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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Modeling and Machine Learning Basics	20%	- Supervised learning fundamentals 1. Regression: linear, multiple, polynomial 2. Classification: logistic regression, k-NN, decision trees 3. Model training, testing and evaluation - Model performance and optimization 1. Overfitting, underfitting and generalization 2. Accuracy, precision, recall, F1-score 3. Hyperparameter tuning basics
Topic 2: Data Acquisition and Preprocessing	30%	- Data cleaning and validation 1. Quality assurance and validation techniques 2. Handling missing, duplicate and invalid values 3. Data standardization and transformation - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Indexing, filtering, sorting and grouping 3. Data structures: Series, DataFrame, ndarray - Data collection, integration and storage 1. Data integration and merging 2. Data collection methods and sources 3. Data formats and storage systems
Topic 3: SQL and Database Integration	10%	- SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Connecting to SQLite, MySQL or PostgreSQL 2. Error handling and best practices 3. Executing queries and retrieving results - Relational database concepts 1. Tables, keys, relationships and normalization
Topic 4: Data Exploration and Statistical Analysis	25%	- Exploratory data analysis 1. Feature selection and dimensionality reduction basics 2. Identifying patterns, trends and outliers - Descriptive statistics 1. Correlation and covariance analysis 2. Frequency distributions and percentiles 3. Measures of central tendency and dispersion - Inferential statistics 1. Hypothesis testing and confidence intervals 2. Statistical significance and interpretation 3. Probability concepts and distributions
Topic 5: Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Heatmaps, pair plots and correlation matrices 3. Line, bar, scatter, histogram, box plots - Data storytelling and reporting 1. Written and verbal presentation techniques 2. Structuring insights and conclusions - Visualization principles and best practices 1. Choosing appropriate chart types 2. Color, layout and clarity 3. Audience-focused presentation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

A. SELECT
B. INSERT
C. DELETE
D. UPDATE

Answer: B,D

NEW QUESTION # 19
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Logistic regression outputs probabilities, while linear regression predicts continuous values
B. Logistic regression uses the logit function to model relationships
C. Linear regression applies only to classification tasks
D. Linear regression requires target variables to be binary

Answer: A,B

NEW QUESTION # 20
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A. Transactional data
B. Web scraping
C. Surveys
D. Sensor logs

Answer: C

NEW QUESTION # 21
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

A. Heatmap
B. Line chart
C. Histogram
D. Scatter plot

Answer: A,D

NEW QUESTION # 22
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They are useful for regression and classification tasks
B. They automatically detect outliers
C. They require labeled training data
D. They work only with numerical features

Answer: A,C

NEW QUESTION # 23
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