Analytics with R, Microsoft SQL Server, Power BI, and Azure Machine Learning
Length: 3 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 keep hearing about the machine learning and R recently. R become a language for data analysis and machine learning that makes the need for learning it more clearly. Moreover, Microsoft introduces R server as a comprehensive platform for using machine learning and R functionality inside the data analysis tools such as SQL Server 2016 and Power BI.
At this training, you will learn:
Day 1: Azure ML, R Basics
In the first day, you will be familiar with the basic concepts of Machine Learning in Azure ML and some basic concepts of R in R Studio and Microsoft Visual studio 2015. In Day 1 you will learn;
- The main functionality of Azure ML and how to set it up.
- Login in Azure ML, create experiment in Azure ML
- Different components in Azure environments
- Data import and Export into Azure studio in “Dataset” component using “Import Data”, “Export Data”, “Enter Data Manually”.
- Using “Reader” component to fetch data from cloud services
- Data format conversion such as “Convert to CSV”, “Convert to Dataset”
- Data Transformation learning with counts” Creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets”.
- Manipulation: Add columns and rows, Apply SQL Transformation, Clean Missing Data, Join Data, Remove Duplicate Row, Select Column in Dataset.
- Split and Sample: Partition and Sample and Split Data.
- Scale and reduce: Group Data in Bins and Normalize Data.
- Feature Selection: for predictive analytics
- Write R scripts in Azure ML using” Execute R”
- Using “Time series Anomaly Detection” Component to detect anomalies in the input time series data.
- How to work with different algorithms in Azure ML and how to use them for predictive, descriptive and prescriptive analysis, such as
- Classifications algorithms such as “Multiclass Decision Forest”,” Multiclass Neural Network”, “Two-class Boosted Decision Tree”.
- Clustering k-means Clustering
- Regression algorithms: Linear Regression, Bayesian Linear Regression.
- How to evaluate created model and analysis the evaluation output using evaluate model component. And how to enhance the model accuracy via cross validation and feature selection
- How to create a API from Created model and how to call the created API in Excel.
- Suggestions for possible architecture when using Azure ML.
- Practice to create a simple ML Experiment in Azure ML.
- The basic of R language.
- Installing R studio, setting up R server in SQL Server, setting up R in power BI,
- Main data structure in R such as Data frame, Vector and List.
- Exploring and Understanding Data in R
- Exploring numeric variable, categorical variable and relationship between variables, using summary and STR function
- How to use some of the main packages in R such as ggplot2 to visualize data.
- Creating a ggplot, Aesthetic mappings, Facets, Geometric objects, Statistical transformations, Position adjustments, Coordinate systems,
- Data Transformation with dplyer
- Functions like filter() for Comparisons, Logical operators, Missing values. Or function “select”
- sqldf to use SQL Statements for query dataset, RODBC for fetching data from SQL database
- The main concepts of basic statistics and how they can be helpful like: mean, median, standard division, and so forth will be discussed.
- Practice in R and using some packages
Day 2: R algorithms and Power BI
The main aim of days 2 is to learn some of main machine learning algorithms, and understand how they work and how they can solve different type of real life problems. You will familiar with these algorithms and their syntax in R, and how to use them in Power BI. Moreover, you will learn how to embed some R visualization in Power BI.
In day 2 you will learn;
- 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
- Decision Tree concepts and its relevant R codes
- What is decision tree, the concepts and how it works
- Introduce different type of decision tree algorithms that are more common.
- Introduce Rpart packages, how to write the related R code in Rstudio and Rvisual studio
- See the Rpart package in Power BI visual
- Package C50 for decision tree how to evaluate result and how to improve the model
- Associative Rules
- Audience will learn the main concepts behid the Associative rules
- Where to use it and how it brings insight for users
- They will learn to write the code in R
- Become familiar with concepts such as support, confidence and Lift.
- Learn how Apriori packages and algorithm works
- How to evaluate the association rules result
- How to display it in Power BI
- How to improve the results
- How to write R codes in Power BI for transformation, and also creating new queries
- How to set up the R in Power BI
- 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 to call azure ML API into Power BI
- Shows how to create functions in Power BI to call the API from Azure ML
- Create a prediction model in Power BI and show the result in visualization.
Day 3: R algorithms and SQL Server 2016
In third day, you will learn how to bring analytics and intelligence using SQL server 2016. Moreover, you will also learn some other important machine learning algorithms.
In Day 3 you will learn:
- Neural Network algorithm
- The concepts behind it and how it works
- The ksvm() function
- Evaluate the model and how to improve the performance
- Time series algorithm
- Time series concept difference between seasonality and trend
- The function that is used to create the time series model
- How to improve and evaluate it
- 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 classification and clustering algorithms to create a recommendation system
- Azure ML recommendation model
Moreover, you will learn how to use machine learning in SQL Server 2016 and in SSRS.
- Set up SQL Server to run the R scripts
- The best practice for implementing a prediction process in SQL Server 2016
- Create a correlation analysis in SQL Server
- Create a decision tree diagram in SSRS via writing code in R
- See an end to end predictive analysis code from getting data, create models, evaluate models and publish the result.
- Practice: creating a predictive solution in SQL Server 2016
This course is full of hands on labs, and you will experiment all examples through real-world demos. At the end of the 3-day training course, you will be able to use techniques and concepts of this training in your Analytics challenges.
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.
What others say about the training and trainer
Kenny McMillan, Sports Physiologist / Data Analyst, Frankfurt, Germany:
I attended RADACADs “Advanced Analytics” course recently in Frankfurt in May 2017. Being a regular user of Power BI (with a science background ) the course was extremely helpful in showing me how to incorporate R data visualisations into Power BI dashboards and for introducing me to machine learning using the Microsoft ML Studio. Leila is an excellent and extremely knowledgeable instructor and explained complex data analytical concepts and methodologies in an easy-to-understand manner. I thoroughly recommend this course to anyone who wants to expand their data analytical skills and knowledge.
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.
Transfer fee to another event date* (up to 2 weeks before event): 25% of the standard price of the event to transfer
Transfer fee to another event date* (from 2 weeks to 1 day before event): 40% of the standard price of the event to transfer
Transfer fee at the day of event*: 60% of the standard price of the event to transfer
*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.