15 Jul Sexy Data Science and its Analysis of IoT
irst it was Big Data and now it’s the Internet of Things; the science of data is becoming increasingly sexy, maybe not Victoria’s Secret sexy but it certainly get the juices flowing for business leaders in the know. Hot or not? Definitely hot. In this episode of the IoT Business Show I speak with Ajit Jaokar about his passion, data science, and the application of machine learning, deep learning and predictive analytics in IoT.
In this episode of the IoT Business Show I speak with Ajit Jaokar about his passion, data science, and the application of machine learning, deep learning and predictive analytics in IoT.
Ajit is a researcher, entrepreneur and academic. His current research focus is on applying data science algorithms to IoT applications, including predictive analytics, time series data, sensor fusion and deep learning and he teaches this at Oxford University and the Technical University of Madrid.
Listeners of this show know that all incremental value of an IoT product comes from the business intelligence it creates. To get there however requires a bit of data wrangling, messaging and interpreting, or in other words, the transformation of data into value. Welcome to the world of data science.
Here’s What We’ll Cover in this Episode
- Data science vs. IoT data science
- Deep learning vs. machine learning
- Machine learning vs. data science
- Data science vs. analytics
- Predictive analytics vs. machine learning
- Machine learning vs cognitive computing
Mentioned in this Episode and Other Useful Links
Support this Podcast
If you have been enjoying this podcast, there are a few ways you can support it:
- Share it on social by clicking on the widget on the left or bottom of the page.
- Click here to open iTunes and leave a one-click review or write your thoughts.
- Leave a donation via PayPal by clicking on the button below.
Have an opinion? Join the discussion in our LinkedIn group
Does anything produce more value in IoT than data science?Click here if you have an opinion on this podcast or want to see the opinion of others