DAX

What is DAX in Power BI?

What is DAX in Power BI?

Business intelligence is a critical aspect of any business. Along the development path of the business intelligence tools, Power BI  has stood out as one of the most versatile tools. Power BI, developed by Microsoft is a cloud-based business analysis suite that comes packaged with tools for performing calculations, data analysis, and data visualization. It is a drag-and-drop tool that allows you to select your desired field for analysis and drop in on the Power BI dashboard to perform analyses and generate insights. If you have undertaken the PowerBI training, you are probably familiar with PowerBI components including:

  • Power Query 
  • Power Pivot 
  • Power View 
  • Power Map 
  • Power BI desktop 
  • Power BI website 
  • Power Q & A

Power BI features 

  • An interactive dashboard 
  • Aesthetic designs 
  • Compatibility with databases 
  • Fast and easy creation of charts, dashboards, and Power BI reports
  • Real-time alerts and visualizations
  • Works well with other Microsoft tools like Azure, SQL Server, and Windows voice assistant Cortana

What is DAX in Power BI? 

DAX (Data Analysis Expressions) is a formula expression language used in BI tools like Power BI Desktop, PowerPivot, SQL Server Analysis Services (SSAS) to define custom calculations and analysis for the tabular data models. DAX in itself is a collection of functions, constants, and operators that are applied to an expression or a formula to perform dynamic aggregations and return one or more values. Ideally, DAX was designed to help extract more information from data that already exists in your model. Therefore, DAX helps data analysts to make the most out of the data sets that they have.  

DAX includes some of the functions used in Microsoft Excel formulas as well as other functions that can be applied to relational data to perform dynamic aggregation. Overall, DAX is simple and easy to learn. 

Importance of Data Analysis Expressions (DAX) 

As we have already seen, Power BI is used to run reports and present insights in the form of visuals from a company’s datasets. Power BI is built with a calculation engine that automatically performs calculations on data to generate reports with the desired insights. However, where more advanced analysis needs to be done on data, Power BI drag-and-drop function is not adequate. This is where DAX comes in. DAX expressions allow you to perform advanced calculations and data analysis on existing data sets. Creating DAX expressions is similar to the way formulas are created in Microsoft Excel. 

DAX Functions 

DAX functions allow you to perform calculations on the data organized in the tables in your data model. As we have already seen, some DAX formulas resemble those used in Excel in name and functionality but have been adjusted to be used on DAX tables in the data models. Other DAX functions are designed to be applied to relational data. Still, others are used for performing dynamic aggregations. 

Steps to creating DAX formulas

To create a DAX function:

  1. Begin by typing the equal (=) sign 
  2. Type the function name or expression 
  3. Type in the required values or supply arguments to the function by selecting the appropriate one from the dropdown list. PowerBI lists all functions in a dropdown list in each category which makes it easy to select the function that you want. 
  4. Close off parentheses. Also, check that referencing is correct on your columns, tables, and values.  
  5. Press enter to apply the formula to your data model

Types of DAX calculations 

Power BI has two main categories of calculations using DAX. These are:

  • Calculated columns
  • Calculated measures 

The data modeling tab includes a formula bar in which you can type your formula to perform your calculations. Both columns and measures use DAX expressions.  

DAX functions 

DAX has at least 250 functions. Some commonly used DAX function categories include:

  1. Date and time functions are used to create time and date calculations. These are similar to the Excel time and date formulas. Examples include DATE, HOUR, NOW, and WEEKDAY. 
  2. Filter functions that are applied on data models to return specific data types filtered by related values. Examples include ALL, ALLEXCEPT, ALLSELECTED, and CALCULATE. 
  3. Aggregation functions that are applied to return scalar values. Examples include MIN, MAX, AVERAGE, and SUM. 
  4. Financial functions are the same as those used in Microsoft Excel. They are used in formulas that perform financial calculations such as rate of return. Examples include ACCRINT (accrued interest), ACCRINTM (accrued interest on maturity), and DISC (discount rate for security).
  5. Logical functions are applied to expressions to return information about the values in the expressions. Examples include IF, AND, OR, and NOT. 
  6. Math and trigonometric functions are also similar to Excel math and trig functions with minor differences in the DAX numeric data types. Examples include ABS, ASIN, COS, and ACOS. 
  7. Other functions are special functions that perform very unique functions that cannot be classified under any of the categories. Examples include EXCEPT, GROUPBY, VALUE, TRIM, and UPPER.  
  8. Relationships management functions are applied to tables to return values on relationships. Examples include RELATEDTABLE, USERELATIONSHIP, CROSSFILTER, and RELATED. 
  9. Statistical functions perform aggregations. Examples include BETA.DIST, CONFIDENCE.NORM, MEDIANX, and BETA.INV. 
  10. Table manipulation functions manipulate existing tables or return tables. Examples include ADDCOLUMNS, CROSSJOIN, DATATABLE, and GROUPBY. 
  11. Text functions are used to manipulate strings. Examples include REPLACE, SEARCH, FIXED, and UPPER. 
  12. Time intelligence functions perform calculations together with aggregations to build comparisons and relationships between time periods. Examples include LASTDATE, NEXTDAY, NEXTMONTH, and NEXTQUARTER. 

Conclusion 

DAX was designed to be a solution to advanced data challenges in Power BI. DAX comes with higher data analysis and calculations capabilities. If you are familiar with basic Power BI concepts and Excel formulas, grasping the DAX function will be much easier for you. DAX enables businesses to make the most of the data sets available to them. This is certainly way beyond the capacities of Power BI which is limited to drawing reports from data and creating visualizations. Thus, DAX brings out the powerful data analysis capabilities of Power BI. 

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agricultural

4 Agricultural Tech Developments That Will Become Helpful This 2021

4 Agricultural Tech Developments That Will Become Helpful This 2021

As this planet becomes more overcrowded, with the world’s population increasing the demand for food, agriculture will become more dependent on technology. Farmers have mouths to feed, and to do so they need to grow crops and raise livestock. Thanks to technological developments, this process is now made easier. The year ahead will witness how Agtech innovations will conquer world hunger and alleviate food insecurity.

  1. Blockchain and Radio-frequency Identification (RFID)

Blockchain works alongside RFID to trace the source of contamination and assure consumers that the produce is grown in sustainable conditions. With blockchain, the source of contamination can be tracked in the supply chain within seconds. For instance, if batches of grown vegetables are already out in the market, and alarm has been notified, only those contaminated will be pulled out. Instead of discarding all crops, a tracing method could recall only the infested crops, so the rest can still be sold.

This is possible because of RFID sensors, which can tag both animals and crops with an ID number, unique for tracking. It stores and collects information to be used by the blockchain. This will help consumers determine whether producers adhere to protocols for health and safety, and to hold farmers accountable. 

  1. Hydroponics

Advanced hydroponics systems are now challenging the notion of traditional farming. This method doesn’t require land at all, and instead makes use of mineral-rich or electrolyzed water to grow crops. Water-efficiency could reach up to 95%, as opposed to land horticulture. Because of this, fish farms even integrate plan systems to the circulation of water in a method known as aquaponics.

  1. Farming Drones

It comes as news to no one that farming is a labor-intensive, painstaking process. So technology came up with a solution: farming drones. These unmanned drones are instrumental due to their satellite imagery, which can create 3D maps that could better inform the farmer about crop cycles. The field data they gather can be collected to help the farmer come up with farming strategies. 

Drones can also monitor the health of crops on a daily basis, taking into account the condition of the soil and weather, to ensure optimal time for harvest.  They can even determine the exact amount of water that a crop needs. What was done manually with heavy machinery is now assisted by software, for more efficient results. Aside from that, drones can perform many functions such as:

  • Planting
  • Crop spraying
  • Crop health assessment and monitoring
  • Irrigation
  • Soil analysis
  1. Automation and Robotics 

Artificial Intelligence (AI) will take center stage and dominate harvesting methods. Agriculture professionals at  Pinduoduo global even suggest that robot harvesters might replace the majority of hand pickers, because of the long list of things they’re capable of. Most robots are equipped with GPS (so you could locate them), powered by solar panels, and autonomous in nature to operate on their own.

Because of their highly-precise movements, robots are cost-efficient and lessen inaccurate use and measure of herbicide. Plus, robots can work all day, without rest, picking fruits and crops. No doubt, they are far better equipped than humans, with special skills ranging from ripeness sensitivity to weed detection.

Agriculture is breaking away from traditional methods of farming and raising livestock. Technological innovations will make knowledge new again. And as outdated modes of farming become obsolete, we must welcome the promise of a brighter future, where there is food on everyone’s plate.

Also check –> https://www.neoadviser.com/microsoft-surface-book-3-officially-revealed-starting-at-1-599/

Augmented Reality And Artificial Intelligence – How They Would Build The Future

What is augmented reality? This word is the current hype of tech enthusiasts and every other person who is interested in the latest technology. There has been a substantial boom in augmented reality in the past five years. Mainly because it allows consumers to visualize a product they like in a 3D environment. As a result, you, as a consumer, get to know more about the product you are looking for and can decide whether you want to purchase it or not.

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In the coming years, with the help of Artificial intelligence, augmented reality will be more affordable, and many small businesses can quickly start using it for their products. According to statistics, you might see more than 1 billion users on augmented reality by the end of 2020. It’s a fantastic technology and just draws people towards it. So today, we are going to discuss how AI and augmented reality will go hand in hand to build a better future.

Augmented Reality A Window To The Future In Present

Before we start, we need to define what augmented reality is and how one can differentiate it with virtual reality. Applications that use augmented reality overlay necessary information of a virtual object seen on the screen on a real-world background in realtime.

Furthermore, when using the augmented reality, one adds the required information in an existing environment. On the other hand, virtual reality is an artificial environment developed by software; it works and responds to gestures and movements like a real environment.

Now let’s move to the real world examples where augmented reality is currently taking charge of shaping the user experience of the customers.

The Gatwick Airport Passenger App

We all have been there, going on a business trip and getting lost in a massive airport to find your connecting flight. Gatwick Airport is one of the biggest airports with a footfall of more than 46 million in a single year; it is one of the busiest airports. So it’s relatively easy to get lost.

Gatwick came with a solution. They developed an augmented app that you can use to easily navigate from one terminal to the other by just looking at your mobile screen. The airport aims to improve passenger traffic with this app shortly.

The Ikea Place App Help

Want to change your furniture but not sure about the dimensions? Don’t worry, scan the room where you want to put the furniture in the Ikea Place App and start putting the Ikea object in the virtual representation of your room. With this app, you can easily avoid the predicament issues that come when you can’t measure the place and the furniture.

Put On Your Make-Up Virtually With Sephora

Sephora, a cosmetic company, has built an app for it is customers, which gives them the freedom to try multiple looks by using their cosmetics on the digitally created face. Just scan your face with the app, and you can start putting on the different colour shade on lips, eyes, and cheeks. You can see how it will look on you even before buying any product now. That can save you a lot of money and space.

Artificial intelligence Is Everywhere

One might think AI is still a new technology, but in truth, John McCarthy made the first AI system in the year 1955. In 2019 its everywhere in phones, laptops, social media apps, even in cars. AI, at this very moment, is currently working in the background of your mobile phone and making recommendations and keeping the data and information up to date.

Given below are some examples of how AI is so seamlessly working in your life.

AI In Google Maps

Every day you go out for work, and at the same time, your Google Maps prompts you with a notification that how much time it’s going to take you to reach the office and whether there are any traffic jams and how you can evade them by using an alternate route. Yes, it’s not your GPS that’s showing you all this, but it’s the Google AI bots that are working tirelessly to make you reach office on time. Shortly, Google is trying to create a self-healing map system that will automatically update the map itself by using AI analyzing imagery.

Tesla’s Range Of Self Driving Car

Elon Musk can’t resist himself to get in the field of AI, and he does it with style. With his company launching a self-driving car, Elon musk has shown us the future of automobiles and driving. Tesla uses autopilot, which works on the onboard computer. The chipset uses neural networks for its vision, sonar, and radar processing software. By using this technology, you have access to all the direction which you can’t see while driving.

Cyborg Replacing Bloomberg Reporters

Here AI is trying to replace humans, but you don’t have to worry as our reporters are giving them a tough fight. In 2019, roughly a third of Bloomberg published content, whether its a news or a magazine goes under automated technology edit before getting published. Moreover, the company reports are taking help from the inhouse cyborg to create a massive amount of content for the company.

If you have read an article on minor league baseball on Bloomberg’s site, don’t be surprised if we say it was written by a robot reporter who is using AI technology to collect the game’s data from the internet.

To Conclude AI and AR Are The Building Blocks Of Our Future

You don’t have to look into the future to find AI and AR. You need to look at your phone screen right now, and you can see how AI and AR are transforming your lives right now. People generally think using AI requires a lot of computing power, but the truth is we are using it at this very moment in our mobile phones. So yes, we can say the future of AI and AR is now.

Author Bio

Great Learning is an ed-tech company that offers programs in career critical competencies such as Analytics, Data Science, Big Data, Machine Learning, Artificial Intelligence, Cloud Computing, DevOps, Digital Marketing and more.

Our programs are taken by thousands of professionals globally who build competencies in these emerging areas to secure and grow their careers. At Great Learning, our focus is on creating industry relevant programs and crafting learning experiences that help candidates learn, apply and demonstrate capabilities in areas that are driving the future.

We are on a mission to make professionals proficient and future ready. In the last 5 years, we have been able to deliver 5+ million hours of learning to professionals worldwide with thousands of them being able to achieve a successful career progression in leading companies such as Microsoft, Amazon, Adobe, American Express, Deloitte, IBM, Accenture, McKinsey and more.

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How does Artificial Intelligence Work?

“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial. “Max Tegmark, President of the Future of Life Institute.

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Artificial Intelligence (AI) is defined as the replication of human intelligence in machines that are programmed to think like humans and simulate their actions. The goals of artificial intelligence include reasoning, perception, and learning. Artificial Intelligence has the power to rationalize and perform actions that have the highest chance of meeting to a specific objective.

From SIRI to self-driving cars, artificial intelligence (AI) is progressing at a fast rate. While science fiction often portrays AI as robots with human-like characteristics, AI has the potential to comprehend anything from IBM’s Watson to Google’s search algorithms to autonomous weapons.

The term artificial intelligence came into being in 1956 but it has become more well-known today due to its advanced algorithms, improvements in computing storage and power and increased data volumes.

We are surrounded by artificial intelligence (AI) directly or indirectly. The tasks which were earlier reserved for humans are now been taken over by machines. Artificial intelligence would soon be the prominent transformative technology that would change human lives.

One of Britain’s pre-eminent scientists, Professor Stephen Hawking quoted to BBC – “The development of full artificial intelligence could spell the end of the human race.” He said that the primitive forms of artificial intelligence developed to date has proved a boon but he fears the consequences of making something that can surpass or match human-beings.

Today every field has a high demand for AI capabilities – mainly question answering systems that can be used for risk notification, legal assistance, medical research, and patent searches. AI performs high-volume, frequent, computerized tasks consistently and without fatigue. Artificial intelligence revolution has unimaginable potential, to transform wide-ranging areas that were unimaginable earlier. In short, in the coming years, AI would revolutionize every aspect of our lives.

Artificial Intelligence works by combining large amounts of data with fast, intelligent algorithms and iterative processing, letting the software to learn automatically from features or patterns in the data. A subfield of AI, cognitive computing strives for a usual, human-like interaction with machines.

Since the role of the data is now more important than earlier, it can create a competitive edge. If you have the best quality data in a competitive industry, even if everyone is applying the same techniques, the best data will always win.

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Technologies that support Artificial Intelligence

  • Machine Learning (ML) – It is an application of AI, focusing on the development of algorithms that can analyze data and make predictions. Machine learning (ML) in simple terms is a machine’s capability to learn from the experiences and examples it understands from the data it receives. 
  • Deep Learning – Deep learning originates from Machine learning, which principally focuses on evaluating more layers and layers of patterns of data as in the neural networks of a real human brain. The machines learn from negative and positive reinforcement of the jobs they carry out, which requires continuous processing and reinforcement to progress.

Neural Network – It is a type of (ML) machine learning which is formed of interconnected units (perceived as neurons) that processes information by relaying information between each unit and responding to external inputs. This process analyzes data multiple times to search associations and develop meaning from undefined data.

  • Cognitive Computing – A subfield of AI, cognitive computing that strives for a human-like interaction (natural) with machines. Using cognitive computing and AI, the vital objective is for a machine to mimic human processes through the ability to interpret speech and images – and then revert logically in response.  
  • Natural Language Processing (NLP) – NLP is the capability of computers to understand, analyse, and generate human language, including speech. The second stage of natural language processing is natural language interaction, which allows human beings to communicate with computers using normal, day to day language to perform tasks. E.g. Skype Translator, which understands the speech of multiple languages in real-time to facilitate communication.
  • Computer Vision – It is a technique which depends upon deep learning and pattern recognition to identify what’s in a video or picture. When machines can process, scrutinize and understand images, they can capture videos or images in real-time and understand their surroundings.

In addition to the above, there are several technologies which enable and support Artificial Intelligence:

  • Graphical Processing Units or GPUs – They are important enablers to AI as they provide the massive computing power needed for iterative processing. Big data plus compute power is required for training neural networks.
  • The Internet of Things (IoT) – It generates a huge amount of data from the devices connected, most of which are unanalyzed. The internet of things is anticipated to connect over 100 billion devices in the coming future.
  • Advanced Algorithms – These are being developed and pooled in new ways to study more data quicker and at various levels. This intelligent processing is crucial to identify and predict rare events, optimizing unique scenarios and understand complex systems.
  • Application Processing Interfaces (APIs) – Portable packages of code which help to add artificial intelligence functionality to software packages and products. They can add image recognition capabilities to home security systems and question and answer capabilities that describe data, create headlines and captions, or call out motivating patterns and insights in data.

A report generated by Gartner suggests that by 2020, AI would generate an estimated 2.3 million jobs. This figure was calculated by taking into account the 1.8 million jobs made simpler by automation. 

How can you learn AI

Now that you know what AI is and the technologies that use AI, you might be intrigued to learn AI and make a career in it. If that’s the case, this is a great time since more and more organizations are implementing AI for various business processes. There are various ways of learning AI, but the best way of doing it is by opting for an AI training course. Once you complete the course, you will be well-versed with the nuances of AI and it will ensure that you have a great career in the AI industry.

The goal of Artificial Intelligence is to provide software that can reason on input and provide an explanation on output. Although AI is not a replacement for humans it will provide human-like interactions with software and offer decision support for specific tasks.

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Artificial Intelligence beat one of the best teams in the world of Dota 2

Artificial Intelligence beat one of the best teams in the world of Dota 2

OpenAI Five successfully beat five players from the OG team at Dota 2, a veteran team that won the Valve 2018 International competition.

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The success of artificial intelligence in the face of OG was with an aggressive and unconventional method, such as the immediate resurrection of heroes in the early stages and the selection of worthy heroes.

While OG fought well, he could not handle OpenAI for more than 30 minutes round. But there are more. OpenAI demonstrated how Five could play in front of people and learn from their game styles.

But do not think it’s perfect. Usually OpenAI chooses short term strategy and can only play under certain rules.

People are better off in long-term strategy and OG could have won if AI would not have created the advantage at first.

OpenAI is powerful enough not only to score successfully in Dota 2, but also in other games. The techniques here learned logically can also be applied to robots and other tasks that are not related to games.

This was the latest OpenAI public demonstration.

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neOadviser – TECH

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Artificial intelligence manages to read the mind

Artificial intelligence manages to read the mind

Artificial intelligence has been able to read the mind of another computer, a first step towards a scenario where machines can co-operate with each other, according to a project whose results are published in the science journal ‘Science’.

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Artificial intelligence manages to read the mind

The company created by “Google” to develop artificial intelligence technologies, “DeepMind” has developed the “ToMnet” project and introduced it to Stockholm at the International Conference on Machine Learning.

Realized under Neil Rabinowitz, the project “ToMnet” is based on three “neurons” networks, more specifically networks that mimic the brain organization, each one being able to learn from experience.

To train artificial intelligence “ToMnet” to predict other machine behaviors, researchers have used a virtual game that is based on three categories of characters moving into a room to collect colored objects, writes atd.

These categories of characters consisted of blind characters that followed the road along the walls, myopia moving to the closest objects and super-visually appealing characters that captured objects using strategies in a certain order to gain more points.

After doing some workouts, the system managed to identify all the characters and correctly predict the behaviors of each of them.

> Read Next: Samsung Galaxy S7, phones vulnerable to hackers

So what do you think of this? Let us know your thoughts in the comments section below, follow us on twitter and facebook for more news and updates.

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