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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data collection methods and sources 2. Data integration and merging 3. Data formats and storage systems - Data preparation with Pandas and NumPy 1. Data structures: Series, DataFrame, ndarray 2. Indexing, filtering, sorting and grouping 3. Data reshaping and aggregation - 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%	- 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. Hyperparameter tuning basics 2. Overfitting, underfitting and generalization 3. Accuracy, precision, recall, F1-score
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. Measures of central tendency and dispersion 2. Frequency distributions and percentiles 3. Correlation and covariance analysis - Inferential statistics 1. Probability concepts and distributions 2. Statistical significance and interpretation 3. Hypothesis testing and confidence intervals
SQL and Database Integration	10%	- Relational database concepts 1. Tables, keys, relationships and normalization - SQL querying 1. Subqueries and filtering 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions - Python-database connectivity 1. Error handling and best practices 2. Executing queries and retrieving results 3. Connecting to SQLite, MySQL or PostgreSQL
Data Visualization and Communication	15%	- Visualization with Matplotlib and Seaborn 1. Customization and styling 2. Line, bar, scatter, histogram, box plots 3. Heatmaps, pair plots and correlation matrices - Visualization principles and best practices 1. Audience-focused presentation 2. Color, layout and clarity 3. Choosing appropriate chart types - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques

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

1. Which method in a Python class is responsible for initializing the object's attributes at the time of creation?

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

2. Why is it important to adjust data presentations based on the audience's background?

A) To include as many technical terms as possible
B) To ensure the data is understood and supports actionable insights
C) To simplify all metrics to percentages only
D) To avoid using charts altogether

3. Which of the following practices contributes most to making Python scripts modular and maintainable?

A) Using global variables freely
B) Writing all logic in the global scope
C) Structuring code with reusable functions and modules
D) Hard-coding file paths in every function

4. Which method is commonly used to handle missing numerical data in a dataset?

A) Dropping all columns
B) Replacing with text labels
C) Filling with the mean or median
D) Multiplying all missing values by zero

5. In supervised learning, what is the primary goal when training a model on labeled data?

A) To cluster data points into similar groups
B) To analyze unstructured data formats like text
C) To learn a function that maps inputs to known outputs
D) To discover hidden patterns without outputs

Solutions:

Question # 1
Answer: D

Question # 2
Answer: B

Question # 3
Answer: C

Question # 4
Answer: C

Question # 5
Answer: C