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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q116-Q121):

NEW QUESTION # 116
What is a major challenge in aggregating data from multiple sources?
Response:

A. Duplicate records and format inconsistencies
B. Increased algorithmic complexity
C. Excessive disk space usage
D. Overfitting in predictive models

Answer: A

NEW QUESTION # 117
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

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

Answer: A,B

NEW QUESTION # 118
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

A. A strong negative linear relationship
B. No correlation
C. A weak positive relationship
D. A moderate non-linear association

Answer: A

NEW QUESTION # 119
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

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

Answer: B

NEW QUESTION # 120
Which file format is most suitable for exchanging large tabular datasets with consistent column data types across systems?

A. .xml
B. .csv
C. .json
D. .txt

Answer: B

NEW QUESTION # 121
......

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