Machine learning is the concept that generic algorithms can tell you something useful about a piece of data without requiring you to build any unique code relevant to the situation. Rather than creating code, you provide data to the generic algorithm, which then produces its own logic depending on the data. For Example: A classification algorithm, for example, is a type of algorithm. It has the ability to categorize data. Without modifying a line of code, the same classification method used to detect handwritten digits could be used to categorize emails as spam or not spam. It's the same algorithm, but because it's fed different training data, it generates different categorization reasoning.
This machine learning algorithm is a black box that can be re-used for lots of different classification problems.
Classical machine learning is frequently classified by how an algorithm learns to improve its prediction accuracy. There are four fundamental techniques for learning:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning.
Let’s see how these algorithms work:
In this sort of machine learning, data scientists provide labeled training data to algorithms and specify the variables they want the program to look for connections between. The algorithm's input and output are both provided.
The following tasks benefit from supervised learning algorithms: • Binary classification: Binary classification is the division of data into two groups. • Multi-class classification: Choosing between more than two categories of replies is referred to as multi-class categorization. • Regression modeling: Predicting continuous values using regression modeling. • Ensembling: Combining the predictions of numerous machine learning models to get an accurate forecast is known as assembling.
Algorithms that train on unlabeled data are used in this sort of machine learning. The program examines data sets for any noteworthy connections. The data used to train algorithms, as well as the predictions or suggestions they provide, are predefined.
The following tasks are well suited to unsupervised learning algorithms: • Clustering: It is the process of dividing a dataset into groups based on similarities. • Anomaly detection: It is the discovery of unexpected data points in a data set. • Association mining: It is the process of identifying groups of things in a data collection that commonly appear together. • Dimensionality reduction: It refers to the process of reducing the number of variables in a data source.
Semi-supervised learning is a hybrid of the two preceding methods of machine learning. Although data scientists may provide mainly labeled training data to an algorithm, the model is allowed to examine the data on its own and establish its own knowledge of the data set. Semi-supervised learning falls between the effectiveness of supervised learning and the efficiency of unsupervised learning.
Semi-supervised learning is utilized in the following areas: • Machine translation is the process of teaching computers to translate languages using less than a full lexicon of words. • Fraud detection: Detecting cases of fraud when there are just a few positive examples. • Data labeling: Algorithms trained on tiny data sets can learn to automatically apply data labels to bigger data sets.
Reinforcement learning is often used by data scientists to teach a machine to execute a multi-step procedure with well-stated criteria. Data scientists build an algorithm to perform a task and provide it with positive or negative cues as it determines how to finish the job. However, for the most part, the algorithm selects what actions to take along the road.
Reinforcement learning is frequently utilized in situations such as: • Robotics: Using this technology, robots may learn to do tasks in the physical environment. • Video gameplay: Reinforcement learning has been used to teach bots to play a variety of video games. • Resource management: Given limited resources and a specific aim, reinforcement learning can assist businesses in determining how to distribute resources.
Machine learning algorithms are molded on a training dataset to create a model. As new input data is introduced to the trained ML algorithm, it uses the developed model to make a prediction. Further, the prediction is checked for accuracy. Based on its accuracy, the ML algorithm is either deployed or trained repeatedly with an augmented training dataset until the desired accuracy is achieved.
The above illustration discloses a high-level use case scenario. However, typical machine learning examples may involve many other factors, variables, and steps.
If not tackled deliberately, the process of selecting the best machine learning model to solve a problem can be time-consuming. Step 1: Align the problem with prospective data inputs for solution consideration. This phase needs the assistance of data scientists and specialists with in-depth knowledge of the problem. Step 2: Gather data, prepare it, and label it as needed. Typically, data scientists lead this process, with assistance from data wranglers. Step 3: Determine the algorithm(s) to utilize and assess their performance. Data scientists are generally in charge of this stage. Step 4: Continue to fine-tune the outputs until they are accurate enough. This process is often carried out by data scientists with input from professionals with in-depth knowledge of the topic.
The process of automating the tasks of applying machine learning to real-world issues is known as automated machine learning. Auto ML encompasses the whole workflow, from raw information to a deployable machine learning model. Data scientists may use Automated Machine Learning (Auto ML) in Power BI for dataflows to train, validate, and deploy Machine Learning (ML) models within Power BI. If you are new to data science, this blog series will help you to start playing around with machine learning and learn how to use the Power BI desktop.
Power BI is a cloud-based set of corporate analytics tools that allows anybody to connect to, view, and analyze data more quickly, efficiently, and effectively. It provides dynamic reports and attractive visualizations that bring data to life by connecting to a wide range of live data through simple dashboards. In short, Power BI is a visualization and Business Intelligence (BI) tool from Microsoft, aimed at helping turn data into insights.
These benefits and features include: • In reports and visualizations, combine data from several sources. • Quickly generate graphs and visual data representations Provide dynamic filtering • Provide drill-through features for easily sharing aggregate and detailed data. • Dashboard visualizations may be combined to express a larger story. • Furthermore, using Power BI Desktop, a range of report types may be created from the data and shared on the web as well as across mobile devices.
The following are the components of Power BI:
- Power BI Desktop: Power BI Desktop is a Windows application that allows you to build reports, dashboards and analyze data.
Power BI Service is the online component of Power BI where you can publish your dashboards and reports. You can also view other dashboards and reports that have been shared with you.
Power BI Mobile allows you to view any of your reporting through your phone.
The basic building blocks in Power BI are: • Visualizations • Datasets • Reports • Dashboards • Tiles
The typical flow of activities in Power BI is as follows: • Create a report using the data you imported into Power BI Desktop. • Publish to the Power BI service, where you may construct dashboards and generate new visualizations. • Share dashboards with others, especially those who are constantly on the move. • In Power BI Mobile applications, you can see and interact with shared dashboards and reports.
For anyone new to Power BI, there will likely be some unfamiliar terminology: • Power Query: A tool used to transform your data and prep it to be used in reporting • DAX: Stands for Data Analysis Expressions, and it is the language you write formulas in. • Measures: Similar to calculated fields in Excel, they allow for dynamic results based on how you interact with the report. • Interactions: The ability to click on, drill into, and slice reports to uncover additional insights. • Slicer: The equivalent to a filter. • Data Model: The ability to transform your data, connect it with other datasets, and build relationships between your data. • Power Query: For data processing & management • Power Pivot: For data modeling & calculations • Power BI Visuals: For data visualization and interactions (for example the Q&A visual, or the smart narrative visual)
The practical uses of Power BI are below: 5. Visualization = Inbuilt feature 6. Server-Level Data Management 7. Analytics With Internal Software Systems 8. Provide complex data within software and apps 9. Streamline Organizational Processes 10. Visualize Details Easily 11. Enhance the marketing 12. Real-Time look at the company’s financial performance 13. Create Consistent Reporting Standards
Human management in business systems relies heavily on data visualization. Computer programs are written in coding languages, but businesspeople must be able to view and comprehend business data. Microsoft Power BI has such features, allowing you to precisely depict critical data elements from several sources in a single dashboard. Power BI connection with Cortana allows you to see data, interact with it in your operating system, and search it using Cortana's strong AI technologies.
Data in enterprises originates from a variety of sources. Most of those sources eventually make it to the server. Power BI solutions enable you to handle all business-related data at the server level, resulting in more broad and full information systems than data acquired via an application running on a few PCs.
To manage it, your company must have access to data from the whole software system. Power BI integrates with any business management software platform. Mail management, social networking platforms, accounting software, CRMs, and classic data platforms like Azure and MySQL are all part of this. Integrated data management is made up of many dashboards for data management. Businesses that employ integrated data management do not need to use different dashboards to handle their data.
Power BI accepts data from a variety of sources and presents it in a variety of situations, including embedding it into your own apps using Microsoft's API. Furthermore, Power BI provides business management capabilities that enable app customization. It also has a product value-add capability for tracking and identifying essential data sets related to the product or service.
Departments inside firms, such as sales, marketing, operations, and human resources, require their own data sets, KPIs, and data management systems. Power BI provides enterprises with particular capability in the form of a data management system/app to streamline procedures from various departments. It allows organizations to use templates for intuitive dashboards and reporting systems rather of designing a system piecemeal for your firm. It enables your teams to control quality and efficiency without requiring software customization.
Power BI enables organizations to verify any type of detail to ensure that operations are functioning properly. While some firms manage a modest quantity of inventory, others log sales calls (made by the sales team members). These data sets are created using the Power BI algorithm and given in an easily accessible and intelligible manner.
Businesses invest much in internet marketing in order to attract clients. However, the vast majority of people fail to convert when online to find answers to their specific difficulties. In this situation, Power BI may assist you in creating a chart to follow the user's activity during their online visit.
Financial troubles have a long-term impact on your firm, especially if they occur unexpectedly. Microsoft Power BI provides insight into the functioning of companies at several levels. With Power BI, you can look at team productivity, top-selling goods, revenue generated by a certain division, and a variety of other metrics. With Power BI, you will be able to gain fast attention to significant financial decreases and resolve issues before they become a major worry.
Every firm relies on apps for its day-to-day operations, from accounting to sales. These applications do not usually have a reporting capability, and if they do, the format differs from app to app. You may use Power BI to extract data and develop reports to give corporate standards. When data is delivered in the same format and manner each time, it is less stressful for managers and takes less effort to get.
I know you might have come across this term Data Analysis before. Either through friends, family, online research, or maybe in lecture halls.
But what actually is data analysis?
Well, before we dive into that, let’s look at it individually.
Data Analysis is a two-word term which are data and analysis.
Many people over the years have defined data in terms of their environment, job description, and circumstance, etc. During my secondary school days, they taught me that data is a series of raw facts or figures which have no meaning.
Data can be a person’s name, age, date of birth, home address, name of school, or even transaction details.
But without this data being processed, it might not make any meaning to us.
It is a known fact that data is not the same as information. However, we have seen situations where individuals used both words interchangeably.
Information is a processed data that carries meaning and can be used in making intelligent business decisions.
But, how can we get information from data?
This is where analysis comes in.
Well, for us to get meaningful information from a series of data, the data must go through some processing or transformations.
Analysis helps us to carefully and adequately look into anything in other to understand its nature or to determine its essential features.
Let us now join the two words.
- Data Analysis
According to https://ori.hhs.gov/ Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.
However, you don’t just analyze any data just because it a data.
- Step 1. You must first understand the actual problem (s) you intend to solve and the right data that would enable you to solve such problem (s).
- Step 2. After identifying the problem (s) and knowing the right data to be used. Next step is to collect the data from a data source. PowerBI allows you to connect and collect data from different data sources, such as MYSQL, Excel, SharePoint etc. Most series of data have errors, are inconsistent, and also inaccurate, which leads us to step 3, which is data cleaning.
- Step 3. Data cleaning enables us to process the collected data, in other to find errors, inconsistencies, and inaccuracies, in order to get rid of them and making sure our data becomes error free and consistency in other to make a reliable analysis. PowerBI has good and effective features that would enable you to clean your data without writing any single line of code.
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Step 4. Now that our data is clean, the next step is to analyze the data to get meaningful information or insight. PowerBI also has reliable, efficient, and effective features that would help you analyze the data to find the relationships, trends, and patterns that will help you solve your problem accurately without actually writing any line of code or performing the mathematical aspect of data analysis. All you do is to click, drag and drop.
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Step 5. In this step, you are required to visualize your analysis in an image form (graphs) to help anyone to understand the information of your analysis.
- Step 6. This final step of this process is to make decisions with the insight you have gotten from the analysis.
- Conclusion
PowerBI has many tools and features which makes data analysis easy, faster, and efficient.
Power BI Desktop is built for data analysts. So, the tool empowers others with its capacity of creating and publishing reports to Power BI. As we discussed in the previous blogs, Microsoft Power BI Desktop is a companion desktop application to Power BI. Let’s discuss the installation process of Power BI Desktop.
First, you need to have a window installed on the personal computer. The latest version of Power BI Desktop is supported for the following operating systems.
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Windows 10
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Windows Server 2012 R2
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Windows Server 2012
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Windows 8
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Windows 8.1
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Windows Server 2016
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Windows Server 2019
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Windows 11
It is available for both 32-bit(x86) and 64-bit(x64) platforms. To have smooth functionality, 2GB Ram and 1GHz CPUs are recommended. Apart from that I would recommend you have at least 1440x900 or 1600x900 (16:9) display resolution. It would enhance your user experience. Unfortunately, the software is not compatible with the Linux operating system.
Step 1 : Download the version of Power BI Desktop that matches with your Windows OS.
Step 2 : Run the EXE installer and follow the setup steps.
Also, the Power BI Desktop is available for free in Microsoft Store for you to install.
After successfully installing the application, the startup screen would be like this.
At this point, you can simply sign in with your school or work account. Please remember that it only works with a Microsoft account. You will not be able to sign-in with your Gmail, yahoo.
After signing into your account, you will get the screen below where you can see your recent reports. Then that is it.
The installation is done. In this window you will find a few video tutorials in order to learn the basics that are recommended by the Power BI Desktop team. Obviously, you are free to follow any of these videos according to your preference.
After closing your startup window. You will see the interface below.
Basically, this is the user interface of the Power BI Desktop. It seems more familiar to you, if you are an office package user. It consists of report view, data view and model view. Each view has unique features.
In Power BI Desktop Report view, you can build visualizations and reports. The Report view has six main areas:
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The pages tab area at the bottom, which lets you select or add report pages.
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The canvas area in the middle, where you create and arrange visualizations.
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The Filters pane, where you can filter data visualizations.
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The Visualizations pane, where you can add, change, or customize visualizations, and apply drill through.
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The Fields pane, which shows the available fields in your queries. You can drag these fields onto the canvas, the Filters pane, or the Visualizations pane to create or modify visualizations.
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The ribbon at the top displays common tasks associated with reports and visualizations.
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The table pane, where you can see your data in table format.
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The Fields pane, which shows the available fields in your queries.
- Model view shows all of the tables, columns, and relationships in your model.
- The properties pane, which allows you to change the field properties accordingly.
- The Fields pane, which shows the available fields in your queries.
With this blog we hope you understand the big picture of the user interface of Power BI Desktop tools. By going through the whole series, you will be able to create a report which solves a given analysis problem.
With Power BI Desktop, we can:
Connect to data, including multiple data sources.
Shape the data with queries that build insightful, compelling data models.
Use data models to create visualizations and reports.
Share our report files for others to leverage, build upon, and share. We can share Power BI Desktop .pbix files like any other files, but the most compelling method is to upload them too the Power BI service.
Power BI Desktop integrates proven Microsoft query engine, data modelling, and visualization technologies. Data analysts and others can create collections of queries, data connections, models, and reports, and easily share them with others. Through the combination of Power BI Desktop and the Power BI service, new insights from the world of data are easier to model, build, share, and extend.
With Power BI Desktop installed, we're ready to connect to the ever-expanding world of data. To see the many types of data sources available, select Get Data > More in the Power BI Desktop Home tab, and in the Get Data window, scroll through the list of All data sources.
On the Power BI Desktop Home tab, select Get Data > Web to connect to a web data source.
In the From Web dialog box, paste the address into the URL field, and select OK.
If prompted, on the Access Web Content screen, select Connect to use anonymous access. The query functionality of Power BI Desktop goes to work and contacts the web resource. The Navigator window returns what it found on the web page, in this case an HTML table called Ranking of best and worst states for retirement, and five other suggested tables. You're interested in the HTML table, select it to see a preview. At this point you can select Load to load the table or Transform data to make changes in the table before you load it.
When you select Transform data, Power Query Editor launches, with a representative view of the table. The Query Settings pane is on the right, or you can always show it by selecting Query Settings on the View tab of Power Query Editor.
Now that you're connected to a data source, you can adjust the data to meet your needs. To shape data, you provide Power Query Editor with step-by-step instructions for adjusting the data while loading and presenting it. Shaping doesn't affect the original data source, only this view of the data.
Shaping can mean transforming the data, such as renaming columns or tables, removing rows or columns, or changing data types. Power Query Editor captures these steps sequentially under Applied Steps in the Query Settings pane. Each time this query connects to the data source, those steps are carried out, so the data is always shaped the way you specify. This process occurs when you use the query in Power BI Desktop, or when anyone uses your shared query, such as in the Power BI service. In the third step, changed type, Power BI recognized whole number data when importing it, and automatically changed the original web Text data type to Whole numbers.
If you need to change a data type, select the column or columns to change. Hold down the Shift key to select several adjacent columns, or Ctrl to select non-adjacent columns. Either right-click a column header, select Change Type, and choose a new data type from the menu, or drop down the list next to Data Type in the Transform group of the Home tab, and select a new data type.
You can now apply your own changes and transformations to the data and see them in Applied Steps.
In Power BI Desktop Report view, you can build visualizations and reports. The Report view has six main areas:
- The ribbon at the top, which displays common tasks associated with reports and visualizations.
- The canvas area in the middle, where you create and arrange visualizations.
- The pages tab area at the bottom, which lets you select or add report pages.
- The Filters pane, where you can filter data visualizations.
- The Visualizations pane, where you can add, change, or customize visualizations, and apply drill through.
- The Format pane, where you design the report and visualizations.
- The Fields pane shows the available fields in your queries. You can drag these fields onto the canvas, the Filters pane, or the Visualizations pane to create or modify visualizations.
You can expand and collapse the Filters, Visualizations, and Fields panes by selecting the arrows at the tops of the panes. Collapsing the panes provides more space on the canvas to build cool visualizations.
To create a simple visualization, just select any field in the fields list, or drag the field from the Fields list onto the canvas. For Example Our Group Have Made one report which is given below :
Now that you have a Power BI Desktop report, you can share it with others. There are a few ways to share your work. You can distribute the report .pbix file like any other file, you can upload the .pbix file from the Power BI service, or you can publish directly from Power BI Desktop to the Power BI service. You must have a Power BI account to be able to publish or upload reports to Power BI service. To publish to the Power BI service from Power BI Desktop, from the Home tab of the ribbon, select Publish.
You may be prompted to sign into Power BI, or to select a destination. When the publishing process is complete, you see the following dialog:
When you select the link to open the report in Power BI, your report opens in your Power BI site under My workspace > Reports.