Course Objectives:

Facing the dilemma between resource shortage and environment destruction, numerous researches have been initiated within the field of Oil & Gas study.

The analysis of Oil & Gas scenarios for future energy systems requires appropriate historical data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce.

Time series analysis in the oil and gas sector involves systematically recording data points at regular intervals over a specified timeframe. This structured approach enables analysts to uncover trends, patterns, and variations in critical parameters, such as production volumes and exploration results. By studying the temporal evolution of key variables, professionals can make informed decisions, predict future trends, and optimize operational strategies for greater Oil & Gas efficiency and profitability.

Learning Objectives:

·       Creates the opportunity to clean your data

·       Time Series Forecasting Can Predict the Future

·       Time series analysis helps you identify platforms

·       You can make the future performance of Oil estimations

·       Helps you to detect anomalies in your problems

Prerequisites:

·       Will teach from scratch, so no prerequisites as such are required

·       Having knowledge of data visualization, graphs, and charts will be an added advantage.

Topics to Be covered:

Introduction to time series data, and how it is different from normal data. We are already doing forecasting in our real life.

Mathematics and Statistics relevant to forecasting

·       Lag features

·       Algebra, Calculus

·       Outlier Removal

Terminology of Time Series

·       Ts objects, Time plots & Seasonality

·       Periodicity & Trend

·       White Noise

·       Unit root & Smoothening

Introductory signal analysis

·       Fourier Transforms: Taking time series

·       data into frequency domain

·       Recursion Plots

·       Which spike is and anomaly

Getting Deeper into time series

·       Differencing

·       ACF and PACF

·       Hypothetical tests

·       Time series Data Analysis

Thinking of time series problem as a regression problem

Statistical Models

§  Auto Regression

§  Moving Average

§  ARIMA

Deep Learning for time series

§  RNN

§  LSTM

Advanced (Optional)

§  Anomaly detection using Auto

§  Encoders & Isolation Forest

Frequently Asked Questions (FAQs):

Why to join this Course?

ü Practical Oriented Knowledge

ü Experienced Digital Experts

ü Flexible Learning Model with recorded lectures

ü Certificate of Completion

ü Project of Completion

What if I don't have any prior coding experience? Can I still join this course?

ü Don't worry, we'll start from scratch. There is no requirement for prior experience.

Will joining this program be beneficial to me because I am proficient in Python?

ü This might help you readily comprehend the data science ideas underpinning oil and gas applications.

ü You will receive many data sets for practice and analysis

Will I get a certificate?

Yes, you will receive a 5-week training certificate upon completion of the programme.

Will there be an assignment?

ü Yes, we'll have assignment on weekly basis and final project at end of the program.

Who can join this course?

ü Undergraduate/Graduate or professional working in energy sector.