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.
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
w
Terminology of time series
a.
Ts objects
b.
Time plots
c.
Seasonality
d.
Periodicity
e.
Trend
f.
White Noise
g.
Unit root
h.
Smoothening
w
Pandas with time series - datetime object
w
Introductory signal analysis
a.
Fourier Transforms: Taking time series data into
frequency domain
b.
Recursion Plots
c.
Which spike is and anomaly
w
Getting Deeper into time series
a.
Differencing
b.
ACF and PACF
c.
Hypothetical tests
d.
Time series Data Analysis
w
Thinking of time series problem as a regression
problem Statistical Models
a.
Auto Regression
b.
Moving Average
c.
ARIMA
w
Deep Learning for time series
a.
RNN
b.
LSTM
w
Advanced (Optional)
a.
Anomaly detection using Auto Encoders
b.
Isolation Forest
Frequently
asked questions(FAQs)
Why to join this Course?
§
Practical Oriented Knowledige
§
Experienced Digital Experts
§
Flexible Leeming 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, well 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 may help you readily comprehend the data science concepts underlying 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 Asf the programme
Will there be an assignment?
Yes, we'll have assignments on weekly basis and final project
at end of the program.
Who can join this course?
Undergraduate/Graduate or professional working in the energy
sector
