Data Analytics in Oil & Gas
Numbers have an important story to tell ,They rely on you to give them a voice.
objectives:
Become a Data Analyst in Oil & Gas / Refinery / Petrochemical plants and inculcate the approach to Business governance that values decisions that can be backed up with verifiable data.
Gaining Knowledge of core operations in Energy Vertical
Solving complex issues analyzing available data in Operations, Maintenance, Reliability, Safety, Procurement, Inventory, Finance, HR and other functions
Become proactive adopting forecasting and predictive techniques.
The program is reliant upon logical thinking, your experience of core operations, problem definition with the available data and effectiveness of its analysis and interpretation. This is independent of your IT skills.
Analytics tools used:
R, Tableau
Who can apply?
Graduates / Post Graduates, Chemical Engineers, Mechanical / Electrical Engineers, Executive Managers, Performance Analyst, Process Engineers, Quality Managers Etc.
| Program Code | Duration | Program | Classroom | Online | Onsite |
|---|---|---|---|---|---|
|
DDMOG |
3 Months |
Data Analytics in Oil & Gas / Refinery / Petrochemicals |
|
|
|
Program Content:
Module 1
Introduction to the Industry
Oil & Gas Industry basics,
Hydrocarbon industry Equipment’s Overview & Operations/Challenges
Module 2
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
Module 3
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)
Module 4
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
Module 5
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 regressionCase Study with R-German Bank
Module 6
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
Module 7
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
Module 8
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
Module 9
Predictive modelling techniques
Practical Sessions
Module 10
Maintenance Strategy Optimization
Practical Sessions
Module 11
Asset Life cycle cost Prediction
Practical Sessions
Module 12
Data Visualization
What is Data Visualization? , Types of Visualisation, Common Data Visualization Issues Data Visualization tools
