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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q139-Q144):

NEW QUESTION # 139
Which method in a Python class is responsible for initializing the object's attributes at the time of creation?

A. __init__()
B. __main__()
C. __str__()
D. __getattr__()

Answer: A

NEW QUESTION # 140
What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

A. Use df.dropna() on the foreign key column
B. Use df.sort_values() to sort both columns before merging
C. Use .isin() to compare the foreign key column against the parent key column
D. Use df.merge() with how='outer'

Answer: C

NEW QUESTION # 141
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

A. list
B. set
C. dictionary
D. tuple

Answer: B

NEW QUESTION # 142
What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

A. Exposure to SQL injection attacks
B. Increased memory usage
C. Slower database performance
D. Missing foreign key constraints

Answer: A

NEW QUESTION # 143
Which of the following statements correctly describe best practices when managing Python modules and packages in data analytics workflows?
(Choose two)

A. Use PIP to install and uninstall third-party libraries as needed
B. Always install packages system-wide for accessibility
C. Regularly update all packages using pip update all
D. Use virtual environments to isolate project dependencies

Answer: A,D

NEW QUESTION # 144
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