Guide Adams Coding and Reimbursement - E-Book: A Simplified Approach (Adams Coding & Reimbursement)

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Module 2: Data Science with Microsoft Cloud – 2 days course

Easy-to-use, interactive features let you make highlights, share notes, run instant topic searches, and so much more. Best of all, with Pageburst, you get flexible online, offline, and mobile access to all your digital books. Combining the basics of coding, insurance, and reimbursement in one concise text, Adams' Coding and Reimbursement: A Simplified Approach, 4th Edition looks at the big picture of medical billing and coding and shows how it fits into the physician reimbursement system.

Clearly organized, full-color chapters guide you through the entire coding and claims process, detailing coding rules and applications, insurance guidelines, and the reimbursement system. 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. 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. The last part of the training focuses on architecture blueprints for Power BI. 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.

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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 analysts, who have the data modeled and ready for them to visualize. In this training, you will learn detailed visualization practices of Power BI. In this two days training, you will learn all components of Power BI needed for building Power BI reports and dashboards.

You will also learn about the Power BI service website , and components that are needed to share your reports with others. The training continues with more focus on Visualization components such as different types of charts and visuals and scenario to use them. You will also learn about advanced visualization techniques such as designing mobile reports, changing the interactivity of visual, detailed learning of slicers and filters, creating parent-child reports, etc.

At the end of this training, you will be able to do all visualization requirements for Power BI and will understand all other components of Power BI and how these are all working together. You will see some basic demos of how easy to use is Power BI in some scenarios. Getting Data is the first experience of working with Power BI. You can connect to many data sources on-premises or on the cloud.

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In this section, you will learn how the Get data experience in Power BI is and how you can transform the data in a way to get it ready for modeling. Data Modelling in Power BI is an in-memory-based technology. You will learn about the structure of modeling in Power BI, and you will learn the importance of relationships and their direction. You will also learn about calculations in Power BI and how to write them. DAX has a similar structure to excel functions, but it is different.

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Data Visualization is the front end of any BI application; this is the user viewpoint of your system. It is critical to visualize measures, and dimensions effectively so the BI system could tell the story of the data. In this module, you will learn conceptual best practices of data visualizations which is valid through all data visualization tools. You will learn Power BI visualization skills.

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  • You will learn how to create effective charts, and dashboards using these tools as well as best practices for working with Power BI Desktop. In this module, we will go through deployment options, Configurations, and requirements required for each environment. You will also learn how to build dashboards on the website, and how to work with Mobile Apps for Apple, Android and Windows Phone. Visualization is an important part of any BI system. In Power BI, Visualization plays a critical part.

    In this section, you will learn about why visualization is important, with few basics of visualizations such as comparing stacked vs. The importance of slicing and dicing data in Power BI is critical. Power BI is not a visualization tool only, but it is also a data exploration tool. There are several ways you can filter the data. In this section, you will learn the difference between filters and slicers. You also learn about different scopes of filtering, as well as filtering modes. For slicers, you will learn different types of slicers and some advanced features such as syncing slicers through different pages.

    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. You will learn about techniques such as changing the interaction of visuals, report page tooltips, Bookmarks and dynamic visualizations in this section. It is a good time to learn about all built-in visuals in Power BI now.

    It is important to know which visual should be used in what scenario. You will also learn in this section about the pros and cons of each visual. You will learn specific features of the visual that can help to solve real-world scenarios.

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    All the examples will be hands-on. In Power BI, there are multiple ways of visualizing items on the map. In this section, you will learn about all the built-in ways of using map visuals in Power BI, the pros, and cons of each method and special considerations for some of the 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. You will learn about the most useful custom visuals in Power BI through many demos.

    In this training, you will learn different ways to use 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. Finally the pre-build advanced analytics visual in Power BI Marketplace will be explained and how to use them will be clarified. 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:. In this section, some introduction to Machine learning will be provided. Also, some explanations on what descriptive and predictive analytics are will be provided. In this module audience will learn:.

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    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. 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.

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    • 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. 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. 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. 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. Microsoft provides lots of interesting advanced analytics chart in the marketplace for Power BI users. This charts able end user to use them for analytics without writing any R scripts. There is a possibility to expand the visualization in Power BI with creating custom visual. These visuals have some difference from what we have in section These custom visuals can be shared with others easily without disclosing the code behind them.

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      These visualizations can be put in a list of visual for other people in the company. In this section. This event would be full of hands-on labs and designed in a way that gives you most of the training value in a short timeframe. The course is in two days modules:. 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.

      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.

      Primary Table. FROM [ Dimension ]. Distinct "Removed Other Columns" ,. Philip Seamark. Too bad. This looks like the perfect solution to my problem. Leave a Reply Cancel reply Your email address will not be published. Academy Membership. Sort By:. Sydney TBD Sydney. Delivery method: In-Person or Online: Check the schedule of upcoming courses. Type of training: Public or private contact us for more details You can join this course online if you are in other countries and cities. Instructor: Dr. 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.

      The training includes but not limited to topics below: 2. 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.

      Models : a bit talk about the available models in Azure mL for predictive, descriptive, prescriptive and anomaly detection. For each scenario, an example will be presented; different algorithms will apply to a problem. How to evaluate and see the result of more than three algorithms on one dataset will be shown.

      Save Save Save Save Save. Organizer Leila Etaati leila radacad. Learn More. Standard transformations; Divide, Integer-divide, Multiply, Add etc. Scientific transformations; logarithm, power square, etc. What is a Structured Column? Add Column vs. What is M? Myth 3: Power BI is only for Microsoft based environments and platforms. Myth 4: Power BI is not a powerful and fully functional BI tool, and cannot be compared with other tools in the market.

      Taxpayers who get this credit, which gives a refund to low- and moderate-income households and more to those with children , are often among the first to file their federal income taxes, meaning that many or most of them got this chore over with back in late January, at the start of tax season. Many families rely on the refund for critical household planning.

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      That cash goes toward overdue utilities, vehicle repairs, and other essentials. Research from the Federal Reserve shows that taxpayers who get this credit see a non-trivial boost in their household spending on food after they receive their refunds. But while the credit is an established, if unsung, part of the safety net, it could stand to be improved. This means increasing the number of people who are eligible for this credit, expanding the definition of work that qualifies people for the credit, and perhaps most importantly, making it pay out monthly instead of annually.

      This proposal would make almost half of Americans eligible for the credit. We also need to look at the folks who are doing just a little bit better than that, but with no safety net. Only those with traditional jobs can get it; caregivers or stay-at-home parents need not apply. Those are all features that the Economic Security Project would like to iron out.

      A more progressive tax policy, Ruben says, would pin this benefit to adults rather than children, allow caregivers and students to claim the credit, and give households an option to receive the credit on a monthly basis in the form of an advance. The Cost-of-Living Refund proposal would cut poverty by nearly a third, doubling the reach of the current credit, according to an analysis from the Urban-Brookings Tax Policy Center.

      Senator Cory Booker is on board. Today, the hopeful unveiled an expanded version of the Earned Income Tax Credit that he calls the Rise Credit , with features similar to the Economic Security Project proposal.