Azure Open Datasets - Simply Explained
Azure Open Datasets gives you instant access to curated public datasets hosted by Microsoft in Azure, so you can spend less time searching for and cleaning data and more time building AI, analytics, and machine learning solutions. Instead of downloading files from dozens of websites, Azure Open Datasets provides trusted, regularly updated data that is ready to use with Azure Machine Learning, Azure Databricks, Power BI, Azure Data Factory, and other Azure services.
Think of Azure Open Datasets as a public library where all the books are already organized, indexed, and sitting next to your workspace. You don't waste hours finding information or converting file formats—you simply choose the data you need and start working. Microsoft keeps many of these datasets synchronized with their original sources, helping ensure you always have current information without maintaining your own copies.
The platform includes datasets covering weather, transportation, public holidays, demographics, census information, economics, public safety, satellite imagery, and geographic information. These datasets can enrich your own business data, allowing you to build more accurate forecasting models, optimize logistics, predict customer behavior, or analyze trends with greater confidence.
For example, imagine you're predicting retail sales. Your historical sales data alone might not explain sudden changes in demand. By combining it with public weather data, holiday calendars, or local population statistics from Azure Open Datasets, your machine learning model gains valuable context and often produces far better predictions.
Data plays a crucial role in modern data science. You rely on accurate and diverse datasets to drive insights and make informed decisions. Azure Open Datasets offers a valuable resource to streamline your workflows. These curated datasets save you time and enhance model accuracy. By using varied data sources, you can address class imbalances and reduce bias in your models. This leads to fairer outcomes and better predictive power. With Azure Open Datasets, you can focus on generating insights rather than spending hours on data preparation.
Key Takeaways
- Azure Open Datasets provide free, high-quality data for data science projects.
- These datasets save time by reducing the need for extensive data cleaning and preparation.
- You can access Azure Open Datasets easily through tools like Python, Power BI, and Azure Machine Learning.
- Using diverse datasets helps improve model accuracy and reduces bias in predictions.
- Curated datasets enhance the reliability and reproducibility of your data science experiments.
- Integrating Azure Open Datasets into your workflows can lead to faster project completion.
- You can combine Azure Open Datasets with your own data for deeper insights.
- Explore the Azure Open Datasets catalog to find datasets that fit your project needs.
Azure Open Datasets Overview
Azure Open Datasets are curated public datasets available on Microsoft Azure. They aim to enhance machine learning solutions by providing scenario-specific features for more accurate models. This service simplifies the process of accessing high-quality data, allowing you to focus on analysis rather than data preparation.
Key Features
Accessibility
One of the standout features of Azure Open Datasets is their accessibility. You can easily access these datasets through various platforms, including Python, Power BI, and Azure Machine Learning. This flexibility allows you to integrate data into your projects with minimal effort. You can quickly retrieve datasets without worrying about complex data pipelines or extensive cleaning processes.
Tip: To get started, explore the Azure Open Datasets catalog. It provides a user-friendly interface to browse and select datasets that fit your project needs.
Variety of Datasets
Azure Open Datasets offer a diverse array of datasets covering real-world topics. You can find data related to weather, public holidays, demographics, transportation, healthcare, and more. This variety allows you to enrich your own data with valuable external context. For instance, you can access NOAA weather observations or US Census data to enhance your analysis.
The significance of curated datasets in data science cannot be overstated. They contribute to the reliability and reproducibility of your experiments. Here’s how:
| Aspect | Explanation |
|---|---|
| Reliability | Curated datasets provide high-quality data that can be consistently referenced across experiments. |
| Reproducibility | They help maintain data lineage, ensuring that results can be traced back to their sources. |
| Compliance | Curated datasets ensure adherence to regulations, which is crucial in fields like healthcare. |
| Ambiguity Reduction | Standardized data reduces ambiguity in data interpretation, leading to clearer results. |
By using curated public datasets, you can document sources and establish stable access patterns. This enhances the reproducibility of your results. For example, a data science team might snapshot a month of open data into internal storage with checksums to ensure data integrity.
Types of Datasets
Azure Open Datasets offers a wide range of datasets that you can utilize for various data science projects. These datasets fall into two main categories: public datasets and domain-specific datasets.
Public Datasets
Public datasets are freely available and cover a variety of topics. They serve as excellent resources for analysis and model training. Here are some notable examples:
| Dataset Name | Description | Typical Use Case |
|---|---|---|
| San Francisco Safety Data | Fire department calls for service and 311 cases in San Francisco. | Analyzing public safety trends in urban areas. |
| Seattle Safety Data | Seattle Fire Department 911 dispatches. | Emergency response analysis and resource allocation. |
| Diabetes | A dataset with 442 samples and 10 features for machine learning. | Training machine learning models for health predictions. |
| OJ Sales Simulated Data | Simulated data for training models on Azure Machine Learning. | Model training and testing in retail analytics. |
| MNIST database of handwritten digits | A dataset for digit recognition with 60,000 training examples. | Image recognition and machine learning education. |
These public datasets allow you to explore real-world scenarios and enhance your analytical capabilities. You can leverage them to gain insights into various fields, from public safety to healthcare.
Domain-Specific Datasets
Domain-specific datasets cater to particular industries, providing focused data for specialized analysis. Two prominent domains include healthcare and transportation.
Healthcare
In the healthcare sector, datasets can help you analyze trends, improve patient outcomes, and support research. For example, the Diabetes dataset mentioned earlier is valuable for training models that predict health-related outcomes. You can also find datasets related to public health statistics, which can inform policy decisions and healthcare strategies.
Transportation
Transportation datasets provide insights into mobility patterns and urban planning. The NYC Taxi & Limousine Commission datasets, including yellow and green taxi trip records, offer detailed information about taxi usage in New York City. These datasets include trip details such as dates, locations, distances, fares, and passenger counts. You can use this data to analyze transportation trends and improve city infrastructure.
| Category | Example Datasets |
|---|---|
| Transportation | NYC Taxi & Limousine Commission - yellow taxi trip records, NYC Taxi & Limousine Commission - green taxi trip records |
| Labor and economics | US Labor Force Statistics |
By utilizing these domain-specific datasets, you can tailor your analysis to meet the needs of specific industries, enhancing the relevance and impact of your findings.
Integrating Azure into Data Science Workflows
Integrating Azure Open Datasets into your data science workflows can significantly enhance your projects. You can access datasets easily and utilize them effectively in your analyses. Here’s how you can do it.
Data Ingestion Techniques
To start using Azure Open Datasets, you need to understand data ingestion techniques. These techniques allow you to import data into your environment for analysis. Here are some common methods:
- Direct Access: You can directly access datasets from Azure using APIs. This method allows you to pull data into your applications seamlessly.
- Azure Storage: You can store datasets in Azure Blob Storage. This option provides a scalable solution for managing large datasets.
- Data Factory: Azure Data Factory enables you to create data pipelines. You can automate the movement and transformation of data from various sources to your Azure environment.
These techniques ensure that you have smooth access to datasets, making your workflow more efficient.
Tools for Integration
Several tools facilitate the integration of Azure Open Datasets into your data science projects. Two of the most powerful tools are Azure Machine Learning and Power BI.
Azure Machine Learning
Azure Machine Learning is a comprehensive platform for building, training, and deploying machine learning models. You can easily integrate Azure Open Datasets into your machine learning workflows. Here’s how:
- Dataset Registration: Register your datasets in Azure Machine Learning. This step allows you to manage and version your datasets effectively.
- Experimentation: Use the datasets to run experiments. You can train your models using high-quality data, improving accuracy and performance.
- Deployment: Once your model is ready, deploy it as a web service. This deployment allows you to make predictions in real-time using Azure Open Datasets.
By leveraging Azure Machine Learning, you can streamline your machine learning processes and enhance your model's capabilities.
Power BI
Power BI is another powerful tool for integrating Azure Open Datasets. It allows you to visualize and analyze data effectively. Here’s how you can use Power BI with Azure Open Datasets:
- Data Import: Import datasets directly from Azure into Power BI. This feature enables you to create interactive reports and dashboards quickly.
- Data Transformation: Use Power Query to transform your datasets. You can clean and shape your data to meet your analysis needs.
- Visualization: Create stunning visualizations to present your findings. Power BI’s capabilities help you communicate insights effectively to stakeholders.
Using Power BI with Azure Open Datasets enhances your ability to analyze data and share insights across your organization.
Practical Applications
Azure Open Datasets provide numerous practical applications across various industries. Organizations leverage these datasets to solve specific business and research problems. Here are some notable case studies that illustrate their impact:
Case Studies
- Government Efficiency: Governments utilize open data to enhance AI representation. This improvement leads to better responsiveness and efficiency in public services. By analyzing datasets, they can identify trends and allocate resources more effectively.
- Healthcare Insights: A healthcare organization used the Diabetes dataset to develop a predictive model for patient outcomes. This model helped doctors make informed decisions about treatment plans, ultimately improving patient care.
- Urban Planning: City planners analyzed transportation datasets to understand traffic patterns. This analysis allowed them to optimize public transport routes and reduce congestion in urban areas.
These examples show how Azure Open Datasets can drive innovation and improve decision-making in various sectors.
Enhancing Machine Learning
Azure Open Datasets significantly enhance machine learning solutions. You can use these datasets for both training and testing your models, as well as for feature engineering.
Training and Testing
When you train your models, high-quality data is crucial. Azure Open Datasets provide clean and curated datasets that allow you to build robust machine learning models. For instance, you can use the MNIST database of handwritten digits to train a model for image recognition tasks. This dataset contains thousands of labeled examples, making it ideal for supervised learning.
Testing your models with diverse datasets also improves their performance. By evaluating your models on different datasets, you can ensure they generalize well to new data. This process helps you identify potential weaknesses and refine your models accordingly.
Feature Engineering
Feature engineering is a critical step in the machine learning process. It involves selecting and transforming variables to improve model performance. Azure Open Datasets offer a wealth of information that can enrich your features. For example, you can combine weather data with sales data to analyze how weather conditions affect retail performance. This combination allows you to create more informative features, leading to better predictions.
Tip: Always explore the relationships between different datasets. You may uncover valuable insights that enhance your analysis and model accuracy.
By integrating Azure Open Datasets into your machine learning workflows, you can streamline your processes and achieve better results. The combination of high-quality data and powerful tools like Azure Machine Learning empowers you to create effective data-driven solutions.
Benefits of Azure Open Datasets
Cost-Effectiveness
Using Azure Open Datasets can significantly reduce your data-related costs. These datasets are free to access, which means you can save money on purchasing data from third-party sources. You only incur charges when you use Azure compute resources for processing. This model allows you to leverage high-quality data without the burden of maintaining your own data pipelines.
Additionally, the curated nature of these datasets means you spend less time cleaning and preparing data. You can focus your resources on analysis and model development instead of data acquisition. This shift leads to a more efficient use of your budget and time.
Time-Saving
Time is a precious resource in data science. Azure Open Datasets help you save time in several ways:
- Curated datasets reduce ambiguity and speed up baselining.
- Documented datasets facilitate faster evaluation and minimize misinterpretations.
- Direct access to Azure-hosted storage enhances processing efficiency.
- Integration with Azure analytics and ML services streamlines ingestion and processing workflows.
By using these datasets, you can quickly access reliable data, allowing you to focus on generating insights rather than spending hours on data preparation. This efficiency can lead to faster project completion and quicker decision-making.
Improved Data Quality
Data quality is crucial for successful data science projects. Azure Open Datasets provide high-quality, curated data that you can trust. These datasets undergo rigorous validation processes, ensuring that they meet high standards. When you use Azure Open Datasets, you benefit from:
- Reliability: You can consistently reference high-quality data across your experiments.
- Reproducibility: Documented datasets help maintain data lineage, allowing you to trace results back to their sources.
- Compliance: Curated datasets ensure adherence to regulations, which is vital in sensitive fields like healthcare.
- Ambiguity Reduction: Standardized data minimizes confusion in interpretation, leading to clearer results.
By relying on Azure Open Datasets, you enhance the overall quality of your analyses and models. This improvement can lead to more accurate predictions and better decision-making.
Azure Open Datasets offer significant advantages for your data science projects. You gain access to high-quality, curated datasets that save you time and enhance your analysis. These datasets align with current trends in open data and data democratization by emphasizing accessibility and fostering collaborations. By leveraging Azure Open Datasets, you can enrich your work and improve decision-making. Explore these datasets today and consider how they can elevate your projects to new heights.
Tip: Start integrating Azure Open Datasets into your workflows to unlock the full potential of your data science initiatives!
FAQ
What are Azure Open Datasets?
Azure Open Datasets are curated public datasets hosted on Microsoft Azure. They provide high-quality data for machine learning, analytics, and research, allowing you to focus on insights rather than data preparation.
How do I access Azure Open Datasets?
You can access Azure Open Datasets through various platforms, including Python, Power BI, and Azure Machine Learning. Simply browse the Azure Open Datasets catalog to find datasets that suit your needs.
Are Azure Open Datasets free to use?
Yes, Azure Open Datasets are free to access. You only incur charges when using Azure compute resources for processing the data. This model allows you to leverage high-quality data without additional costs.
Can I use Azure Open Datasets for commercial purposes?
Yes, you can use Azure Open Datasets for commercial purposes. However, always check the specific licensing terms for each dataset to ensure compliance with usage guidelines.
What types of datasets are available?
Azure Open Datasets cover a wide range of topics, including weather, healthcare, transportation, and demographics. This variety allows you to enrich your analyses with diverse data sources.
How do Azure Open Datasets improve data quality?
Azure Open Datasets undergo rigorous validation processes, ensuring high-quality, reliable data. This curation enhances the accuracy of your analyses and models, leading to better decision-making.
Can I combine Azure Open Datasets with my own data?
Absolutely! You can combine Azure Open Datasets with your own data to enrich your analyses. This integration allows you to gain deeper insights and improve the accuracy of your models.
What tools can I use with Azure Open Datasets?
You can use various tools with Azure Open Datasets, including Azure Machine Learning for model training and Power BI for data visualization. These tools help you maximize the potential of the datasets.
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Welcome to another episode of Microsoft Knowledge Nuggets.
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I'm Mirko Peters.
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Today's topic is one that almost everyone learning data
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science or machine learning runs into.
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You've watched a few tutorials, you've got the basics down,
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but there's one thing that keeps stopping you cold.
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You don't have any interesting data to practice with.
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You start searching for public data sets
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and that's when the trouble begins.
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You find a government website with weather data,
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which is great, but the files are in some weird format
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you've never seen.
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The column names don't make sense
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and half the values are missing.
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You spend hours just trying to open the thing
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and by the time you've cleaned it up enough to actually use,
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the excitement is gone.
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I've been there and it's frustrating.
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Today's episode removes that barrier completely.
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By the end of this episode,
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you'll know what Azure Open Data Sets is
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and how to grab free, ready to use data in minutes
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without any scraping, cleaning or headaches.
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Let's dive in.
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What Azure Open Data Sets actually is.
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So what exactly is Azure Open Data Sets?
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Here's the simplest definition.
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It's a free library of public data sets
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that Microsoft has already cleaned and hosted on Azure.
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Think of it like a library
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where someone else has already checked every book
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for torn pages, fixed the spelling errors,
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and put everything in alphabetical order
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so you just walk in and grab what you need.
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Now this is important.
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It's not a separate paid service you have to sign up for
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or some premium add-on.
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It's just data sitting in Azure storage that you can access.
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That's it.
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Microsoft has taken public data sets
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from places like NOAA, the US Census Bureau and City Governments
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and they've done all the hard work
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of normalizing the schemers, fixing inconsistencies
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and setting up refreshed schedules
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so the data stays current.
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Contrast that with the old way of doing things.
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You'd find a data set on some government site,
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download a zip file full of CSV files
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each with a different format,
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open them up and find missing values,
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weird date formats and columns
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that don't match between files.
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Then you'd spend hours, sometimes days,
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writing code to clean it all up.
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And by the time you were done,
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you weren't even sure you'd done it right.
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Microsoft handles all of that.
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They normalize the data into consistent schemers,
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fix the gaps and store it in Park A format,
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a columnar storage format that makes queries lightning fast.
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If you've ever tried to query a giant CSV file
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and waited forever for it to load,
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you'll appreciate what Park A does.
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It's optimized for analytics,
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so your queries run much faster than they would
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on plain text files.
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Here's the key point, the data itself is free
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and Microsoft pays for the storage always.
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You only pay for compute if you process it
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and we'll talk about that later.
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But the data sitting in Azure Storage costs you nothing.
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Let me give you an analogy to make this concrete.
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Imagine you're moving into a new office building.
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In the old way of doing things,
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you'd get a pile of lumber, dry wool, wiring and plumbing fixtures
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and you'd have to build each room yourself from scratch.
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That's what working with raw public data feels like.
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Azure Open Data Sets is the opposite.
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These are pre-built rooms of data you can walk into.
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The weather room is already furnished,
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the census room has everything labeled and organized.
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You don't have to assemble anything,
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you just walk in and start working.
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The problem Open Data Sets solves,
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so why did Microsoft build this,
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what problem were they trying to solve?
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Let's break it down.
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Discovery is the first problem.
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You need reliable public data, but finding it takes time.
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There are hundreds of government websites,
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academic repositories and data portals out there.
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Some are well maintained, some are abandoned
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and some make you fill out forms or agree to licenses
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before you can download anything.
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Just finding a clean, usable dataset might chew up days of searching.
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Then there's the preparation problem.
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Raw data from government sources is messy, inconsistent and full of gaps.
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Take the NOA weather data.
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It comes from thousands of weather stations around the world,
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each reporting in slightly different formats.
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Some stations report temperature in Celsius, others in Fahrenheit,
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some record precipitation, others don't.
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And some have years of clean data,
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while others have huge gaps where the sensor was broken.
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Cleaning that up by hand is miserable work,
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scale is another problem.
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Some of these datasets are huge.
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The weather data covers decades.
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The NYC Taxi Data has hundreds of millions of trips,
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the satellite imagery sits in petabytes.
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Moving that much data to your compute environment is slow and costly.
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If you're running a machine learning model on a cloud VM,
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you don't want to spend hours downloading data
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before you can even start training.
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And finally, reproducibility matters.
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When you're running experiments, you need your data to be consistent.
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If you download a dataset today and your colleague downloads it next week,
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you need the same thing.
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Curated, documented datasets make experiments repeatable.
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You can publish your results and someone else can verify them
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using the exact same data.
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Now, let's clear up a myth.
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Most people think machine learning is just about the algorithm.
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But the real insight from Microsoft is that most machine learning problems
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are impacted by real world factors.
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Weather, holidays, demographics, economic conditions.
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If you're trying to predict sales, knowing whether it's a holiday
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or a rainy Tuesday makes a huge difference.
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But if you're predicting energy consumption, temperature, and time of day matter.
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If you're modeling insurance risk, neighborhood demographics are critical.
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Without these external signals, your model is missing context.
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It's like trying to predict traffic patterns
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without knowing what time of day it is.
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You might get some patterns right, but you'll miss the big picture.
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And that's the core of the time to data problem.
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It can take weeks of work just to get usable data.
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Weeks of searching, downloading, cleaning, and validating.
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By the time the data is ready, the business question might have changed.
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Azure Open Data Set collapses that timeline from weeks to minutes.
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What's in the catalog?
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Weather and holidays.
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Let's dive into what's actually in this catalog.
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The first data set almost everyone finds useful is weather data.
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Microsoft hosts the NOAA Weather Data Set.
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It gives you historical and near real-time readings
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from weather stations all over the world.
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We're talking temperature, precipitation, wind speed,
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barometric pressure, the kind of data that affects everything
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from what people buy to how much energy they use.
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And it covers decades of data updated regularly.
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So if you want to look at weather patterns from the 1990s
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or pull last week's readings, it's all in there.
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Now, weather data is great.
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But here's the thing, there's another data set
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that pairs with it perfectly.
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Public holidays, Microsoft has a holiday's data set
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covering 38 countries from 1970 all the way to 2019.
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So you can look backward for historical analysis
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or forward for planning.
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It gives you country-specific holiday flags for any day
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to change.
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It's in there.
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Want to check if that Monday and August is a bank holiday
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in the UK, one query, and you've got it.
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Why are these two data sets so powerful together?
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Weather and holidays dramatically improve
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time series predictions.
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Think about retail sales forecasting.
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If you're trying to predict how many units of a product
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you'll sell next week, knowing that it's a holiday
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and that it's going to rain can cut your prediction
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error by double digits.
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That's not a small improvement.
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That's the difference between having
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too much inventory, sitting in a warehouse, and running out
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of stock on your best selling item.
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And here's the thing, most companies
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don't have this data internally.
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They have their sales data, sure,
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but they don't have a clean, reliable source
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of weather history or holiday calendars.
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So they either build it themselves, which takes time
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or they go without, which hurts their predictions.
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Azure Open Data Set gives them that missing piece instantly,
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but weather and holidays are just the beginning.
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What's in the catalog?
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Census, demographics, and socioeconomic data.
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The next category matters if you're building models
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that predict something about people.
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It's census and demographic data.
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Microsoft hosts US Census data covering population,
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income, education levels, and age distribution.
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And it's available at different geographic levels,
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no state, county, even census tract.
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So you can grab the median household income
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for a specific neighborhood or the population breakdown
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by age group for an entire state.
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And there's also the US labor force statistics data set
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that gives you employment rates, labor participation numbers,
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and industry breakdowns.
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You can see how many people work in manufacturing
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versus services in a given region
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or how unemployment has changed over time.
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Why does this matter for beginners?
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Because if you want to build a model
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that predicts something about people,
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this is the perfect place to start.
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And here's a real example.
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Imagine you're building a model for an insurance company.
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You have claims data, ages, amounts,
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but your model isn't accurate
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because you're missing context about where these people live.
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So you enrich that data with census income
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and education statistics.
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Suddenly, the model sees patterns it missed before.
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People in lower income areas file different claims
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and higher education levels change risk profiles.
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The model gets better because it has more information
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about the real world.
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That's what makes Azure Open Data Sets valuable for beginners.
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You don't need to be a data engineer
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to pull census data into your project.
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It's already cleaned and ready.
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Join it with your own data
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and your analysis gain steps it didn't have before.
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What's in the catalog?
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Mobility, public safety, and health care.
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The classic beginner favorite is the NYC TaxiTrips Data Set.
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It's one of the most well-known public data sets out there
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millions of trip records would pick up
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and drop off times locations and fares.
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Every yellow taxi trip in New York generates a record
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where they got in, where they got out,
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how much it cost, how long it took.
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It's a great data set for learning data analysis.
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Why do beginners love it?
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Because it's interesting and visual,
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load it into Power BI and start making maps
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of where people travel most.
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See how fares change throughout the day, spot patterns,
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which neighborhoods get busy at rush hour,
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which airports generate the most trips,
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however affects ride volume.
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It tells the story,
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so learning feels like solving a puzzle, not doing homework.
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Public safety data is next.
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Microsoft hosts the San Francisco Safety Data,
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which records crime incidents with type, location,
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and timestamp.
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Again, perfect for beginners because the data is immediately
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meaningful.
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Build hotspot maps, analyze crime patterns by time of day,
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it shows how open data supports real civic projects.
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City planners and police use this to decide
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where to deploy resources.
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Then there's the COVID-19 data lake.
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It's not just one data set.
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It combines multiple sources, patient outcomes, testing,
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hospital capacity, policy, mobility.
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During the pandemic, researchers used it to track the virus,
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model it, spread, and evaluate interventions.
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That shows how open data sets can have real world impact
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when they're accessible.
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These three data sets, taxi trips, public safety,
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and COVID-19 share something important.
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They're not abstract.
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You don't need to be an expert to understand them.
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The data is about real people, places, and events.
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That makes learning easier.
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There are also benchmark data sets
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for people learning ML specifically.
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What's in the catalog?
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Benchmark ML data sets.
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So you've got all this real world data from the previous section.
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But what if you're following a tutorial
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and you just need a clean, simple data set
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to practice a specific technique?
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Azure Open Data Set has you covered there, too.
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Think of it like a toolbox.
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You have your power tools for big jobs,
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but sometimes you just need a simple hammer to learn the basics.
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That's what these benchmark data sets are for.
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Take the diabetes data set with 442 samples and 10 features.
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It's a classic regression problem
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you'll see in textbooks and tutorials all the time.
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And it's available right in Azure Open Data sets,
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ready to load into Azure ML.
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If you're learning automated ML,
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this is a great data set to start with.
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Small enough to iterate quickly,
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but real enough to teach you the workflow.
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Then there's the Microsoft News Data Set,
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called Mind for Short,
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which is for people interested in recommendation systems.
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It's a large-scale benchmark
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with user interactions, news content, and metadata.
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If you want to build a news recommendation engine,
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the kind of thing that powers the recommended
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for use section on news websites.
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This is the data set to practice on.
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It's the same kind of data that real recommendation systems use.
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And there's even a simulated OJ sales data set,
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built specifically to show how Azure ML scales.
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You can train thousands of models simultaneously on this data,
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which is a great way to understand parallel processing
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and hyper parameter tuning.
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Here's the thing.
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If you're following an Azure ML tutorial and you need data,
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it's probably already in this catalog.
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Microsoft has made sure that the data sets used in their documentation
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and learning paths are available through Open Data sets.
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So you don't have to go searching for the exact file,
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the tutorial uses.
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It's already there.
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But having a great catalog is useless
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if you can't actually get the data.
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How to access Open Data sets, Python, Power BI, and Azure ML.
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So you've seen what's in the catalog,
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but now the real question is,
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how do you actually get this data into your hands?
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The answer depends on which tool you're using,
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but there are multiple paths,
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and some of them don't even require an Azure account.
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Let's break it down.
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The simplest option is Python.
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From any Python environment, no Azure account needed,
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Microsoft publishes a Python package called Azure ML Open Data sets.
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You install it with PIP,
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then import any data set directly,
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for example, from Azure ML.
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Open Data sets, import NOAD weather.
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You specify a start date and an end date,
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and the package handles the rest.
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It downloads the relevant files from Azure Storage,
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processes them, and hands you back a pandas data frame.
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That's it.
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Three lines of code, and you've got decades of weather data
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ready to analyze.
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This works from Jupyter Notebook, VS Code,
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any Python environment you're comfortable with.
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Inside Azure Machine Learning, the process is even smoother.
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You open your workspace, go to the data section, click Create,
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and then choose from Azure Open Data sets.
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A catalog pops up showing you everything available.
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You browse through, pick the data set you want,
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filter it by date range or geographic area,
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and register it to your workspace.
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Once it's registered, as your ML generates a code snippet automatically,
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that you can use in any pipeline or experiment,
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so you don't have to remember the storage parts or the import syntax.
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It's all handled for you.
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What about Power BI?
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This is where things get interesting for analysts who don't write code.
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You can use the Azure Blob Storage connector in Power BI
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and point it to the public storage URL
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that Microsoft documents for each data set.
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So if you want to pull NYC Taxi Data into a dashboard,
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you find the storage URL on the data sets overview page,
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plug it into Power BI and start building visualizations.
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Weather data, taxi trips, public safety incidents,
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all of it available directly in your reports
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without any intermediate data engineering.
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For the SEAL crowd, there's Azure Synapse.
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You can query open data sets using the open-roadset function
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against the public block paths.
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This is serverless SQL, meaning you don't need to provision any storage upfront,
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you just write a query and Synapse goes and reads the data directly from where it lives.
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Want to ask how many taxi trips happened on Christmas Day in 2019?
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You write one SQL query and you've got your answer in seconds.
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And if you're using Azure Databricks,
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the same Python SDK works in Databricks notebooks,
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but you can use Spark DataFrames instead of Pandas,
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which matters when you're working with larger data sets.
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The NYC Taxi Data has millions of records.
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Pandas can handle that, but Spark handles it better.
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Same import, same syntax, just with Spark under the hood.
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Here's the common thread across all of these methods.
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Microsoft publishes the storage account URLs for every data set,
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and those URLs are public.
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Anyone can access them from any tool.
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The Azure Open Data Set SDK is just a convenience layer on top.
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If you wanted to, you could write your own code
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to read the raw-parkay files directly from those storage URLs.
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The SDK just makes it easier by handling the date filtering and conversion for you.
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The cost question, is it really free?
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This is the question everyone asks.
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Is it really free?
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The short answer is yes, and also no.
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Here's why it goes both ways.
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Actually, the data itself is completely free.
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Microsoft pays for the storage always.
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It's not a promotion or a limited time offer.
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It's built into the design of the service.
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They host it, they pay the bills,
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and you access it without spending a cent on the data itself.
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But here's where the costs come in.
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You pay for compute.
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That means if you spin up a virtual machine to process the data,
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you pay for that VM.
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A Databricks cluster analyzing millions of taxi trips,
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you pay for that cluster.
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00:14:25,440 --> 00:14:27,760
If you use Synapse Serverless SQL,
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you pay for the queries you run.
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00:14:29,440 --> 00:14:31,000
These are standard Azure costs.
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They aren't specific to Open Data Sets,
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then there's Egress.
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This one can catch you off-guard.
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If you move data across Azure regions
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or move it out of Azure entirely,
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you pay Egress charges.
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00:14:40,880 --> 00:14:43,880
Typical pricing is around 8 to 9 cents per gigabyte.
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That doesn't sound like much until you're moving terabytes.
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And some of these data sets are huge.
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The weather data alone covers decades and multiple weather stations,
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reading it all from a different region adds up quickly.
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There's also the cost of your own storage.
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Maybe you copy data into your own storage accounts
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because you want to keep a snapshot or transform it.
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You pay for that storage at standard Azure rates.
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Microsoft is transparent about this.
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If a data set has Egress charges,
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you'll find it documented on the overview page, no surprise bills.
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But you need to check before pulling large amounts of data across regions.
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Here's the key tip for beginners.
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Keep your compute in the same region as the data set.
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Every data set has a specified region where it's hosted.
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If your VM or Databricks cluster is in that same region,
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you avoid Egress charges entirely.
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The data stays local, the queries are faster and your bill stays low.
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Let's look at two real scenarios to make this concrete.
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Scenario one, you're a beginner pulling a small sample of weather data
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into a Power BI dashboard, a few thousand rows,
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maybe a few megabytes.
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Your compute cost is zero because Power BI handles it.
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Your Egress is zero because you're not moving much.
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This costs you nothing beyond your Power BI subscription.
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00:15:44,520 --> 00:15:49,400
Scenario two, you decide to process all NYC taxi data for the last 10 years.
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We're talking hundreds of millions of trips.
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00:15:51,400 --> 00:15:54,400
You spin up a large data bricks cluster to run the analysis.
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You're paying for that cluster by the hour.
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00:15:56,240 --> 00:15:57,520
If you're in the wrong region,
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00:15:57,520 --> 00:15:59,840
you're also paying Egress on terabytes of data.
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This can cost hundreds of dollars in compute alone.
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Here's the simple takeaway.
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Don't let the word free fool you into building wasteful pipelines.
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Sample first, then scale,
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pull a small subset of the data to explore.
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Validate your approach.
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Once you know what you're doing,
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then scale up to the full data set.
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That way you're not paying for expensive compute time
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while you're still figuring out your code.
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So that's Azure Open Data Sets.
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It removes the biggest barrier for beginners.
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Finding and cleaning data.
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You get free, ready to use data sets in minutes.
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You don't have to scrape anything.
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You don't have to clean anything.
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You just start using the data.
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If this knowledge nugget helped,
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00:16:33,760 --> 00:16:36,520
subscribe to the show on your favorite podcast platform.
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00:16:36,520 --> 00:16:38,920
Share it with someone who's always wanted to learn data science,
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00:16:38,920 --> 00:16:40,360
but didn't know where to start.
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00:16:40,360 --> 00:16:43,760
Next time we'll pull weather data into a Python notebook, step by step.
Founder of m365.fm, m365.show and m365con.net
Mirko Peters is a Microsoft 365 expert, content creator, and founder of m365.fm, a platform dedicated to sharing practical insights on modern workplace technologies. His work focuses on Microsoft 365 governance, security, collaboration, and real-world implementation strategies.
Through his podcast and written content, Mirko provides hands-on guidance for IT professionals, architects, and business leaders navigating the complexities of Microsoft 365. He is known for translating complex topics into clear, actionable advice, often highlighting common mistakes and overlooked risks in real-world environments.
With a strong emphasis on community contribution and knowledge sharing, Mirko is actively building a platform that connects experts, shares experiences, and helps organizations get the most out of their Microsoft 365 investments.
