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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions
Topic 2: Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data formats and storage systems 2. Data integration and merging 3. Data collection methods and sources - Data cleaning and validation 1. Handling missing, duplicate and invalid values 2. Quality assurance and validation techniques 3. Data standardization and transformation - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation
Topic 3: 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. Written and verbal presentation techniques 2. Structuring insights and conclusions
Topic 4: SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - Python-database connectivity 1. Executing queries and retrieving results 2. Error handling and best practices 3. Connecting to SQLite, MySQL or PostgreSQL - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
Topic 5: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Model training, testing and evaluation 3. Classification: logistic regression, k-NN, decision trees - Model performance and optimization 1. Overfitting, underfitting and generalization 2. Accuracy, precision, recall, F1-score 3. Hyperparameter tuning basics

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)

A. Construct queries using f-strings for readability
B. Limit database user permissions to only necessary operations
C. Store credentials securely using environment variables
D. Sanitize input by replacing dangerous characters with asterisks

Answer: B,C

NEW QUESTION # 29
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 # 30
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

A. csv
B. openpyxl
C. sqlite3
D. matplotlib

Answer: B

NEW QUESTION # 31
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A. Parquet files can only be used with Excel
B. Parquet stores data in plain text, making it human-readable
C. Parquet supports schema evolution and columnar storage
D. Parquet offers row-based compression

Answer: C

NEW QUESTION # 32
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A. numpy.outer()
B. array1.dot(array2)
C. array1 * array2
D. numpy.matrix()

Answer: C

NEW QUESTION # 33
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