No fee will be refunded for no show.