Course Title: Data Science and Advanced Analytics with Microsoft Technologies
From 1 to 7 days (Lecture + Labs): depends on the modules you enroll.
In-Person or Online: Check the schedule of upcoming courses.
Type of training:
Public or private (contact us for more details)
The well-known worldwide training in Microsoft Advanced Analytics field on the planet from one to seven days of training delivered by the well-known experts and MVPs, authors of books, and speakers of many conferences themselves. In this training course, you will learn some basic concepts for Machine Learning, Predictive and Descriptive analytics. You learn how to write R codes for the aim of data wrangling, data modeling, data visualization, and machine learning. Moreover, you will learn how to use custom AI tools like Azure Machine learning for creating your desire model, deploy it and use it as web service in other applications and scenarios like the Internet of Things (IoT). You will learn about how to use R in a dashboard, how to R in the cloud and on-premises storage. You will learn how to use pre-build AI tools like Bot and cognitive services to create smart report and applications. Expect learning best practices with great scenarios in this course. This course is designed in separate modules based on the type of audience. If you are a data scientist, data analyst, business intelligence developer, or data architect, this course has many things to teach you all.
This course is delivered to thousands of people all around the world, check out only a few of the recommendations at the bottom of this page, and check some of our clients.
Instructor: Dr. Leila Etaati
Our trainer is the world well-known name in the Microsoft Data Science field. Leila Etaati is Microsoft AI, and Data Platform (Most Valuable Professional) focused on AI and BI Microsoft technologies; Microsoft has awarded her MVP because of her dedication and expertise in Microsoft BI technologies from 2016 till now, she is one of two AI MVP in Australia and New Zealand. She is a speaker in world’s best and biggest Microsoft Data Platform, BI and Power BI, AI conferences such as Microsoft Ignite, Microsoft Business Applications Summit, Microsoft Data Insight Summit, PASS Summits, PASS Rallys, SQLBits, TechEds, and so on. She is the author of some books on this topic, and she has more than 11 years’ experience in the Microsoft BI technologies and Data science. Leila is the co-founder of RADACAD and a consultant for more than seven years.
Advanced Analytics with Microsoft Technologies
This is the most comprehensive course for Microsoft Advanced Analytics and Data Science on the planet which split into modules. You can enroll in any of these modules separately or take the whole course. Modules designed independently, which means each module can be taken regardless of the order of modules. Here are a list and detailed agenda of each module:
- Module 1: Power BI for Data Scientists (2 days)
- Module 2: Data Science with Microsoft Cloud (2-days)
- Module 3: Advanced Data Science with Microsoft Services (2-days)
- Module 4: AI and Cognitive Services in Applications (1-day)
Module 2: Data Science with Microsoft Cloud – 2 days course
This training is designed for data scientists, data developer or data architecture, who have the data in the cloud or thinking about using Microsoft machine learning tools in cloud scale. In this training, you will get familiar with machine learning cloud possibilities.
In this course, you will learn how to Azure ML Studio as the first tool for machine learning cloud that introduced in 2014. The detail on how to create a model, how to enhance machine learning algorithms, how to import data and so forth will be explained. Then, you will get familiar with Azure Data Science Virtual Machine as a comprehensive tool for machine learning, some of the tools in it like the Tensor flow and Azure ML workbench will be explained. Then, how to do machine learning in Azure Data Lake store will be explained. Finally, Azure Data Bricks and how to use it for the aim of machine learning will be explained too. This training has many hands-on labs, and all the required scenario will be explained step by step.
At the end of this training, the audience will learn how to define a machine learning problem and how to use Azure ML Studio for cloud machine learning, and also how to use Azure data Lake Analytics, Azure Data Bricks and so forth for machine learning.
The training includes but not limited to topics below:
2.1: Azure Machine Learning (ML) Studio
In this section, you will learn some basic of machine learning and how it works. Then some introduction to Azure ML Studio will be provided.
- Import data: The main component for importing data from local PC, how to import data from another workspace, how to import data from HTML website, how to import data from cloud such as Azure SQL DB.
- Data Cleaning: data cleaning is the main process that we should do before any machine learning process. I will explain the available component in Azure ML. How to clean missing values, remove duplicate data, select column, clip value (remove outliers), group data into bins, and create an indicator for data. You will learn how to normalize the data, how to use SQL Statement for data transformation, how to use enter data manually, how to edit the data type, change the column name with edit metadata component, how to join data from different data resource , how to increase the number of low incidence in a dataset which unbalanced.
- Feature Selection Data Sampling: the process of feature selection will be explained, how to split data, how to partition data using a sampling approach, how to create different folds for the aim of cross-validation.
- Models: a bit talk about the available models in Azure mL for predictive, descriptive, prescriptive and anomaly detection. An example of a prediction of a group, a value, clustering data and anomaly detection will be shown. For each scenario, an example will be presented; different algorithms will apply to a problem. The main concepts of k-mean, how to use elbow chart to identify the number of the cluster will be discussed, the PCA chart will be explained. The trained model for recommendation “match box recommender” will be explained through a scenario. The main concepts of collaborative filtering and content-based filtering and hybrid approach for recommendation will be discussed.
- Training and Scoring models: how to choose algorithms and how to train model and test model, what is cross-validation, how to do cross-validation to apply a model to different folds of data, how to check the different values for each parameter and see the related accuracy to that.
- Evaluate: how to evaluate a classification problem concepts of Accuracy, Recall, Precision, AUC will be discussed, the evaluation criteria for regression algorithm MAE, RMSE and so on will be shown. How to evaluate and see the result of more than three algorithms on one dataset will be shown. Using different evaluate the model, enter data manually component and add row component.
- Publish to Web: the process of creating a web service from a model will be discussed, how to check it in Excel will be shown, also how to use it in Stream Analytics as a function for data will be shown.
- Sharing workspace, create projects: the process of how to share the workspace with others will be discussed, how to create a project for each experiment, how to create a trained model component for reuse, and a data transformed dataset, how to export datasets to CSV, and other formats will be shown.
- Azure ML model in Power BI: Create a model in Azure ML Studio and use it in Power BI
- Azure ML Model in IoT: the process of how to create a model in Azure ML model studio and apply it on streamed data.
2.2: Data science Virtual Machine
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. It has much popular data science and other tools pre-installed and pre-configured to jump-start building intelligent applications for advanced analytics. It is available on Windows Server and Linux. In this session, the audience will learn
- How to set it up
- Environment needs
- What parameter needs to be set up
- What software and tools can be used
- visual studio and how to access R and Python
- Azure ML workbench
- SQL Server and Power BI
- Deep learning introduction
- Deep learning tools such as Tensor flow and so forth.
2.3: Azure Data Lake Analytics
Azure Data Lake Analytics is a distributed, cloud-based data processing architecture offered by Microsoft in the Azure cloud. It is an on-demand analytics job service that simplifies big data. Instead of deploying, configuring, and tuning hardware, you write queries to transform your data and extract valuable insights. The analytics service can handle jobs of any scale instantly by setting the dial for how much power you need. You only pay for your job when it is running, making it cost-effective. In this section, you will learn
- How to set up Azure Data Lake Store gen1 and Azure Data Lake Analytics
- Th main process of how to set them up will be explained
- How to access Azure Data Lake Analytics via visual studio also will be explained
- How to use an existing sample of U-SQL
- An introduction into USQL
- How to run USQL in the cloud and how to run it
- How to write R codes inside USQL
- How embed the R or Python code
- How to pass parameters and access the data
- What is the best practice to create a machine learning project in data Lake
- How to create a model
- Store model
- And use the model for production
2.4: Azure Data Bricks
Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. Azure data bricks are one of the main platforms for the aim machine learning in this part audience will get familiar with
- What is data bricks and how to set it up
- The environment of data bricks and explain how they work
- How to able to write R, Python, Scala, and SQL at the same time
- Visualization of Databricks
- Workspace and clusters
- How to get information from Azure Data Lake store
- How to get data from event Hub
- What is notebook
- How to pass data from one cell to other
- How to use Databricks for a machine learning scenario storage model in Azure Data lake and show the result in Power BI
Our Power BI in-person training will be held in high-quality venues with the recommendation for hotel bookings for attendees. There will be special group rating fee as well as early bird and past attendees discount. for the schedule of our in-person training follow this link:
We run online training with GoToWebinar and GoToTraining applications. These applications provide a highly reliable communication channel between instructor and attendees. For the schedule of our online training follow this link:
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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 visualizations 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.
Martin Catherall (Microsoft Data Platform MVP):
As part of SQL Saturday Auckland 2016 I attended an “Analytics with Power BI and R.” pre-con with Leila. Leila took the class’s knowledge from rudimentary to competent in a day. We first looked at R, the language and the software. Once these skills were obtained we started to look at Power BI and the integration that it has with R. We worked through a few examples and Leila answered all of the class’s questions and offered to provide supplementary material for some of the more advanced questions. I left the class feeling that my R and Power BI knowledge was at a competent level and ready to dive into some of the more advanced material that Leila provided.
Cancellation up to 5 weeks before the event: full 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 the event): 25% of the standard price of the event to transfer
Transfer fee to another event date* (from 2 weeks to 1 day before the 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.