There is a wide variety of useful external datasets available to download and integrate into your IoT analytics, often for free. There are far too many of these to do an exhaustive tour, but we will touch on some useful examples. Using these datasets is a great low-cost way to enhance the value of your IoT data.
- Preface
- Defining IoT Analytics and Challenges
- IoT Devices and Networking Protocols
- IoT devices
- Networking basics
- IoT networking connectivity protocols
- IoT networking data messaging protocols
- Analyzing data to infer protocol and device characteristics
- Summary
- IoT Analytics for the Cloud
- Creating an AWS Cloud Analytics Environment
- Collecting All That Data - Strategies and Techniques
- Getting to Know Your Data - Exploring IoT Data
- Exploring and visualizing data
- Look for attributes that might have predictive value
- R (the pirate's language...if he was a statistician)
- Summing it all up
- Solving industry-specific analysis problems
- Summary
- Decorating Your Data - Adding External Datasets to Innovate
- Communicating with Others - Visualization and Dashboarding
- Applying Geospatial Analytics to IoT Data
- Data Science for IoT Analytics
- Machine learning (ML)
- Anomaly detection using R
- Forecasting using ARIMA
- Deep learning
- Summary
- Strategies to Organize Data for Analytics
- The Economics of IoT Analytics
- Bringing It All Together
Adding external datasets
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