The objective of this training program is to re-skill data scientists. The volume of data is rapidly increasing with the proliferation of IoT devices. IoT has turned everything into a potential source of data. Data in its raw form is not always useful. Data need to be processed to transform into information. The volume, velocity, and variety of data have made conventional processing and analytical approaches obsolete.
IoT Analytics course introduces participants to a fundamental understanding of sensor data, systems, and innovative and novel analytical approaches. Machine learning methods are used for data analysis, which is similar to data mining, but the main goal of machine learning is to automate decision models. Algorithms are the heart and soul of machine learning, and they help computers find hidden insights. So, in essence, machine learning algorithms need to be learned. The machine needs to learn from data. Data will have multiple dimensions: type (quantitative or qualitative), amount (big or small size), and number of variables available to solve a problem. Learning algorithms should also be as general purpose as possible. We should be looking for algorithms that can be easily applied to a broad class of learning problems.
R and Python are leading programming languages that have an array of packages for IoT data analytics. This course introduces R, Python, and various advance Python packages being used in IoT analytics. Standard R & Python IDEs are going to be used to perform hands-on sessions/programming exercises.
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