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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A) To optimize the model's training accuracy
B) To reduce the number of iterations during training
C) To evaluate the model's performance on unseen data
D) To ensure the loss function is minimized

2. What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

A) To ensure all values are stored as plain strings
B) To avoid using loops and conditional logic
C) To encapsulate related data and behavior for better organization
D) To define global variables that persist across files

3. What is the primary goal of pre-processing data before performing analysis?

A) To ensure the data is clean, consistent, and suitable for modeling
B) To compress datasets for storage efficiency
C) To obscure sensitive information
D) To minimize the number of data points

4. What is the best approach when comparing SQL NULL values with Python data during conditional checks?

A) Convert all NULL to string before comparison
B) Use == None directly in SQL queries
C) Replace NULL with zero before importing
D) Use Python's None and SQL's IS NULL clause

5. What is the main purpose of using the bootstrapping technique in inferential statistics?

A) To filter outliers from a dataset
B) To validate the syntax of SQL queries in Python
C) To increase the number of features in a model
D) To estimate the sampling distribution by resampling with replacement

Solutions:

Question # 1
Answer: C

Question # 2
Answer: C

Question # 3
Answer: A

Question # 4
Answer: D

Question # 5
Answer: D

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