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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.sort_values()
B. df.replace()
C. df.dropna()
D. df.rename()

Answer: B

NEW QUESTION # 109
Which of the following best describes the use of relational databases in analytics?

A. Suitable for managing tabular structured data
B. Cannot enforce schema
C. Best for real-time log ingestion
D. Used only for image processing

Answer: A

NEW QUESTION # 110
Which storage system is best suited for storing and retrieving large volumes of unstructured data, such as images or logs?

A. Data cubes
B. Object storage systems
C. In-memory databases
D. Relational databases

Answer: B

NEW QUESTION # 111
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

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

Answer: B

NEW QUESTION # 112
What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

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

Answer: C

NEW QUESTION # 113
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