Python Fundamentals for Analysis
Practical Python for people who analyse data: the core building blocks, working with tabular data in a dataframe, vectorised thinking, missing values, grouping, quick charts, calm debugging and reproducible working habits.
52m
Total time
4
Competency levels
12
Lessons
What you'll learn
- Explain when an analysis outgrows a spreadsheet and what Python adds in repeatability, volume and auditability
- Use variables, types, lists, dictionaries, indexing, loops, conditionals and functions to build a working script
- Apply the load, inspect, clean, transform, summarise and export sequence to a CSV file using a dataframe
- Replace row-by-row loops with vectorised column operations and handle missing values deliberately
- Group and aggregate data, report counts alongside averages, and plot a quick chart to sanity-check results
- Read a traceback bottom-up to debug calmly, and work reproducibly with version control, relative paths and pinned environments
Curriculum
Progress through each competency level in order.
1
Awareness
- 3mWhy Analysts Reach for PythonKnowledge
- 2mVariables, Types, Lists and DictionariesKnowledge
- 7mAwareness Check: Why Python, and What It Is Made OfAssessment
2
Knowledge
- 2mLoops, Conditionals and FunctionsKnowledge
- 2mFrom CSV to DataframeKnowledge
- 8mKnowledge Check: Logic and the Dataframe WorkflowAssessment
3
Skill
- 3mVectorised Thinking, Missing Values and GroupingKnowledge
- 3mBuild a Repeatable Analysis ScriptAssignment
- 8mSkill Check: Working with Real DataAssessment
4
Mastery
- 3mDebugging Calmly and Working ReproduciblyKnowledge
- 3mMake an Analysis Reproducible and Hand It OverAssignment
- 8mMastery Check: Debugging and Reproducibility JudgementAssessment
Ready to build Python Fundamentals for Analysis?
Create a free account on iCompetent and start progressing through the levels today.