IoT Analytics Training

 >>  IoT Analytics Training

IoT Analytics Training


 (4.9) | 285 Ratings


Introduction


IoT Analytics Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 35 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

IoT Analytics Training Modules

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

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Course Curriculum

IoT Analytics Training Modules

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

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