Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
4 Hour(s) 30 Minute(s)
Language
English
Taught by
Energy Data Scientist, PhD
Rating
Course Overview
Build Accurate Time Series Forecasts with Python - Energy Sector Application
EXTRAS
There are entire books and hundreds of papers & Python models available to download in my Skool community. I also have Financial Times articles , and over 100 online courses on Python modelling of energy markets, energy investments etc.
I also post job opportunities for the wider energy sector.
The value is immense!
I also do supervision which means when you start learning a course, you can message me as many questions as you need, any time e.g. daily.
This link below offers 7 days free access to this Skool community. No obligation to pay anything in advance. At the end of the 7- day period, you can decide whether you want to continue at $25/month or just cancel and leave.
Honestly, the $25/month is a very generous price . You will realise it once you join. Universities , colleges, would charge thousands! This is really a unique opportunity of immense value, at a really low cost!
Link: www [dot] skool [dot] com/software-school-for-energy-7177
WHO I AM:
Researcher and educator specializing in energy data science (PhD in Energy)
REGULAR ENHANCEMENTS:
Course reviewed periodically with updates.
What You'll Learn:
How to build a Shallow Neural Network model in Python that can forecast CO₂ emissions
How to achieve high accuracy in the forecasts that you will produce
How to work with World Bank historical data
How to implement advanced statistical tests
How to apply your model to real-world cases (India, China, USA, UK, European Union analysis)
Perfect For:
Environmental consultants and analysts
Energy economists and policy makers
Data scientists in sustainability
Climate professionals
Why This Matters:
With net-zero targets and mandatory carbon reporting, professionals who can produce credible emissions forecasts are in high demand. Master the skills that set you apart in the growing climate economy. Companies now require carbon footprint assessments for regulatory compliance and ESG reporting. Governments need emissions projections for policy planning. Consultancies charge premium rates for these capabilities. Whether you're advancing your current career or transitioning into sustainability, these practical forecasting skills open doors to roles paying $150,000-250,000+ in the rapidly expanding green economy.
Course Content
- 6 section(s)
- 31 lecture(s)
- Section 1 Introduction
- Section 2 Data Preprocessing
- Section 3 Dataset split
- Section 4 Dataset scaling
- Section 5 Shallow Neural Networks
- Section 6 Conclusion
What You’ll Learn
- Build Shallow Neural Network models to forecast CO2 emissions using Python
- Apply a proven 10-step methodology for creating statistically sound and reliable forecasts
- Work with real World Bank data to analyze emissions trends for India, China, USA, UK, EU and global averages
- Master essential statistical tests including overfitting analysis, naive model benchmarking, and sensitivity analysis
- Quantify forecast uncertainty using confidence intervals and error metrics like MAPE
- Create publication-ready visualizations of historical trends and future projections
- Understand when Shallow Neural Network modelling is appropriate for time series forecasting vs other methods
- Implement best practices for model validation, hyperparameter tuning, and results interpretation
Skills covered in this course
Reviews
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AAllan Huber
The final project was challenging but incredibly rewarding—a great way to cement knowledge.
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RRoyal Glass
I loved how the course built up from basics to advanced concepts seamlessly.
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FFelix Osborne
A perfect blend of theory, coding, and real-world applications—highly recommended!
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RRemy Johnston
The instructor’s clear explanations made even the toughest topics digestible
