Teim ## Data Analytics in Power Generation & Distribution

Numbers have an important story to tell ,They rely on you to give them a voice.

## objectives:

With Teim training program you can leverage the capabilities of an organization by building the blocks and drivers for smart power generation, distribution and its management.

Our training programs bring deeper insights and data analytics in strengthening the operations & maintenance practices, predictive analysis of equipment failures, address key indicators responsible for transmission losses and smart distribution system.

R, Tableau

## Who can apply?

Graduates / Post Graduates, Project Manager, Control Engineer, Operation & Maintenance Manager, Plant Manager etc.

Program Code Duration Program Classroom Online Onsite
DDMPU
3 Months
Data Analytics in Power Generation & Distribution

## Introduction to the Industry

Power Generation & Distribution basics, Power Generation & Distribution Equipment’s Overview & Operations/Challenges

## Basics of Analytics I (Descriptive Statistics)

Introduction to data:

Measure of Central Tendency: Mean (Arithmetic) & Merits-Demerits, Median, Mode, Standard deviation, Variance, Skewness, Kurtosis

Probability Distribution: Normal Distribution, Standard Normal Distribution, Standardization of data

Basics of R code: Understanding data type, Arithmetic operations, Creating vectors, Metrix, list, setting working Directory, Creating data frame, Summary statistics

## Basics of Analytics II (Inferential Statistics)

Hypothesis Testing: Null Hypothesis, Alternate hypothesis, Level of significance, P-Value, Decision criteria

Statistical test (parametric and non- Parametric Test): Large and small sample test, Z-Test, t-test, F-test, Chi-square test, ANNOVA ( Analysis of variance)

## Linear & Multi-Linear Regression

What is Regression?, Covariance & Correlation, Features of r (correlation)​, Testing the significance of the correlation coefficient,​Types of regression analysis, Purpose of regression analysis​, Purpose of regression analysis​,  R2  coefficient determination, Coefficient of determination (R2) and Adjusted R2, Multiple Linear Regression​, Typical Applications of Regression Analysis, Residual Analysis​. Multi-collinear​, Hetero-skedasticity​, case study

## Logistic Regression

Logistic Regression Basics, Generalized Linear Model (glm), What is logistic regression?​,Types of logistic regression analysis​, Applications of logistic regression analysis ,Prerequisite / when & why binary logistic regression​Case Study with R-German Bank

## Clustering

What is clustering?​, When to use cluster analysis?  ​Application of cluster analysis​, Types of cluster analysis ​,K means (In detail), Case Study with R

## Decision Tree

What is decision tree?​ Why decision tree? ​Types of decision tree ​Constructing decision tree​ , Random forest and CART (In detail), Case Study with R

## Time Series Modeling

What is Time series​,Components of Time Series​,Techniques for forecasting​- Simple Moving Average​,Weighted Moving Average​, Simple Exponential Smoothing​, Double Exponential Smoothing​, Triple Exponential Smoothing​, Time Series Models Comparison, Use Cases, Industry Applications​, Basic Concepts (acf, pacf, AR, MA)​, ARMA Model​, ARIMA Model ​,Industry Applications​

## Predictive modelling techniques

Practical Sessions

## Maintenance Strategy Optimization

Practical Sessions

## Asset Life cycle cost Prediction

Practical Sessions

## Data Visualization

What is Data Visualization?​ , Types of Visualisation​, Common Data Visualization Issues Data Visualization tool​s

## Interested in this program? 