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.
Understanding the Data, Information, Knowledge, and Wisdom (DIKW) Pyramid, Types of Data, Physical and Logical Representation of Data, Natural languages – Symbolic Representation, Computer Languages – Data Encoding, Storage, and Interpretation
Handling of sensor data, data pre-processing, and integration of different data sources, Heterogeneity and distributed nature, Selection of sensor to capture right set of data, Analog to digital conversion, Time and frequency domain analysis, Sampling theorem, Aliasing, Selection and cleaning, Edge analytics
Extracting meaning from data, Techniques for visualizing relationships in data and systematic techniques for understanding the relationships, Exploring data – Visualization, Correlation, and Regression, Probability distributions.
Concept of machine learning, Introduction to R programming, Regression – Linear and non-linear, Algorithms – MLR, Logistics and non-linear regression, Classification, Algorithms – SVM, Decision trees, boosted decision trees, Naïve Bayes, Quality of classification – Concepts of ROC, hit rate, kappa statistics and K-S statistics, Feature selection – Learn feature selection methods for regression- Ridge and LASSO
Feature selection methods for classification methods- Information value based, filter based and wrapper based, Algorithms and techniques for marketing analytics – Conjoint analysis, Hidden Markov models
Interested in this course? Let’s connect!
This is the most suitable course for data scientists and IoT developers.
The kit includes: Arduino Mega (ATMega2560) Sensors – Analog temperature sensor, Humidity sensor, IR Proximity Sensor, Switches – Push Button (10), Breadboard, LEDs (10), Resistors (10), , Connecting leads (25), WiFi – ESP8266 ESP01
Yes, you will get lifetime access to the LMS.