18EE654 Testing and Commissioning of Electrical Equipment syllabus for EE

Introductory:

Introduction to Data, Big Data and Data Science, Big Data Architectures, Small Data, What is Data? A Short Taxonomy of Data Analytics, Examples of Data Use,A Project on Data Analytics.

Descriptive Statistics:

Scale Types, Descriptive Univariate Analysis, Descriptive Bivariate Analysis.

Multivariate Analysis:

Multivariate Frequencies, Multivariate Data Visualization, Multivariate Statistics, Infographics and Word Clouds.

Data Quality and Preprocessing:

Data Quality, Converting to a Different Scale Type, Converting to a Different Scale, Data Transformation, Dimensionality Reduction.

Clustering:

Distance Measures, Clustering Validation, Clustering Techniques.

Frequent Pattern Mining:

Frequent Itemsets, Association Rules, Behind Support and Confidence, Other Types of Pattern.

Cheat Sheet and Project on Descriptive Analytics:

Cheat Sheet of Descriptive Analytics, Project on Descriptive Analytics.

Regression:

Predictive Performance Estimation, Finding the Parameters of the Model, Technique and Model Selection.

Classification:

Binary Classification, Predictive Performance Measures for Classification, Distance-based Learning Algorithms, Probabilistic Classification Algorithms.

Course Outcomes:

At the end of the course the student will be able to:

• Define data, its architecture and examples of data use.
• Explain methods of descriptive analytics of data.
• Explain methods for multivariate analysis, data preparation and data transformation and reducing.
• Explain techniques for clustering the data and pattern mining
• Explain the methods of predictive analytics, performance measures for regression and algorithms for regression.
• Explain performance measures for classification of data and methods of prediction.

Question paper pattern:

• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub- questions) from each module.
• Each full question will have sub- question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbook

1 A General Introduction to Data Analytics João Mendes et al Wiley 2019