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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
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. Color, layout and clarity 3. Audience-focused presentation - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques
SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - 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
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Indexing, filtering, sorting and grouping 2. Data reshaping and aggregation 3. Data structures: Series, DataFrame, ndarray - Data cleaning and validation 1. Data standardization and transformation 2. Quality assurance and validation techniques 3. Handling missing, duplicate and invalid values
Data Modeling and Machine Learning Basics	20%	- Model performance and optimization 1. Accuracy, precision, recall, F1-score 2. Overfitting, underfitting and generalization 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
Data Exploration and Statistical Analysis	25%	- Inferential statistics 1. Statistical significance and interpretation 2. Probability concepts and distributions 3. Hypothesis testing and confidence intervals - Descriptive statistics 1. Correlation and covariance analysis 2. Measures of central tendency and dispersion 3. Frequency distributions and percentiles - Exploratory data analysis 1. Identifying patterns, trends and outliers 2. Feature selection and dimensionality reduction basics

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q92-Q97):

NEW QUESTION # 92
Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

A. Checking for duplicate rows
B. Replacing NULL values with placeholders
C. Sorting the data frame by index
D. Confirming numeric columns contain only integers

Answer: A,D

NEW QUESTION # 93
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

A. Log transformation
B. Min-max normalization
C. One-hot encoding
D. Z-score normalization

Answer: D

NEW QUESTION # 94
What is a key assumption of linear regression that distinguishes it from logistic regression?

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

Answer: A

NEW QUESTION # 95
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

A. re
B. math
C. sqlalchemy
D. pandas.read_html()

Answer: D

NEW QUESTION # 96
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)

A. Allows the use of lambda for procedural logic
B. Enables procedural programming only through decorators
C. Supports clearer abstraction by modeling real-world entities
D. Encourages code reuse through inheritance and modularity

Answer: C,D

NEW QUESTION # 97
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