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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data import/export operations 3. Data sources and acquisition methods - Introduction to Data Analysis 1. Data analysis process lifecycle 2. Types of data (structured, unstructured, semi-structured) 3. Data analysis concepts and terminology
Topic 2: Python Programming for Data Analysis	30%	- File Operations 1. Context managers (with statement) 2. Reading from files (text, CSV) 3. Writing to files - Control Flow and Functions 1. Conditional statements (if, elif, else) 2. Function definitions and parameters 3. Return values and scope 4. Loops (for, while) - Python Data Types and Structures 1. Lists, tuples, dictionaries, sets 2. Numbers, strings, booleans 3. Data type conversions
Topic 3: Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Descriptive statistics computation 2. Correlation analysis 3. Data distribution analysis 4. Pattern identification - Data Analysis Workflow 1. Problem definition 2. Analysis and modeling 3. Data exploration and cleaning 4. Results interpretation and presentation
Topic 4: Working with Data Using Python Libraries	30%	- NumPy Fundamentals 1. Array indexing and slicing 2. Vectorized operations 3. Basic statistical functions 4. NumPy arrays and operations - Pandas Library 1. Handling missing data 2. DataFrame operations (merge, join, concat) 3. GroupBy operations 4. Data selection and filtering 5. Series and DataFrame structures - Data Visualization 1. Matplotlib basics 2. Seaborn introduction 3. Customizing plots 4. Creating basic charts (line, bar, scatter, histogram)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q128-Q133):

NEW QUESTION # 128
What may occur if a model is evaluated using the same data it was trained on?

A. Data leakage
B. Overfitting
C. Overgeneralization
D. Underfitting

Answer: B

NEW QUESTION # 129
What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

A. Use df.merge() with how='outer'
B. Use df.sort_values() to sort both columns before merging
C. Use df.dropna() on the foreign key column
D. Use .isin() to compare the foreign key column against the parent key column

Answer: D

NEW QUESTION # 130
Which metric is most appropriate for evaluating the accuracy of a classification model?

A. R-squared
B. Confusion matrix
C. Mean squared error
D. Precision

Answer: D

NEW QUESTION # 131
What is the function of the melt() operation in Pandas when reshaping wide-format data?

A. Sorts the DataFrame based on a column
B. Converts column values into rows
C. Combines multiple DataFrames
D. Transposes the DataFrame

Answer: B

NEW QUESTION # 132
When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

A. std()
B. mean()
C. skew()
D. sum()

Answer: C

NEW QUESTION # 133
......

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