As a first-year Chemistry student interested in energy markets, I wanted structured training in data analysis. Google’s Advanced Data Analytics Certificate – a seven-course programme covering Python, statistics, machine learning, and visualisation – gave me a systematic way to apply core analytical concepts to general datasets, building skills I can adapt to energy data in future projects.
The statistics modules taught me to:
- Calculate confidence intervals and hypothesis tests using Python
- Model probability distributions (normal, binomial) with practice datasets
- Apply sampling methods to avoid bias - techniques I can transfer to analysing energy price volatility or renewable generation trends.
Using Waze traffic data, I learned how to:
- Build simple/multiple linear regression models
- Practice Ridge/Lasso regularisation to reduce overfitting
- Conduct ANOVA and Chi-squared tests
While the course used non-energy examples, these methods are adaptable for modelling relationships like weather vs. power demand or commodity price drivers. It also introduced ML concepts:
- Clustering (K-means) to group similar patterns
- Random forests and gradient boosting for classification
- Model validation techniques
I also learned how to create Tableau visualisations and apply a methodical approach to data analysis through Google’s PACE (Plan, Analyse, Construct, Execute) framework.
For a student like me, it’s a foundation to start exploring energy datasets with more rigour.