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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Line, bar, scatter, histogram, box plots 2. Heatmaps, pair plots and correlation matrices 3. Customization and styling - 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
Topic 2: Data Acquisition and Preprocessing	30%	- Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Data reshaping and aggregation 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 formats and storage systems 2. Data integration and merging 3. Data collection methods and sources
Topic 3: SQL and Database Integration	10%	- 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 - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
Topic 4: Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics - Descriptive statistics 1. Frequency distributions and percentiles 2. Correlation and covariance analysis 3. Measures of central tendency and dispersion - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals
Topic 5: Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Hyperparameter tuning basics 2. Accuracy, precision, recall, F1-score 3. Overfitting, underfitting and generalization - Supervised learning fundamentals 1. Model training, testing and evaluation 2. Regression: linear, multiple, polynomial 3. Classification: logistic regression, k-NN, decision trees

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q77-Q82):

NEW QUESTION # 77
What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

A. Abstraction
B. Composition
C. Encapsulation
D. Inheritance

Answer: D

NEW QUESTION # 78
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

A. Replacing all functions with lambda expressions
B. Applying inheritance to avoid redundant code
C. Using global variables instead of object properties
D. Encapsulating behavior in class methods

Answer: B,D

NEW QUESTION # 79
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.title()
B. plt.xlabel()
C. plt.legend()
D. plt.annotate()

Answer: D

NEW QUESTION # 80
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A. str() function to convert data formats
B. id() function to retrieve unique object identifiers
C. is operator for memory-level comparison
D. == operator for checking value equivalence

Answer: B,C

NEW QUESTION # 81
What is a key assumption of linear regression that distinguishes it from logistic regression?

A. Predictors must be independent
B. The target variable is unordered categorical
C. Output values must be binary
D. The relationship between predictors and the outcome is linear

Answer: D

NEW QUESTION # 82
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