Description
Turn a cleaned dataset into clear, defensible insights
This hands-on data science practical guides you through a complete exploratory data analysis workflow in Python. Using a realistic synthetic service-delivery dataset, you will ask focused questions, examine numerical and categorical variables, investigate data quality, compare groups, explore relationships, and communicate findings without overstating the evidence.
What you will practise
- Defining useful exploratory questions
- Confirming analysis readiness and variable types
- Creating a concise dataset overview
- Summarizing numerical and categorical variables
- Visualizing distributions and categorical patterns
- Investigating missing values and potential outliers
- Comparing groups and examining relationships
- Recording findings, limitations, and quality checks
- Exporting reproducible tables and figures
What you will produce
- Numerical and categorical summary tables
- Dataset overview and missingness summaries
- A documented outlier review
- Distribution, comparison, and relationship charts
- An exploratory visual summary
- A concise findings table with limitations
- Portfolio-ready evidence of your workflow
The practical includes step-by-step explanations, executable Python code, a cleaned synthetic dataset, and generated example outputs. It is suitable for students, researchers, analysts, monitoring and evaluation professionals, and learners building practical data science skills.
No real participant records are used. Basic familiarity with Python and tabular data is helpful.
This is a digital product. No physical item will be shipped.
