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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Which of the following would most likely be classified as unstructured data?

A) Normalized employee database in PostgreSQL
B) Product ID and price list in a CSV
C) Inventory levels in a spreadsheet
D) Collection of customer service audio recordings

2. Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

A) pandas
B) flask
C) NumPy
D) Pygame

3. Why is it important to define and apply validation rules consistently across all stages of a data pipeline?

A) It guarantees compatibility with visualization tools
B) It automatically optimizes SQL queries
C) It prevents the propagation of corrupted or invalid data
D) It reduces processing time by skipping null checks

4. What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

A) A strong negative linear relationship
B) No correlation
C) A weak positive relationship
D) A moderate non-linear association

5. Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

A) var()
B) median()
C) sort_values()
D) pivot_table()

Solutions:

Question # 1
Answer: D

Question # 2
Answer: A,C

Question # 3
Answer: A

Question # 4
Answer: A

Question # 5
Answer: A,B