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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q78-Q83):

NEW QUESTION # 78
Why is it important to adjust data presentations based on the audience's background?

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

Answer: C

NEW QUESTION # 79
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

A. FROM
B. HAVING
C. GROUP BY
D. JOIN ON

Answer: D

NEW QUESTION # 80
Which element is essential to justify a conclusion drawn from a dataset?

A. The origin of the dataset
B. File format of the source data
C. The use of color in plots
D. Logical reasoning and supported metrics

Answer: D

NEW QUESTION # 81
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

A. Key-value mapping
B. Index-based iteration
C. Append
D. Slicing

Answer: C,D

NEW QUESTION # 82
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.rename()
B. df.replace()
C. df.dropna()
D. df.sort_values()

Answer: B

NEW QUESTION # 83
......

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