ESG Data Science Foundations with Python & AI
Turn ESG questions into trustworthy data analysis with Python, pandas, and AI. Learn to inspect, clean, calculate, visualize, and verify sustainability data, then complete your own ESG Data Analysis Use Case.
The course storyline: from ESG questions to trustworthy evidence
The whole course follows one line: Question, Data, Python, Analysis, Verification, Communication. AI may assist the analysis; people stay responsible for the evidence and the conclusion.
- 1. Question
What exactly are we trying to answer?
- 2. Data
Which columns, grain and units can answer it?
- 3. Python
Which minimal pandas steps get us there?
- 4. Analysis
What do the metrics and the chart show?
- 5. Verification
Have the formula, units, output and claims been checked?
- 6. Communication
How do we keep facts, interpretation and unknowns apart?
Your next step
DS 101 · Module 1: From ESG Questions to Python Data Analysis
Curriculum
- Module 1From ESG Questions to Python Data AnalysisStart with ESG questions, dataset structure, and core metrics, then use the minimum necessary Python and pandas to complete your first reproducible analysis.
- Module 2Trustworthy ESG Analysis with AI AssistanceHandle data quality, calculate ESG metrics, build visualizations, and use AI to assist analysis while verifying code, numbers, and conclusions.
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Practice and assessment
- Practice A: Guided LabComplete your first ESG analysis on clean synthetic data: Scope 1 + 2, emissions intensity and three evidence statements.Not done yet
- Practice B: Data Quality ChallengeFind missing values, duplicates, type, range, outlier and naming problems in the practice data, and record your decisions.Not done yet
- Practice C: Independent LabComplete a trustworthy analysis on your own: quality checks, cleaning, metrics, a chart, verification and a record of AI assistance.Not done yet
- DS 101 pre-test and post-testPre- and post-tests check how your data thinking, Python reading, ESG metric reasoning and verification skills change.Not done yet
- ESG Data Analysis Use CaseTurn what you learned into your own ESG Data Analysis Use Case, stating the data, metrics, analysis steps and how you will verify them.Not done yet
About this course
Outcomes
- Turn an ESG or sustainability question into an analyzable data question
- Explain the basic concepts of dataset, observation, variable, metric, and missing value
- Use Python and pandas to load, inspect, select, filter, and aggregate ESG data
- Identify data-quality issues involving missing values, duplicates, units, types, and outliers
- Calculate basic ESG metrics including Scope 1 + 2 emissions, emissions intensity, and year-over-year change
- Create basic visualizations for comparison and trend analysis
- Use generative AI to assist with writing, explaining, and debugging analysis code
- Verify AI-generated code, formulas, units, outputs, and written conclusions
- Complete an ESG Data Analysis Use Case for further implementation
Prerequisites
- No programming background required
- AI 101 is recommended but not required
- You can use a browser, understand rows and columns at a spreadsheet level, and download and upload files
- You can copy and run code cells in a notebook environment (Python 3, pandas, matplotlib)
Who it is for
- Students, faculty and administrative staff who want to move from Excel and spreadsheets to reproducible data analysis
- Learners who work with ESG, sustainability, CSR, carbon management or sustainability reporting data
- Learners without a computing background who want AI to help with Python analysis while still verifying the results themselves
No programming background required. AI 101 is recommended first, but this is advice only and never blocks you from starting.
Certificate
Earns a capability certificate (L2).
Requirements to earn it
- Complete all 2 course modules
- Complete all 1 core practice tasks
- Pass the post-test assessment at 80% or higher
- Submit a project artifact and pass review
Course feedback
It takes 2 to 3 minutes and is used to improve the course.