Data Analysis with Python: PCAD Exam Preparation
Build on your entry-level skills and handle real analytical work from start to finish.
About this course
Build on your entry-level skills and handle real analytical work from start to finish. Over seven days you will combine data from databases and APIs, write analytical SQL, and prepare complex datasets with advanced pandas techniques.
You will then test hypotheses, build and evaluate regression and basic machine learning models, and turn results into reports and dashboards that support decisions. Practice questions throughout prepare you for the Certified Associate Data Analyst with Python exam.
What you'll learn
- Acquire and combine data from files, relational databases and APIs
- Write SQL queries with joins, grouping and filtering for analysis
- Prepare complex datasets with advanced pandas techniques
- Apply inferential statistics and hypothesis testing to business questions
- Build and evaluate regression and basic machine learning models
- Design visualisations and reports that support decisions
- Approach the PCAD exam with a clear understanding of its format and question styles
Curriculum
- From business question to analysis plan
- Reproducible analysis workflows
- Documenting assumptions and decisions
- Data ethics and governance
- SQL joins, subqueries and aggregation
- Connecting Python to databases
- Working with REST APIs and JSON
- Combining data from several sources
- Reshaping with pivot and melt
- Working with dates and times
- Text cleaning and categorical data
- Feature engineering basics
- Data quality checks
- Univariate and multivariate exploration
- Detecting patterns, trends and anomalies
- Segmenting and comparing groups
- Summarising insights
- Sampling and confidence intervals
- Hypothesis testing
- t-tests and chi-square tests
- Interpreting p-values and effect sizes
- Simple and multiple linear regression
- Logistic regression concepts
- Model assumptions and diagnostics
- Evaluating model performance
- Supervised and unsupervised learning
- Training and test splits
- Working with scikit-learn
- Clustering basics
- Avoiding overfitting
- Advanced charts with Matplotlib and Seaborn
- Designing dashboards and reports
- Communicating uncertainty
- Writing data-driven recommendations
- PCAD exam structure and question types
- Timed practice questions
- Reviewing weak areas
- Test-day preparation
Who it's for
Analysts with a foundation in Python and statistics who want to deepen their skills and earn the PCAD certification.
Before you start
- Knowledge equivalent to the PCED level, or our Data Analysis with Python: PCED Exam Preparation course
- Working knowledge of Python, pandas and basic statistics
Prepares for
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