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IOT Analytics Training Content:

  • COURSE INTRODUCTION: The objective of this training program is to re-skill data scientists. The volume of data is rapidly increasing with proliferation of IoT devices. IoT has turned everything into 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.
  • PRE-REQUISITES: Computer fundamentals and basic programming.
  • DURATION: 6 Days(48 HRS)
  • CERTIFICATON: At the end of the course you will get the certificate for completing the training.

Course Content:

DATA REPRESENTATION

  • Understanding 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

SENSOR ANALYTICS

  • 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

STATISTICAL ANALYSIS

  • Statistics is about 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.

MACHINE LEARNING

  • Concept of machine learning,
  • Introduction to R programming, Regression- Linear and non linear,
  • Algorithms- MLR, Logistics and nonlinear 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