Course Benefits

·       Live lectures from experienced professionals

·       Recorded session

·       Digital manuals

·       Field case studies

·       E-Certificates after an assessment test

Course Objectives

Facing the dilemma between resource shortage and environment destruction, numerous researches have been initiated within the field of energy study. For example, confronted with the fast increase in energy demand caused by economy growth, energy security has become a quite important issue, and lots of researches tried to capture the main trend in energy development, involving the productions, consumptions and prices of various energy forms.

The analysis of energy scenarios for future energy systems requires appropriate 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 is a specific way of analysing a sequence of data points collected over an interval of time. In time series analysis, analysts record data points at consistent intervals over a set period of time rather than just recording the data points intermittently or randomly.

Learning Objectives

ü Creates the Opportunity to Clean Your Data

ü Time Series Forecasting Can Predict the Future

ü Time Series Analysis Helps You Identify Patterns

Prerequisites

v Will teach from scratch, so no perquisites as such required.

v Having knowledge of data visualization, graphs and charts will be add on advantage.

Topics to be Covered

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

w  Mathematics and Statistics relevant to forecasting

a.     Lag features

b.    Algebra, Calculus

c.     Outlier Removal

·       Terminology of time series

a.     Ts objects

b.    Time plots

c.     Seasonality

d.    Periodicity

e.    Trend

f.      White Noise

g.     Unit root

h.    Smoothening

·       Pandas with time series - datetime object

·       Introductory signal analysis

a.     Fourier Transforms: Taking time series data into frequency domain

b.    Recursion Plots

c.     Which spike is and anomaly

·       Getting Deeper into time series

a.     Differencing

b.    ACF and PACF

c.     Hypothetical tests

d.    Time series Data Analysis

·       Thinking of time series problem as a regression problem

·       Statistical Models

a.     Auto Regression

b.    Moving Average

c.     ARIMA

·       Deep Learning for time series

a.     RNN

b.    LSTM

·       Advanced (Optional)

a.     Anomaly detection using Auto Encoders

b.    Isolation Forest

Frequently Asked Questions

Why to join this Course?

·       Practical Oriented Knowledge

·       Experienced Digital Experts

·       Flexible Learning Model with recorded lectures

·       Certificate of Completion

·       Project of Completion

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

·       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?

·       This might help you readily comprehend the data science ideas underpinning oil and gas applications. You will receive many data sets for practice and analysis.

Will I get a certificate?

Yes, you will receive a 6-week training certificate upon completion Acf the programme

Will there be an assignment?

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

Who can join this course?

Undergraduate/Graduate or professional working in energy sector.