Microsoft Power BI is a business analytics tool that enables users to visualize and share insights from their data. It provides interactive visualizations, dashboards, and reports that allow businesses to make data-driven decisions. Power BI integrates with a wide range of data sources, including cloud-based and on-premises databases, Excel spreadsheets, and third-party applications, making it highly versatile. Users can create real-time dashboards and share reports across teams or publish them to the web. The tool also supports data transformation, cleaning, and modeling, enabling users to prepare data for analysis without the need for complex coding. Power BI’s use cases span various industries, including finance, healthcare, marketing, and retail. It is widely used for sales performance analysis, financial reporting, customer insights, and operational efficiency monitoring, helping organizations gain actionable insights, track KPIs, and optimize processes.

What is Microsoft Power BI?

Microsoft Power BI is a cloud-based suite of business analytics tools designed to provide interactive visualizations and business intelligence capabilities. It empowers users to connect to various data sources, prepare and analyze data, and create rich dashboards and reports that can be shared across the organization. Power BI is accessible via a desktop application, a web-based service, and mobile apps.

Key Characteristics:

• User-Friendly: Intuitive drag-and-drop interface for building reports and dashboards.
• Extensive Data Integration: Connects to hundreds of on-premises and cloud-based data sources.
• Cloud-Based: Enables real-time access to reports and dashboards from anywhere.

Top 10 Use Cases of Microsoft Power BI

1. Sales and Revenue Analysis: Track sales performance, identify revenue trends, and forecast future sales based on historical data.
2. Customer Insights: Analyze customer behavior, preferences, and feedback to improve customer engagement and retention.
3. Financial Reporting: Generate dynamic financial reports, including income statements, balance sheets, and cash flow analyses.
4. Supply Chain Management: Monitor supply chain metrics, optimize inventory levels, and track supplier performance in real time.
5. Marketing Analytics: Measure campaign effectiveness, track marketing ROI, and identify audience demographics for targeted strategies.
6. Human Resources Analytics: Analyze employee performance, track recruitment metrics, and monitor workforce diversity and turnover rates.
7. Operations Management: Streamline operations by monitoring key performance indicators (KPIs) and identifying inefficiencies.
8. Healthcare Analytics: Use Power BI to track patient outcomes, analyze clinical data, and optimize resource allocation.
9. Education Analytics: Analyze student performance, track enrollment trends, and evaluate the effectiveness of teaching strategies.
10. Project Management: Monitor project progress, manage budgets, and ensure timely delivery using real-time dashboards.

Features of Microsoft Power BI

1. Data Connectivity: Connects to over 100 data sources, including Excel, SQL Server, Azure, Salesforce, and Google Analytics.
2. Interactive Dashboards: Create visually appealing, interactive dashboards with customizable visuals.
3. Natural Language Queries: Allows users to ask questions in natural language and get instant insights.
4. AI-Powered Analytics: Offers AI capabilities, such as predictive analytics and anomaly detection.
5. Real-Time Analytics: Provides real-time data streaming and live dashboards for instant decision-making.
6. Collaboration: Share dashboards and reports securely with team members and stakeholders.
7. Mobile Accessibility: Access dashboards and reports on mobile devices with the Power BI mobile app.
8. Integration with Microsoft Ecosystem: Seamlessly integrates with Microsoft Office, Azure, and Teams.
9. Data Security: Ensures data security with role-based access control and compliance with industry standards.
10. Custom Visualizations: Supports third-party custom visuals to enhance data representation.

How Microsoft Power BI Works and Architecture

1. Data Sources: Power BI connects to various data sources, including files, databases, cloud services, and APIs. Users can load data into Power BI Desktop for preparation and modeling.
2. Data Modeling: Data is cleaned, transformed, and structured using Power Query. Users can create relationships between tables, define measures, and build calculated columns.
3. Visualization: Power BI provides a drag-and-drop interface to create interactive visualizations, such as charts, graphs, and maps.
4. Publishing: Users can publish reports and dashboards to the Power BI Service, making them accessible to others in the organization.
5. Collaboration and Sharing: Power BI enables collaboration by allowing users to share insights via dashboards, apps, or embedding them into other applications.
6. Real-Time Updates: Dashboards can be updated in real time using data from IoT devices, streaming analytics, or other live data feeds.

How to Install Microsoft Power BI

Microsoft Power BI is primarily a graphical, interactive tool used for business analytics and reporting, and it does not have a “code-based” installation like typical Python or command-line software. However, there are options to integrate and automate tasks in Power BI programmatically, using APIs or through tools like Power BI Desktop for creating reports and Power BI Service for sharing them.

Installation of Power BI Desktop (For Local Use)

1. Download Power BI Desktop:
   • Go to the Power BI Download page.
   • Download and install the appropriate version of Power BI Desktop for your operating system.
   • You can also find Power BI Desktop in the Microsoft Store for Windows.
2. Launch Power BI Desktop:
   • After installation, open Power BI Desktop from your applications or Start menu and begin using it to create reports, dashboards, and visualizations.

Programmatic Use and Automation (Power BI REST API)

Power BI has extensive APIs that allow programmatic access to datasets, reports, dashboards, and more. Here’s how you can interact with Power BI programmatically using Python.

1. Install Python Packages for Power BI Integration:

• Install the requests library for making HTTP requests, which are commonly used for interacting with APIs, including Power BI.

pip install requests

2. Power BI REST API Authentication: Power BI uses OAuth 2.0 for authentication, so you need to set up an Azure Active Directory (AAD) application to authenticate and interact with the API. Steps:

• Go to Azure Portal and register a new application under Azure Active Directory.
• Grant the application permissions for Power BI by assigning the correct API permissions.
• Obtain your Application (Client) ID, Tenant ID, and Client Secret.

3. Use Power BI REST API (Python Example): Once you’ve set up your Azure app, you can use Python to interact with Power BI through its REST API.

Here’s an example of how to authenticate and retrieve a list of reports:

import requests
import json

# Set your credentials
tenant_id = "YOUR_TENANT_ID"
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"
resource_url = "https://analysis.windows.net/powerbi/api"
api_url = "https://api.powerbi.com/"

# Get the OAuth token
auth_url = f"https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token"
body = {
    "client_id": client_id,
    "client_secret": client_secret,
    "scope": resource_url + "/.default",
    "grant_type": "client_credentials"
}
headers = {
    "Content-Type": "application/x-www-form-urlencoded"
}
response = requests.post(auth_url, data=body, headers=headers)
auth_token = response.json().get("access_token")

# Make an API request to Power BI
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {auth_token}"
}
reports_url = f"{api_url}v1.0/myorg/reports"
reports_response = requests.get(reports_url, headers=headers)

# Print the list of reports
if reports_response.status_code == 200:
    reports = reports_response.json()
    for report in reports['value']:
        print(f"Report Name: {report['name']}")
else:
    print(f"Error: {reports_response.status_code}")

• Authenticates via OAuth2 using client credentials.
• Fetches a list of reports from Power BI.
• Prints the report names.

Basic Tutorials of Microsoft Power BI: Getting Started

Step 1: Install Power BI Desktop
Download and install Power BI Desktop from the official website.

Step 2: Import Data
Use the “Get Data” option to connect to data sources like Excel, SQL Server, or online services.

Home > Get Data > Choose Source > Load

Step 3: Clean and Transform Data
Use Power Query Editor to clean and transform your data.

Transform > Remove Duplicates > Add Columns > Merge Tables

Step 4: Create Visualizations
Drag fields onto the canvas to create charts, graphs, and other visuals.

Insert > Visuals > Customize Properties

Step 5: Publish to Power BI Service
Publish your report to the Power BI Service for sharing and collaboration.

File > Publish > Publish to Power BI

Step 6: Share and Collaborate
Share your dashboards and reports with team members and schedule refreshes for updated data.

The post What is Microsoft Power BI and Its Use Cases? appeared first on Artificial Intelligence.

Introduction

In the modern IT landscape, data is being generated at an unprecedented rate. The ability to effectively analyze and visualize this data is essential for businesses to stay competitive, understand their operations, and make data-driven decisions. One of the key tools for visualizing and interacting with data, especially in the context of Elasticsearch, is Kibana.

Kibana is a powerful open-source data visualization tool designed to work with Elasticsearch. It provides a user-friendly interface to search, view, and analyze data stored in Elasticsearch indexes. With its real-time data processing and interactive dashboards, Kibana makes it easier for businesses to identify trends, monitor systems, and gain insights from their data. In this blog post, we will explore what Kibana is, its top 10 use cases, its features, how Kibana works, the installation process, and provide a basic tutorial to help you get started with Kibana.

What is Kibana?

Kibana is an open-source data visualization platform that works in conjunction with Elasticsearch to analyze large volumes of data. It is part of the Elastic Stack (formerly known as the ELK Stack), which consists of Elasticsearch, Logstash, and Kibana. Kibana provides an easy-to-use interface for interacting with the data stored in Elasticsearch indices and allows users to create custom dashboards, graphs, and reports.

Kibana enables users to explore data visually using interactive charts, graphs, and maps. It supports real-time data processing, enabling businesses to monitor systems and applications, perform log analysis, track performance metrics, and analyze large data sets in a meaningful way. Kibana is widely used in various industries for IT operations, security monitoring, business intelligence, and data analytics.

Top 10 Use Cases of Kibana

Kibana’s versatile capabilities make it applicable across a wide range of industries and use cases. Here are the top 10 ways businesses and organizations can use Kibana:

1. Log Analysis and Management

One of the most common uses of Kibana is for log management. Organizations can ingest log data into Elasticsearch and use Kibana to search, visualize, and analyze log data in real-time. This helps detect anomalies, troubleshoot issues, and monitor system health.

2. Monitoring and Operational Dashboards

Kibana is frequently used to create dashboards that display real-time metrics related to system performance, server health, and application uptime. IT teams use Kibana to monitor infrastructure components such as servers, networks, and cloud services.

3. Security Information and Event Management (SIEM)

Kibana is a powerful tool for security monitoring, especially when used as part of a SIEM system. Security teams use Kibana to analyze security logs, monitor network traffic, detect security incidents, and visualize attack patterns. Kibana helps organizations maintain proactive security postures and mitigate risks.

4. Business Intelligence and Analytics

Business analysts use Kibana to analyze large sets of business data, such as sales data, customer feedback, or operational performance. Kibana’s visualization capabilities help users create interactive reports and dashboards to uncover business trends and inform decision-making.

5. Application Performance Monitoring

With Kibana, developers can visualize and monitor application performance in real-time. By integrating with Elasticsearch, Kibana helps track metrics such as response time, error rates, throughput, and more, enabling businesses to optimize their applications and enhance user experiences.

6. Data Exploration and Ad-Hoc Queries

Kibana allows users to perform ad-hoc queries on the data stored in Elasticsearch. This is especially useful for data analysts who need to explore data on the fly, identify patterns, and draw insights without requiring complex SQL queries or database configurations.

7. Infrastructure Monitoring and Capacity Planning

Kibana is often used for infrastructure monitoring, helping IT teams track hardware and software resource utilization, network traffic, and system performance. Kibana helps businesses plan for capacity by providing insights into resource usage trends, enabling informed scaling decisions.

8. Customer Insights and Experience Analysis

By visualizing customer-related data such as behavior, transactions, and interactions, organizations can use Kibana to analyze customer journeys, preferences, and pain points. This enables businesses to improve customer experience and personalize marketing strategies.

9. IoT Data Visualization

With the rise of the Internet of Things (IoT), Kibana is used to visualize data generated by IoT devices, such as sensors, wearables, or smart devices. Kibana’s ability to handle large datasets allows businesses to monitor and visualize real-time IoT data, facilitating proactive management of IoT networks.

10. Fraud Detection and Risk Management

Financial institutions and e-commerce platforms use Kibana to detect fraudulent activities by analyzing transactional data, user behavior, and patterns. Kibana can visualize suspicious activities and alert security teams, helping reduce fraud and manage financial risks.

What Are the Features of Kibana?

Kibana is packed with features that make it a powerful data visualization tool. Some of its key features include:

• Interactive Dashboards: Create dynamic dashboards to visualize data with various types of charts, graphs, maps, and tables.
• Real-Time Data Processing: Kibana supports real-time data analysis, allowing you to view live data and monitor ongoing events.
• Custom Visualizations: Build custom visualizations using a wide range of chart types, such as pie charts, bar charts, line graphs, heat maps, and geographical maps.
• Search and Query Capabilities: Kibana offers advanced querying capabilities, including full-text search, filters, and aggregations, to explore and analyze data.
• Elastic Stack Integration: Kibana seamlessly integrates with Elasticsearch, Logstash, and Beats, enabling a comprehensive data analysis and monitoring solution.
• Machine Learning: Kibana supports machine learning features for anomaly detection, forecasting, and trend analysis, helping organizations make predictive decisions.
• Alerting: Kibana includes alerting features that notify users of critical events, such as system failures, security breaches, or performance issues.
• Security and Access Control: Kibana allows for role-based access control, ensuring that sensitive data is accessible only to authorized users.
• Geospatial Analysis: Kibana’s support for geospatial data allows you to visualize geographic information, such as customer locations or sales territories, on interactive maps.
• Timelion: Kibana includes Timelion, a powerful time-series analysis tool that helps visualize time-based data trends and patterns.

How Kibana Works and Architecture

Kibana is part of the Elastic Stack and works in conjunction with Elasticsearch to provide data visualization and analysis capabilities. The architecture of Kibana can be broken down into the following components:

1. Elasticsearch: At the core of Kibana is Elasticsearch, a distributed search and analytics engine that stores, indexes, and processes large volumes of data. Kibana interacts with Elasticsearch to query and visualize the data stored in its indexes.
2. Kibana Interface: The Kibana user interface (UI) is web-based, allowing users to interact with Elasticsearch data through visualizations and dashboards. Users can create charts, graphs, and reports by querying data stored in Elasticsearch.
3. Logstash and Beats: Data collected by Logstash (a data processing pipeline) and Beats (lightweight data shippers) is sent to Elasticsearch, where it can be indexed and processed. Kibana then retrieves and visualizes this data.
4. Plugins: Kibana supports plugins that can extend its functionality. Popular plugins include those for machine learning, alerting, security, and reporting.
5. Data Exploration: Kibana allows users to explore data interactively by drilling down into individual data points, using filters, and aggregating data into various formats.

How to Install Kibana?

Installing Kibana is straightforward and can be done on a local machine, server, or cloud platform. Here’s how you can install Kibana:

1. Download Kibana:
   • Go to the Kibana download page and select the version of Kibana compatible with your system (Windows, macOS, or Linux).
2. Install Elasticsearch:
   • Kibana requires Elasticsearch to work, so you will need to have Elasticsearch installed and running. You can download Elasticsearch from the Elastic website.
3. Install Kibana:
   • For Linux systems, use the package manager (e.g., APT, YUM) to install Kibana.
   • For Windows or macOS, you can run the installer directly from the Kibana download page.
4. Start Kibana:
   • Once installed, start Kibana by running the following command in the terminal: ./bin/kibana
   • Kibana will start a local server (usually on port 5601). Access the Kibana UI by visiting http://localhost:5601 in your web browser.
5. Configure Kibana:
   • After launching Kibana, you may need to configure it to connect to your Elasticsearch instance by editing the kibana.yml configuration file.

Basic Tutorials of Kibana: Getting Started

Here are some basic steps to help you get started with Kibana:

1. Creating Your First Visualization:

• Log into Kibana and go to the Visualize tab.
• Select the type of visualization you want to create (e.g., bar chart, pie chart).
• Choose an Elasticsearch index pattern and configure the data source (fields) for your visualization.
• Customize the visualization to suit your needs and save it.

2. Building a Dashboard:

• After creating visualizations, go to the Dashboard section.
• Click “Create new dashboard” and add your saved visualizations to it.
• Arrange the visualizations as desired and save the dashboard.

3. Filtering Data:

• Use the filter bar at the top of the Kibana interface to filter data based on specific fields (e.g., dates, values, or categories).
• Apply multiple filters to refine your visualizations and dashboards.

4. Setting Up Alerts:

• In the Alerting section, create alert conditions based on thresholds for your data (e.g., when a metric exceeds a certain value).
• Configure notification channels to receive alerts via email, Slack, or other methods.

The Power of Kibana for Data Visualization

Kibana is an incredibly powerful tool for visualizing and analyzing data stored in Elasticsearch. Whether you’re monitoring system logs, analyzing business performance, or tracking security events, Kibana provides the tools you need to create meaningful visualizations and gain insights into your data. With its user-friendly interface, real-time processing capabilities, and flexible architecture, Kibana is an essential tool for any data-driven organization.

From IT operations to business intelligence and security monitoring, Kibana’s versatility allows users across various industries to leverage data visualization for better decision-making and performance optimization.

The post What is Kibana and Use Cases of Kibana? appeared first on Artificial Intelligence.

Introduction

In today’s data-driven world, the ability to analyze and visualize data is more important than ever. Businesses, organizations, and individuals rely on data insights to make informed decisions and drive strategic initiatives. However, making sense of vast amounts of raw data can be a daunting task without the right tools.

Tableau is one of the leading data visualization tools that helps users transform raw data into meaningful insights through interactive, easy-to-understand visualizations. Whether you’re a business executive, data analyst, or researcher, Tableau enables you to visualize data in ways that facilitate decision-making, performance tracking, and predictive analysis.

In this blog post, we will explore what Tableau is, the top 10 use cases for Tableau, its features, how Tableau works and its architecture, the process to install Tableau, and provide you with basic tutorials to get started with Tableau.

What is Tableau?

Tableau is a powerful data visualization tool that enables businesses and individuals to analyze and visualize data in a dynamic, interactive way. It offers a wide array of features that allow users to create visually appealing dashboards, reports, and charts to gain deeper insights into their data. Tableau can connect to a variety of data sources, including spreadsheets, databases, cloud services, and big data platforms, allowing users to work with data from multiple systems in real time.

Tableau allows users to drag and drop data fields to create interactive visualizations, such as bar charts, pie charts, line graphs, heat maps, and more. These visualizations help to communicate complex insights quickly and efficiently, making data analysis more accessible to non-technical users.

Tableau comes in different versions, including Tableau Desktop (for individual use), Tableau Server (for sharing visualizations in an organization), and Tableau Online (a cloud-based version for collaboration).

Top 10 Use Cases of Tableau

Tableau’s versatility makes it applicable in a wide range of industries and business functions. Here are the top 10 use cases where Tableau excels:

1. Business Intelligence (BI) and Reporting

Tableau is widely used for business intelligence, as it allows organizations to collect, analyze, and visualize large amounts of data. Users can create interactive dashboards and reports to monitor business performance, track key metrics, and make data-driven decisions. Tableau simplifies the process of creating reports, making it accessible even for non-technical users.

2. Financial Analysis and Planning

Financial professionals use Tableau to visualize financial data, track performance, and forecast trends. Tableau helps analyze key financial metrics such as revenue, expenses, profits, and cash flow. Interactive visualizations enable financial analysts to identify patterns, conduct variance analysis, and perform scenario planning.

3. Sales and Marketing Analytics

Sales and marketing teams use Tableau to track key performance indicators (KPIs) such as customer acquisition, lead conversion rates, marketing campaigns’ effectiveness, and sales pipeline performance. Tableau helps businesses identify target audiences, optimize sales strategies, and monitor the success of marketing efforts by visualizing customer data, trends, and outcomes.

4. Supply Chain and Operations Management

In supply chain management, Tableau is used to track inventory, monitor logistics, and improve operational efficiency. Businesses can create visualizations that show real-time data on shipments, delivery times, inventory levels, and supplier performance. Tableau helps optimize supply chain processes and improve decision-making related to procurement, demand forecasting, and production planning.

5. Customer Segmentation and Behavior Analysis

Tableau is used to analyze customer data to identify key segments and target groups. Businesses can visualize customer preferences, purchasing behaviors, and engagement patterns. This helps in tailoring marketing strategies, personalizing offers, and enhancing customer satisfaction by understanding their needs and demands.

6. Healthcare Data Analysis

Healthcare providers and organizations use Tableau to analyze patient data, track healthcare outcomes, and monitor key performance indicators (KPIs). Visualizing patient care data, hospital operations, and treatment efficacy helps improve patient outcomes, optimize hospital performance, and enhance healthcare delivery.

7. Human Resources (HR) Analytics

HR professionals use Tableau to monitor workforce data, such as employee performance, turnover rates, and hiring trends. Tableau can help create visualizations that track employee satisfaction, analyze compensation and benefits data, and assess the effectiveness of HR policies and recruitment strategies.

8. Government and Public Sector Analytics

Governments and public sector organizations use Tableau for policy analysis, resource allocation, and data transparency. Tableau enables governments to track social services, crime statistics, health outcomes, and public safety measures. Data visualizations help in communicating information to the public, driving policy decisions, and improving public services.

9. Big Data Analytics

Tableau integrates with big data platforms, such as Hadoop, to help users analyze and visualize vast amounts of structured and unstructured data. Tableau helps make sense of complex data sets by visualizing them in a digestible format, allowing businesses to uncover trends, insights, and hidden patterns within big data.

10. Predictive Analytics and Forecasting

Tableau is often used for predictive analytics, enabling users to forecast future trends, behaviors, and outcomes. By integrating with statistical models and machine learning tools, Tableau allows businesses to visualize predictions related to sales, customer behavior, market trends, and more.

What Are the Features of Tableau?

Tableau is packed with powerful features that make it an industry leader in data visualization. Some of the key features include:

• Drag-and-Drop Interface: Easily build interactive dashboards and visualizations without any coding knowledge.
• Data Connectivity: Connects to a wide range of data sources, including Excel, SQL databases, cloud services, and big data platforms.
• Interactive Dashboards: Create dashboards with interactive filters, drill-downs, and high-level views, allowing users to explore data.
• Real-Time Data Processing: Tableau supports live data connections and real-time analytics, providing up-to-date insights.
• Data Blending: Combine data from multiple sources to get a comprehensive view of your business.
• Advanced Analytics: Tableau supports calculations, trend lines, forecasting, and regression analysis, providing advanced analytics capabilities.
• Collaboration Features: Share dashboards and reports securely with others using Tableau Server or Tableau Online.
• Mobile Access: Tableau provides mobile apps that allow users to view and interact with dashboards on smartphones and tablets.
• Storytelling: Create data-driven stories with your visualizations to guide users through key insights.
• Custom Visualizations: Build custom visualizations and integrate third-party extensions to meet specific business needs.

How Tableau Works and Architecture

Tableau’s architecture is designed for both individual users and organizations looking for enterprise-grade analytics solutions. The architecture can be broken down into the following components:

1. Tableau Desktop: The desktop application used by individual users for building visualizations and dashboards. It allows users to connect to data sources, prepare data, and create visualizations.
2. Tableau Server: A centralized platform for sharing and collaborating on Tableau workbooks and dashboards. It allows multiple users to access and interact with reports created in Tableau Desktop.
3. Tableau Online: A cloud-based version of Tableau Server, allowing users to share dashboards and collaborate in a web-based environment.
4. Tableau Public: A free version of Tableau that allows users to create and share visualizations online. It is mainly used for public data projects and educational purposes.
5. Tableau Data Engine: Tableau’s in-memory data engine that enables fast data processing and analytics. It works by optimizing data queries and processing large datasets efficiently.
6. Data Connectors: Tableau supports numerous connectors to various data sources, including relational databases, cloud services, and big data platforms, ensuring easy data access and integration.

How to Install Tableau?

To install Tableau on your system, follow these steps based on the version you are using (Tableau Desktop, Tableau Server, or Tableau Public). Here, we’ll cover the installation process for Tableau Desktop.

Step 1: Download Tableau

1. Visit the Tableau Website:
   • Go to the Tableau download page to get the installation file.
2. Choose Your Version:
   • If you’re a new user, you can download the free trial version of Tableau Desktop for 14 days.
   • If you already have a Tableau account, you can download the latest stable release or access a previous version through the customer portal.
3. Sign Up or Log In:
   • If prompted, sign up for a Tableau account, or log in if you already have one.

Step 2: Install Tableau Desktop

1. Run the Installer:
   • After downloading the setup file, double-click on the installer file to run it.
   • For Windows, the file will typically be named something like TableauDesktop-installer.exe.
   • For Mac, the file will be a .dmg file.
2. Follow the Installation Wizard:
   • For Windows:
     • The installation wizard will guide you through the setup process. Click Next to proceed with the default installation options.
     • Select the installation folder and click Install. It will take a few minutes for the installation to complete.
   • For Mac:
     • Open the .dmg file and drag the Tableau Desktop application into your Applications folder.
3. Complete the Installation:
   • Once installed, click Finish on the installer for Windows or close the installation window for Mac.

Step 3: Activate Tableau

1. Launch Tableau Desktop:
   • Open Tableau Desktop by clicking on the application icon on your desktop or in the Start Menu (Windows) or Applications Folder (Mac).
2. Activate Your Product:
   • Upon opening Tableau, you will be prompted to enter an activation key.
   • If you have purchased Tableau, you should have received an activation key via email. Enter the key to activate Tableau.
   • If you’re using the free trial, Tableau will automatically start the trial version.
3. Sign In to Your Tableau Account (Optional):
   • You can also sign in to your Tableau Account to synchronize your settings and access Tableau Server or Tableau Online resources.

Step 4: Start Using Tableau

1. Begin Your First Project:
   • Once activated, you can start using Tableau to connect to your data sources, create visualizations, and analyze your data.
   • You can connect to various data sources like Excel, SQL databases, Google Analytics, and many more.
2. Explore Tableau’s Learning Resources:
   • Tableau provides a variety of tutorials and training materials to help you get started.
   • You can access these resources directly from the Tableau Help menu or via the Tableau Learning website.

Step 5: Keep Tableau Updated

1. Check for Updates:
   • To ensure you’re using the latest version of Tableau, periodically check for updates. Tableau will usually notify you when a new version is available.
   • You can also manually check for updates by going to Help > Check for Updates in the Tableau Desktop menu.

Basic Tutorials of Tableau: Getting Started

Here are some basic tutorials to help you get started with Tableau:

1. Creating Your First Dashboard:

• Open Tableau and connect to your data source.
• Drag fields from the data pane onto the rows and columns shelves to create visualizations.
• Use the “Show Me” panel to choose the best visualization type for your data (e.g., bar chart, line graph, heatmap).
• Create a dashboard by dragging multiple visualizations onto a single canvas.

2. Adding Filters to Your Dashboard:

• Add filters to your dashboard to allow users to interact with the data and focus on specific subsets.
• Drag a field (e.g., date, region, product category) to the “Filters” shelf and configure how the filter should behave.

3. Building Calculated Fields:

• Create calculated fields to perform custom calculations based on your data.
• Go to the data pane, right-click, and select “Create Calculated Field.” Use Tableau’s formula editor to write your calculation.

4. Sharing Your Tableau Visualizations:

• Once you’ve built your visualizations and dashboards, you can share them by publishing to Tableau Server or Tableau Online.
• Alternatively, export your dashboard as a PDF or image file to share with others.

The post What is Tableau and Use Cases of Tableau? appeared first on Artificial Intelligence.

By using deep learning and predictive analytics, researchers have determined who could develop age-related chronic disease based on immune system health.

Researchers from the Buck Institute and Stanford University have created an inflammatory clock for aging (iAge) that uses deep learning and predictive analytics to determine immunological health and chronic diseases associated with aging. By utilizing artificial intelligence technology, researchers studied the blood immunome of 1001 people.

The team of researchers also discovered a modifiable chemokine associated with cardiac aging. This chemokine can be used for early detection of age-related pathology and can help provide targets for interventions.

“Standard immune metrics which can be used to identify individuals most at risk for developing single or even multiple chronic diseases of aging have been sorely lacking,” David Furman, PhD, Buck Institute Associate Professor, Director of the 1001 Immunomes Project at Stanford University School of Medicine and senior author of the study said in a press release.

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“Bringing biology to our completely unbiased approach allowed us to identify a number of metrics, including a small immune protein which is involved in age-related systemic chronic inflammation and cardiac aging. We now have means of detecting dysfunction and a pathway to intervention before full-blown pathology occurs,” Furman continued.

According to first author Nazish Sayed, MD, PhD, Assistant Professor of Vascular Surgery at Stanford Medicine, the study highlights the soluble chemokine CXCL9 as the major contributor to iAge. Furman describes CXCL0 as a small immune protein typically called to action to attract lymphocytes to infection sites.

“But in this case we showed that CXCL9 upregulates multiple genes implicated in inflammation and is involved in cellular senescence, vascular aging and adverse cardiac remodeling,” Furman stated then added that silencing CXCL9 reversed the loss of function in aging endothelial cells in humans and mice.

According to Furman, the age of one’s immune system provides important information regarding health and longevity.

“On average, centenarians have an immune age that is 40 years younger than what is considered ‘normal’ and we have one outlier, a super-healthy 105 year-old man (who lives in Italy) who has the immune system of a 25 year old,” he said.

 Results for the initial analysis and the cardiac health study were able to be validated. Additionally, Furman said that the researchers found a correlation between CXCL9 and the results from the pulse wave velocity testing.

“These people are all healthy according to all available lab tests and clinical assessments, but by using iAge we were able to predict who is likely to suffer from left ventricular hypertrophy (an enlargement and thickening of the walls of the heart’s main pumping chamber) and vascular dysfunction,” Furman said.

These artificial intelligence tools can be used to track a patient’s risk of developing multiple chronic diseases by assessing the total physiological damage done to their immune system.

Predictive analytics of age-related frailty can be determined by comparing biological immune metrics to information about how long it takes an individual to perform a task, such as standing up from a chair or walking a certain distance.

“Using iAge it’s possible to predict seven years in advance who is going to become frail,” Furman said. “That leaves us lots of room for interventions.”

In 2013, a group of researchers conducted a study on aging and identified nine “hallmarks” in the process. Age-related immune system dysfunction was not one of them.

“It’s becoming clear that we have to pay more attention to the immune system with age, given that almost every age-related malady has inflammation as part of its etiology,” said Furman.

“If you’re chronically inflamed, you will have genomic instability as well as mitochondrial dysfunction and issues with protein stability. Systemic chronic inflammation triggers telomere attrition, as well as epigenetic alterations. It’s clear that all of these nine hallmarks are, by and large, triggered by having systemic chronic inflammation in your body. I think of inflammation as the 10th hallmark,” Furman concluded.

The post Deep Learning, Predictive Analytics Helps Identify Chronic Diseases appeared first on Artificial Intelligence.

A deal to buy data analytics and data management leader SAS, a privately held company with annual sales around $3 billion, could be valued as high as $15 billion to $20 billion.

Hardware and software infrastructure vendor Broadcom is in talks to acquire privately held business analytics and data management software giant SAS Institute in a deal potentially valued at $15 billion to $20 billion, according to a Wall Street Journal report.

A deal could be reached in the next few weeks, according to the report, but is by no means assured.

An acquisition of SAS would the latest of several major acquisitions by Broadcom, a San Jose, Calif.-based semiconductor manufacturer that has moved in recent years to become a supplier of IT hardware and software infrastructure technology.

Broadcom acquired infrastructure software vendor CA Technologies in November 2018 for $18.9 billion and bought cybersecurity software developer Symantec Enterprise in November 2019 for $10.7 billion. Broadcom also bought network systems manufacturer Brocade Communications Systems in 2017 for $5.5 billion.

SAS, based in Cary, N.C., in the Research Triangle Park/Raleigh-Durham area, is one of the IT industries’ biggest developers of business analytics, AI and data management software.

SAS (SAS stands for Statistical Analysis System) grew out a data analysis software project at North Carolina State University where the company’s founders, including current CEO James Goodnight, were professors and students.

The company was founded in 1976 and has expanded into a broad product line of data analytics and data management offerings from its flagship SAS Viya visual analytics software, to data integration and governance tools, to Internet of Things analytics software, and business analytics applications for specific industries and tasks such as fraud detection and risk assessment in financial services.

The company’s founders have retained ownership of privately held SAS – Goodnight has a two-thirds stake in the company while co-founder and executive vice president John Sall owns one-third, according to a company fact page.

SAS doesn’t provide detailed financial data about its operations, although the company did issue an annual report earlier this year that disclosed that its revenue in 2020 was $3 billion.

SAS currently has 12,545 employees, according to the company’s website, with about 5,200 at its Cary headquarters.

A SAS spokesperson declined to comment on the WSJ report while Broadcom did not respond to a request for comment.

The post Broadcom In Talks To Buy Big Data Analytics Giant SAS Institute: Report appeared first on Artificial Intelligence.

With demand gains M.Sc. in Big Data Analytics in India

Big data analytics is gaining more popularity among data science students day by day. It is one of the most sought-after career options in the advancing technological era. With companies thriving to convert data into information and information into valuable insights, knowledge of big data analytics could be ever beneficial in catering to the needs of the increasing data. This also demands many professionals with big data analytics skills and expertise. Colleges are offering M.Sc. in Big Data Analytics to prepare students better to handle data and businesses. By enrolling in this program, students can be equipped to sustain longer in the market of the data world. Such programs also help students to improve their business decisions and professional skills making them industry-ready. Here are the top 7 colleges offering M.Sc. in Big Data Analytics in India.

1. St. Xavier’s College (Autonomous)-Ahmedabad

Program: M.Sc. in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 90,000 per year

The M.Sc. in Big Data Analytics in India program offered by St. Xavier’s College can help in the overall development of the student by enhancing the quality and consistency of talent. The course has gained great admiration from academia, students, and various stakeholders across India. This has been developed by former Director of Indian Statistical Institute, Padma Shri Dr. Bimal Roy. The courses consist of Statistical Methods, Probability & Stochastic Process, Linear Algebra & Linear Programming, Computing for Data Sciences, and Data Management. The program is a collaborative effort of the Department of Computer Sciences, Mathematics, and Statistics. The cut-off is 50% with relevant courses in the Bachelors.

2. Central University of Rajasthan

Program: M.Sc. in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 37,745

The Department of Data Sciences and Analytics aims to train students in the rapidly growing field of Data Science, to be a vibrant research hub in this area, and interact with other disciplines to evaluate, analyze and utilize the knowledge. The department came up in this course in August 2018, with the partnership with Tata Consultancy Service came up with M.Sc. in Big data analytics in India. The average cut-off is 50% with a bachelor’s in mathematics as one of the subjects or B.Tech in computer sciences.

3. Ramakrishna Mission Vivekananda University-Howrah

Program: M.Sc. in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 47,500

Ramakrishna Mission Vivekananda Educational and Research Institute along with Tata Consultancy Services has first launched a Master’s level degree course in M.Sc. Big Data Analytics in India was designed by TCS. The postgraduate program spans four semesters. The content will be Mathematics, Statistics, Economics, Computer Science and their application in Big Data Analytics. The students can learn the fundamentals of data modeling, migration, manipulation, cleansing, and so on. It can help them learn business functions and analytics in various streams from Finance to Retail to Pharma. The fourth semester will be a full-time internship in the area of Big Data Analytics at any reputed organization for gaining experience and practical knowledge. The cut-off is 50% with relevant courses in the Bachelors.

4. St. Joseph College- Bangalore

Program: M.Sc. in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 1,25,000

The Department of Big Data Analytics was established in the year 2016-17 is committed to training students to analyze big data and to arrive at valuable insights in the domain. The course was designed to equip students with a Mathematical foundation, statistical and machine learning theories for analytics. Graduates with a mathematics or statistics background with 40% in the cognate subject and 50% marks are eligible.  The focus areas of this course are a mathematical foundation, statistical and machine learning theories for analytics, engineering aspects of analyzing huge volumes of data, and application of analytics in functional areas. The three semesters each of 4 months are classroom-based, and the final semester is for an internship in the industry for a better experience.

5. St. Xavier’s College- Mumbai

Program: M.Sc. in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 90,000 per year

The MSc in Big Data Analytics in India program offered by St. Xavier’s College can help in the overall development of the student by enhancing the quality and consistency of talent. The course has gained great admiration from academia, students, and various stakeholders across India. This has been developed by former Director of Indian Statistical Institute, Padma Shri Dr. Bimal Roy. The courses consist of Statistical Methods, Probability & Stochastic Process, Linear Algebra & Linear Programming, Computing for Data Sciences, and Data Management. The program is a collaborative effort of the Department of Computer Sciences, Mathematics, and Statistics. The cut-off is 50% with relevant courses in the Bachelors.

6. MIT World Peace University- Pune

Program: MSc in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 4.50 Lakh

Master of Science in Data Science and Big Data Analytics is a two-year full-time program. This is a niche program that focuses on Data Science and Big data tools and technologies. The course builds on the strength and innovation power of Science & Engineering. The program ensures to provide students with the flexibility to focus on Big Data Tools & technologies and Data Sciences. The program provides continuous guidance by the industry experts to bridge the gap between Industry & Academia. The cut-off is 50% with relevant courses in the Bachelors.

7. St. Aloysius College (Autonomous)- Kerala

Program: M.Sc. in Big Data Analytics

Duration: 2years

Type: Full-time

Fee: INR 4,100 to INR 3,00,000

M.Sc. Big Data Analytics has been developed to create Postgraduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. The course is aimed to help the learner to develop the skills and expertise needed to face the industry world. The program splits into three areas: statistics, computing, and management. The eligible percentage is 50%.

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The organization leverages big data analytics for effective customer experience.

With digital transformation and innovation, access to a huge pile of information as well as data which includes buying behaviour and preferences of customers has become easily available. Unlike traditional CRM information, data is now collected increasingly from blogs, social media posts through the use of smartphones and other digital sources. This varied information and data are known as big data.

Modern technology serves in providing tools to all industries that enhance operations leading to maximization of revenue and minimization of waste. As a result, many industries now have access to updated information about customer experience, financial transactions, product acceptance. Decision-makers can view, analyse and reply in real-time.

Unlike traditional data, big data is unstructured and distributed, and it is impossible for traditional SQL databases to manage and control those data. Therefore, big data analytics are required to evaluate and measure a huge pile of information collected from customers.

Big data analytics, is a method of advanced analytics, which includes multifaceted applications with elements such as predictive models, statistical algorithms powered by analytics systems.

Organizations can make use of big data analytics to make informed data-driven decisions that can result in an improved business-related outcome. This can lead to more effective and efficient marketing, customer personalization, and new revenue opportunities.

How to make use of big data analytics for better customer experience-

Build a data centre

Collecting all information and data and assembling them into one place creates a clear, enterprise-wide data layer giving prominence to all departments. This enables organizations to make informed choices for customers and employees. Big data analytics can simplify and transmute customer and company relationships. Using data analytics helps the organization to know their customers better and this helps in minimizing or rather bridging the gap in their marketing strategies.

Identify customers effectively through data

Building a strong data platform and using big data analytics helps in gaining access to all the information about the customers which leads to effective identification of customers. Also, it helps in collecting customer feedback which is useful for product innovation and market strategy. Through big data, analytics organizations can build loyalty towards the customer as well as respond to their complaints. This will also help in tracking marketing trends.

Advancing talent recruitment and retention

Accessing functional data helps in fuelling improvements in the field of hiring and getting candidates more effectively and efficiently. This helps in reducing hiring time, decreases costs, and also enhances candidate’s and company’s experience. Big data analytics helps in the growth of a company’s digital and analytical models. It is important to put more employees at the forefront to make use of big data analytics to access data which will allow them to make effective decisions for the organization as well as customers.

Access to personal information

Big data analytics helps in collecting personal information (gathered through big data) about a customer, thus helps in interacting with the customer in a meaningful and productive way. Thus, this level of customer personalization in brand communications can be expected.

Track customer behaviour and motivation

Big data analytics can be used to find out customer sentiments and behaviour. Through big data analytics, it is also easy to track customer experience and review which enables an organization to make an informed decision about customer behaviour.

No matter what industry or organization it is, big data analytics are used to find a way to improve market strategies by getting access to a huge pile of data through social media posts, blogs, etc. With more data available than ever before, big data analytics is used to improve and enhance customer experience.

The post IMPLEMENTATION OF BIG DATA ANALYTICS FOR BETTER CUSTOMER EXPERIENCE appeared first on Artificial Intelligence.

A new machine learning algorithm system uses predictive analytics to determine which transcription factors are active in individual cells.

Scientists at the University of Illinois Chicago have introduced a new system that uses a machine learning algorithm and predictive analytics to find what transcription factors are most likely to be active in individual cells. The system was created to provide researchers with a more efficient method of identifying the regulators of genes.

Transcription factors are proteins that bind to DNA and have control around what genes are active inside a cell. Understanding and manipulating these signals in a cell is crucial to the biomedical field. Additionally, using this method of manipulating signals within a cell has proven to be an effective way to discover new treatments and illnesses.

However, there are hundreds of transcription factors inside a human cell. It could take years of research, and lots of trial and error, to determine the most active factor.

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“One of the challenges in the field is that the same genes may be turned ‘on’ in one group of cells but turned ‘off’ in a different group of cells within the same organ,” Jalees Rehman, UIC professor in the department of medicine and the department of pharmacology and regenerative medicine at the College of Medicine, said in a press release.

“Being able to understand the activity of transcription factors in individual cells would allow researchers to study activity profiles in all the major cell types of major organs such as the heart, brain or lungs,” Rehman continued.

The system developed by the University of Illinois Chicago is named BITFAM, standing for Bayesian Inference Transcription Factor Activity Model. The machine learning algorithm system operates by “combining new gene expression profile data gathered from single cell RNA sequencing with existing biological data on transcription factor target genes,” UIC stated in a press release.

With all the information, the system will run multiple computer-based simulations to find the best fit and predict the activity for every transcription factor in the cell.

The system was tested on cells from tissue in the lung, heart, and brain by Rehman and fellow UIC researcher Yang Dai, UIC associate professor in the department of bioengineering at the College of Medicine and the College of Engineering.

“Our approach not only identifies meaningful transcription factor activities but also provides valuable insights into underlying transcription factor regulatory mechanisms,” Shang Gao, first author of the study and a doctoral student in the department of bioengineering said in a press release.

“For example, if 80% of a specific transcription factor’s targets are turned on inside the cell, that tells us that its activity is high. By providing data like this for every transcription factor in the cell, the model can give researchers a good idea of which ones to look at first when exploring new drug targets to work on that type of cell,” Gao continued.

According to the researchers, the machine learning algorithm system is available to the public and could be applied widely. Users can combine the system with additional analysis methods that may be better suited for their own studies. This could include finding new drug targets.

“This new approach could be used to develop key biological hypotheses regarding the regulatory transcription factors in cells related to a broad range of scientific hypotheses and topics. It will allow us to derive insights into the biological functions of cells from many tissues,” Dai said.

Rehman explained the application relevant to his lab is to use the new machine learning algorithm system to focus on factors that increase disease in certain cells.

“For example, we would like to understand if there is transcription factor activity that distinguished a healthy immune cell response from an unhealthy one, as in the case of conditions such as COVID-19, heart disease or Alzheimer’s disease where there is often an imbalance between healthy and unhealthy immune responses,” Rehman said.

The post Machine Learning Algorithm Brings Predictive Analytics to Cell Study appeared first on Artificial Intelligence.

There was a time when rent was estimated manually. You would need to know the worth of your property on the market before applying a percentage of that valuation as your rental rate. Oftentimes, this was not your final rate, and you would need to look around your neighborhood to see what other landlords were charging. Rental listings give you some idea of rates in the neighborhood, but you would need to also consider other factors such as lot size, demand in your area, and various other expenses. The whole process could be challenging for an ordinary landlord without a lot of experience in real estate.

Fortunately, with technological advances, there are quicker ways of arriving at a rental estimate using rental analytics tools. They allow you to quickly respond to market situations that could affect property valuation, both positively or negatively. If you are in the business of property management, these tools make a previously laborious process much easier and more convenient. Here’s what rental analytics can tell you that a manual process may be hard-pressed to match:

Location Analytics 

Analytics software can take all the variables about your property’s location to give you a better idea of rentals in your area. You may think that a Starbucks opening in the vicinity could immediately jack rental prices up. However, the proximity of homes to certain establishments does not necessarily mean rental rates automatically rise. Often, what influences rental rates is a mix of type and quality of community establishments. The different data points that can be factored into rental estimate calculations can be done by a rental analytics tool and updated regularly.

Impact of Non-Traditional Variables 

Besides the traditional factors influencing rental rates, there is now an emergence of new and unconventional data sources that have an influence on rental rates. Some of these are factors like the number of coffee shops within a certain radius, homes, and buildings with swimming pools, the strength and reliability of mobile phone signals in the area, and even Yelp reviews of businesses in the neighborhood.

While these are not the traditional factors when valuing real estate, including them in the equation makes rent estimates much more accurate. With its machine learning algorithm, rental analytics software can be fed this information and can make its best rental rate recommendation that considers all data points.

Areas With the Highest Potential 

Advanced analytics can identify areas that are currently undervalued but have the highest potential. This allows landlords greater confidence in setting rental rates. At the same time, potential real estate investors can narrow their search for property knowing the potential return on investment from rental income.

As a property manager, landlord, or potential real estate investor, you want to have the most accurate analyses of the rental market of the properties you manage or are interested in investing in. The information that rental analytics can provide will surely be a very useful tool in your decision process. 

The post WHAT RENTAL ANALYTICS CAN TELL YOU appeared first on Artificial Intelligence.

In a digital age, it only takes a few years for research into cutting-edge areas like predictive analytics modelling and artificial intelligence to find practical uses in everyday business contexts. Areas like general usability, user interface, and semantics that are changed to empower a broader cross-section of potential users.

Business-focused users keen to leverage statistical methods may not be capable of or comfortable with interacting in a pure text terminal in Python or R, for example. After all, open-source code is exactly that: can you blame someone for not wanting to make an investment decision based on code found freely on the internet? Business users need an easy way to interface with powerful analytics and need a trusted brand that stands behind them. That’s exactly why Minitab has changed the game, making machine learning easy for everyone.

Today, business users do not have to compromise to do advanced analytics. For statistical analysis, predictive analytics, and machine learning, there is a 50-year-old powerhouse called Minitab.

Over the past couple of years, Minitab has revolutionized the market by bringing the world’s most advanced data gathering, processing, visualizations, and analysis to the masses. Most recently, Minitab broke the barrier to putting “Data Science” into the hands of business professionals. And unlike others who promised this in the past, Minitab delivers.

That’s because, across the company’s product portfolio, there is a strong emphasis on usability and business outcomes – Minitab is deployed by a broad cross-section of the business community.

For decision-makers tasked with a business process or operational improvements, access to data and the ability to use it to achieve clear goals is critical. The lifeblood of today’s organizations is information, so using it to examine what was, what is, and what might happen can result in in lowered costs, higher revenues, and more efficient, timely actions for long-term strategy.

The talk may be of variables and predictors in mathematics and statistics — for the business’s Change Manager, it’s data sources, outcomes, and results. The phraseology might be different, but the required data processing and analysis remain the same. Don’t be intimidated by the term “Machine Learning.” All it refers to is learning from your data, which is effectively what data analysis is. Don’t believe us? Try it yourself.

With Minitab at the core of your data operations, there is immediate “plug and play” access to hundreds of data sources via included connectors that allow companies to access the data silos, repositories and applications across the network and in the cloud. It’s not surprising that Minitab was the highest-rated data integration tool according to Gartner Peer Insights.

Coupled with the statistical core of the Minitab data analysis platform, professionals can get a full picture of their data by leveraging archived information and real-time data streams as they happen in any organization.

Data is cleaned, transformed, and presented, providing the basis for predictive analytics modelling and insights into existing work processes.

As companies begin to scratch the surface of the data resources they have, hidden relationships between events and variables are uncovered. Factors that were never apparent can surface, even to the objective observer, and visual relationships and correlations emerge. Insights gained help both data specialists and line-of-business experts to determine how best to achieve the company’s objectives.

For line-of-business managers and non-data scientists, the visual language of Minitab helps show and correlate the various factors at play. It allows them to create predicted outcomes to proposed changes to operations in safe modelling environments.

The beauty of the Minitab portfolio is its design for use in practical settings. The platform’s openness and user interface mean it can be used in multiple verticals and unexpected use-cases: manufacturer Tate & Lyle used AI techniques and plotted thousands of variables to refine its sweetener consistency for better customer experience, for example. Where one might least expect it, Minitab’s statistical power is creating change.

In a vast range of industries, advanced analytical modelling, analysis, and machine learning algorithms are being deployed by organizations to improve outcomes in thousands of scenarios. At one time, this type of statistical analysis was only seen in finance and high-end medical research and pharma, but not today. Minitab is making real, meaningful differences in thousands of settings.

It integrates with both cloud and on-premises applications and services, from marcomms to stream processors. Minitab installs locally or is now available as a SaaS, ready to be accessed from anywhere with an internet connection.

To learn more about the Minitab suite of offerings and begin leveraging its accessible power to affect change, start your journey here.

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