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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
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. Hypothesis testing and confidence intervals 2. Probability concepts and distributions 3. Statistical significance and interpretation
Data Acquisition and Preprocessing	30%	- Data collection, integration and storage 1. Data integration and merging 2. Data formats and storage systems 3. Data collection methods and sources - Data preparation with Pandas and NumPy 1. Data reshaping and aggregation 2. Indexing, filtering, sorting and grouping 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 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
SQL and Database Integration	10%	- 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 - SQL querying 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions 2. Subqueries and filtering
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 - Data storytelling and reporting 1. Structuring insights and conclusions 2. Written and verbal presentation techniques - Visualization principles and best practices 1. Audience-focused presentation 2. Color, layout and clarity 3. Choosing appropriate chart types

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

1. What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

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

2. Which practices are considered part of professional Python scripting standards?
(Choose two)

A) Using consistent indentation and naming conventions
B) Avoiding error handling to keep code clean
C) Including in-line comments and docstrings
D) Avoiding use of modules and libraries

3. Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A) Presence of duplicate data
B) Boolean feature encoding
C) Features with different units and scales
D) Distance-based modeling algorithms

4. Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

A) flask
B) pandas
C) NumPy
D) Pygame

5. Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

A) Imputation using mean or median
B) Dropping all columns
C) Filling with random values
D) Replacing with NULL

Solutions:

Question # 1 Answer: D

Question # 2 Answer: A,C

Question # 3 Answer: C,D

Question # 4 Answer: B,C

Question # 5 Answer: A