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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals - Descriptive statistics 1. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis
Topic 2: Data Visualization and Communication	15%	- Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Color, layout and clarity 2. Choosing appropriate chart types 3. Audience-focused presentation - Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Customization and styling 3. Heatmaps, pair plots and correlation matrices
Topic 3: SQL and Database Integration	10%	- Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 4: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Hyperparameter tuning basics 3. Overfitting, underfitting and generalization
Topic 5: Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data collection methods and sources 2. Data formats and storage systems 3. Data integration and merging - Data cleaning and validation 1. Data standardization and transformation 2. Handling missing, duplicate and invalid values 3. Quality assurance and validation techniques - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Data reshaping and aggregation 3. Indexing, filtering, sorting and grouping

Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:

Question 1

What distinguishes structured data from unstructured data in the context of data modeling and storage?

A. Unstructured data cannot be stored or analyzed
B. Structured data follows a consistent schema or format
C. Structured data cannot be stored in databases
D. Unstructured data contains only numeric values

Question 2

Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

A. Adding plot titles and axis labels
B. Changing plot color palettes
C. Inserting audio annotations
D. Automatic plot animations

Question 3

Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

A. It uses random sampling with replacement
B. It enables confidence interval estimation
C. It requires a large population sample
D. It assumes a normal distribution

Question 4

What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

A. To avoid using loops and conditional logic
B. To encapsulate related data and behavior for better organization
C. To define global variables that persist across files
D. To ensure all values are stored as plain strings

Question 5

Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They are useful for regression and classification tasks
B. They work only with numerical features
C. They automatically detect outliers
D. They require labeled training data

Solutions:

Question 1
Answer: B

Question 2
Answer: A,B

Question 3
Answer: A,B

Question 4
Answer: B

Question 5
Answer: A,D