Open source software, such as R or Python, is low cost but not really free in practice. There are expenses that need to be considered for any IoT analytics business case where open source software components are used.
- 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
Open source economics
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