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You will learn all these items in this section, you will also learn about time zone consideration when working with current date and time in Power BI, and how you can resolve it in Power Query. When you combine tables, you get a structured column as a result, which can be a table, list, or record in every value. There are several transformations you can apply on structured columns, which you will learn in this section. You will also learn what may be the potential issue with some of these transformations. There are two types of transformations in Power Query; Transforming an existing column, or adding a column based on a transformation.
In this section, you will learn about these two types, their differences, and few other transformations that we have available in the add column tab of the Power Query Editor through some examples. Power Query is a powerful tool for data transformation. This power can be amplified even more if you can make your queries dynamic.
Instead of repeating several steps for similar data sources, you can create a function from those steps, and run that function for all other sources. Functions get parameters as the input. Functions and parameters can make everything in Power Query dynamic. If you want to learn Power Query advanced deep dive, this is the section to go through.
If you want to be a good data wrangler or data developer with Power Query, you must learn M scripting. The good news is that M scripting is not a hard language to learn. As you are dealing with data in Power Query, it is important to learn how to work with table, list, and record from the code. In this section, you will learn about these three structures in code, and how to navigate between different parts of each structure. Now that you know more about M scripting, it is time to see how powerful this part of Power Query can be compared to the graphical interface of query editor.
In this section, you will learn features that you have access to apply using M scripting. You will learn ways to get a list of all functions, doing error handling in an advanced way. Applying some changes in functions and parameters which is only possible through the code. You will also learn an end-to-end example using everything you learn about M at the end. Some transformations or operations needs careful attention in Power Query. In this section, you will learn about performance tips and trick for Power Query to make sure you have always a well performance tuned data transformation logic.
Each sample will be discussed through the live demo in this section. In any data related solution, you should expect bad data rows to appear. This section is all about how to handle errors, deal with bad data rows, create troubleshooting reports, etc. At the end of the training, we go through some end-to-end solutions using Power Query. These solutions leverage everything you learned through the training about this tool and language; you will see how all those parts come to help together to build the solution. We will go through building a date dimension which has all calendar columns, fiscal columns, and public holidays fetched live, and we will talk about combining files from a folder.
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.
If you are a data scientist, data analyst, business intelligence developer, or data architect, this course has many things to teach you all. 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 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.
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. 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.
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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. 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. This training is designed for data science, data analysis and who want to do machine learning by writing R or Python code. This course will start with some explanation of different machine learning algorithms and approaches.
Then, some discussion on basic statistical analysis will be provided such as probability, factor analysis, hypothesis testing and so forth. Then the process of machine learning from business understanding, data cleaning, feature selection, model selection, split data for testing and training, evaluating the created model and finally developing and visual the trained model and analyzing the result will be presented.
For predict analysis algorithms such as decision tree, boosted decision tree, decision forest will be explained.
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The concept and how they work will be explained. Then how to set parameters for each of them will be illustrated. Also, the process of data preparation for each of these algorithms will be discussed. Finally, the related code for writing this algorithm in the cloud will be explained. The same process will be done for the descriptive algorithms such as clustering. In this two days training, the audience will learn some deep concepts for machine learning, data analysis, main algorithms for predictive, descriptive and statistical analysis using R, R in Power BI and SQL Server.
The main concepts, life cycle and best practice of doing machine learning with Microsoft product will be explained. In this section, the audience will learn some of the algorithms such as Decision tree, Decision Forest, regression and SVM for the aim of predictive analytics. The main concepts of these algorithms will be explained, and the related R or Python code will be shown. How to analysis the trained model and set up the parameters also will be discussed. Finally, how to evaluate the result will be explained.
Descriptive analytics is an unsupervised learning approach. In this part, the audience will be familiar with some of the main algorithms for descriptive analytics from text mining, clustering and, Market basket analytics. Forecasting is one of the main approaches for time series.
The main concepts of the time series will be explained and how to decompose time series, how to use exponential smoothing and ARIMA for forecasting the time series data. This training is designed for data modeler, who have the data prepared to be modeled for analysis. Usually, people who attend this training needs to attend module 2 beforehand to learn about the step before the modeling. In this two days training, you will learn DAX from zero to hero. You will learn how to design the best model in Power BI with relationships, considering formatting and data types.
You will learn from Simple DAX calculations to complex expressions and calculations for solving real-world challenges of a BI solution. The training continues with more focus on DAX; we will talk about the evaluation context in DAX, which defines the mindset and the way of thinking when you are writing DAX expressions. You will learn about DAX function categories such as Aggregation functions, Iterators, Filter functions, parent-child functions, time intelligence functions, functions dealing with relationships, etc.
You will learn all scenarios through hands-on examples of real-world data. At the end of this training, you will be able to design the proper data model in Power BI, understand all relationship requirements and implement the right relationship, write complex DAX expressions for your analytics need, and put them all together to build the best model for your data analysis solution using Power BI. Power BI uses the in-memory engine, named xVelocity. The in-memory engine of Power BI makes the analysis super-fast.
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Everything will respond very fast in this model. In this section, you will learn about the basics of the modeling engine and some of the differences of that with SSAS and Power Pivot. This is the expression language in Power BI for analytics. DAX is a dynamic expression language which will consider the interaction of the user at the time of visualization.
Using DAX, you can do calculations such as year to date, year over year comparison, etc. Most of the data modeling training is about DAX. In this section, you will learn the basics of DAX. There are three types of calculations in Power BI. Calculated Column, Measure, and Calculated Table. You can write DAX expression in all these three types of objects. This section will teach you what the main difference between the calculated column, measure, and the calculated table is, and what are scenarios of using them.
The first set of important functions in DAX are aggregation functions. The way of working with iterators is different. Iterators get an input table and an expression. Example of iterator function is SUMX. Filter functions are probably the most important functions in DAX. You can refer to a column in DAX like Excel , but you cannot refer to rows. If you want to refer to rows in an expression, you must filter it, and that is why Filter functions are important.
There are several filter functions, and the behavior of all of them are unique. In this section, we will talk about some of these functions through real-world examples. Understanding Evaluation contexts are one of the most critical learnings in DAX. There are two types of contexts; Row context, and filter context. In this section, you will learn about the difference of all these, and you will learn scenarios that you need to be careful when the context changes.
Some other functions change the behavior of relationship such as UseRelationship function. In this section, you will learn about relationship functions in DAX and scenarios of using them. Calculations based on time and date are critical for many businesses such as finance. You can use DAX to do calculations such as year to date, fiscal year to date, year over year comparison, and rolling 12 months average. In this section, you will learn some basic time intelligence functions such as TotalYTD to a calculated year to date. You will also learn about scenarios when you do not have the built-in function for your use case and will learn how to write the combination of function usages in DAX to achieve the solution.
However, you can take a step further, and make the expression of DAX even more dynamic. The user can change a value which is defined statically in your DAX expression using a parameter. Parameters will make your DAX expressions even more dynamic. In this section, you will learn about parameters, and their usages, and the scenario of using a parameter table to select from multiple measures dynamically. DAX can navigate through a hierarchy with an unknown number of levels.
Example of such a hierarchy is a chart of accounts or organizational hierarchy. In this section, you will learn about parent-child functions which can be used for organization hierarchy. You will learn different scenarios of using these functions in real-world examples. Performance considerations when doing the modeling in Power BI is the last but not least important part of this course.
You will learn about tips and tricks that keep your model performing well even with a huge number of data rows. In this section, you will also learn about other tips that make the maintenance of your model easier, such as using a tool like Power BI Helper. In this one-day training, the audience will get familiar with some AI tools available in Microsoft such as cognitive services, Bot framework, AI websites and so forth.
The main specifications of these tools are that there is no need to write R or python codes for the aim of machine learning. In this one-day training audience will learn how to set up these AI tools in Azure, and how to use some of the cool AI websites like custom vision, QnA and so forth. Microsoft Cognitive Services formerly Project Oxford are a set of APIs, SDKs and services available to developers to make their applications more intelligent, engaging and discoverable.
In this section, below item will be explained. Developers can get started in seconds with out-of-the-box templates for scenarios including basic, form, language understanding, question and answer, and proactive bots. There is a possibility to create an application in combination with Cognitive services.
The audience will learn how to create a Face recognition API in. Net application for identifying the age, emotion, and so forth. Also, some more explanation of how to use Microsoft flow for creating a process to apply the cognitive services on the data. The process of how to set up the Flow using a template or to use a blank flow will be explained. There is a possibility to create a model in Azure, create an API out of it, then use it in Stream analytics for applying machine learning in live data will be explained.
Moreover, how to use that model in Power BI also will be explained briefly. This training is designed for data architect or administrator, who is designing the architecture of leveraging Power BI in a solution. Someone who wants to understand how all components of Power BI are sitting beside each other to build the whole solution. This training is designed for understanding the strategy of using Power BI rather than the development of it. In this training, you will learn about architecting the strategy of a Power BI solution from end to end.
This is not training about development Modules 1 to 3 already covered that. You do not need to attend previous modules to attend this course. This course is designed separately from those. However, knowing some of the basics of Power BI is helpful. You will learn about Power BI Service, and different types of connections in Power BI and choose the right type of connection for your solution. You will also learn all different ways of sharing a Power BI Solution and the pros and cons of each. The course continues with a detailed discussion about row-level security.
You will learn how gateway configuration and set up will be in the whole package. You will also learn about integrating Power BI with other tools, and some architecture blueprints to follow when you are designing a Power BI solution. At the end of this training, you will be able to design the architecture of Power BI for your requirement; you can choose the right way of sharing and design the gateway configuration as well as the communication of Power BI with other tools and services.
You will be able to answer any questions that come to your mind about a solution architecture in Power BI world and make the right decision to choose the right strategy of Power BI usage in your organization. In this section, you will learn about these objects and their configuration. You will also learn about the different types of connections in Power BI, and the position of the gateway in the solution, configuring and installing it. There are several ways of sharing Power BI reports and dashboards.
Each of the methods has pros and cons and should be used in specific scenarios. In this section, you will learn scenarios to use each of these methods for sharing, and the sharing of architecture, and a comparison between all these methods at the end. Sharing is about giving users access to the entire content; security is about giving them access to part of it.
There are different ways of implementing security which is called row-level security in Power BI. Statics row-level security is a good option when roles are limited items. The next level is to define a dynamic row-level security using DAX functions. Dynamic row-level security comes as different patterns which will be discussed here through examples.
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As a Power BI administrator, you need to have a careful eye on some of the metrics, and control some of the settings across your organization Power BI tenant. In this section, you will learn about Power BI administrator configuration options and options which are critical to controlling. You will also learn about all licensing options for Power BI and will have a clear view of what would be the best licensing option for you. In this section, you will learn about all integration options for Power BI.
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The last part of the training focuses on architecture blueprints for Power BI. Table of Contents Preface Chapter 1: He has a BsC in Computer engineering; he has more than 10 years experience in programming and development mostly on Microsoft technologies. His articles on different aspects of technologies especially on SSIS can be found on his blog: For further details you can also visit his personal blog at http: Leave a Reply Cancel reply Your email address will not be published.
Check the schedule of upcoming courses. 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. Here are a list and detailed agenda of each module: Data Science with Microsoft Cloud 2-days Module 3: 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. You will see some demos and introduction about: Introduction to R Language: Introduction to Machine Learning In this section, some introduction to Machine learning will be provided.
In this module audience will learn: How to draw some basic charts like Histogram, Boxplot, table gride will be shown.
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. 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. Pre-build Advanced Analytics Visualization Microsoft provides lots of interesting advanced analytics chart in the marketplace for Power BI users.
How to access the Power BI advanced analytics visualization in Marketplace What is decision tree visualization and how to use it What is time series visualizations? How to use clustering and correlation analytics visualization 1. For the schedule of our online training follow this link: Cancellation from 5 weeks to 2 weeks before the event: Save Save Save Save Save. Time 17 Monday 9: Organizer Leila Etaati leila radacad.
Online Central Time Zone. Power BI for Architects 1 day Module 5: The training includes but not limited to topics below: Introduction to Power BI: What is Power BI? What is Power Query: Data Visualization Data Visualization is the front end of any BI application; this is the user viewpoint of your system. The content that you will learn in this module includes but not limited to; Basic Sharing and using workspaces in Power BI web site Dashboard vs.
Visualization Basics Visualization is an important part of any BI system. Filters Scopes of Filters: Advanced Visualization in Power BI Know that you know about visualizations and slicing and dicing, it is a good time to talk about some advanced techniques which will take your visualizations to the next level in Power BI. Custom Visuals In addition to the built-in list of Power BI visuals, you can leverage some of the third party visuals created by other companies called Custom Visuals.
Power BI is only for Microsoft based environments and platforms. Power BI is not a powerful and fully functional BI tool, and cannot be compared with other tools in the market. In-Person Training; Our Power BI in-person training will be held in high-quality venues with the recommendation for hotel bookings for attendees.
Kemp, Canada The course was an excellent investment overall, it exceed my expectations. Organizer Reza Rad reza radacad. Data Types and Data Structures Before going any further in learning Power Query, you need to understand about data structures and data types. Combine Queries One of the most common data transformations is combining datasets. Better Power Query Editor Experience To get the best experience with Power Query Editor, you need to consider organizing your queries and steps in the right way.
Reducing Number of Rows; Filtering Filtering rows in Power Query is an important transformation especially when the dataset is big, or when the data needs to be cleaned. Table Transformations Some of the most important table transformations will be explained in this section. Text Transformations When you work with text values, there are many transformations you can apply. Numeric Transformations You will learn in this section how to do numeric transformations.
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Standard transformations; Divide, Integer-divide, Multiply, Add etc. Scientific transformations; logarithm, power square, etc. Date and Time Transformations There are many transformations applicable to date and time columns. Structured Column Transformations When you combine tables, you get a structured column as a result, which can be a table, list, or record in every value. What is a Structured Column? Expand Aggregate Expand and Aggregate: Add Column Transformations There are two types of transformations in Power Query; Transforming an existing column, or adding a column based on a transformation.
Add Column with a Transformation Index Column: Power Query Formula Language: Working with Data Structures in M As you are dealing with data in Power Query, it is important to learn how to work with table, list, and record from the code. Advanced M Scripting Now that you know more about M scripting, it is time to see how powerful this part of Power Query can be compared to the graphical interface of query editor.
Error Handling In any data related solution, you should expect bad data rows to appear. Use Cases At the end of the training, we go through some end-to-end solutions using Power Query.