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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

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

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

2. 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

3. Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A) numpy.outer()
B) numpy.matrix()
C) array1.dot(array2)
D) array1 * array2

4. Which practices help maintain data integrity across large datasets in Pandas workflows?
(Choose two)

A) Automatically converting all text to uppercase
B) Sorting columns alphabetically
C) Defining and enforcing schema constraints
D) Using assertions to enforce domain-specific rules

5. What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

A) Execute → Connect → Commit → Close
B) Commit → Connect → Execute → Close
C) Connect → Execute → Commit → Close
D) Connect → Commit → Execute → Close

Solutions:

Question # 1
Answer: A,C

Question # 2
Answer: B

Question # 3
Answer: D

Question # 4
Answer: C,D

Question # 5
Answer: C