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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

NEW QUESTION # 78
When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)

A. on
B. ignore_index
C. dropna
D. suffixes

Answer: A,D

NEW QUESTION # 79
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

A. DataFrame.nunique()
B. DataFrame.drop_duplicates()
C. DataFrame.unique()
D. DataFrame.remove_duplicates()

Answer: D

NEW QUESTION # 80
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. Replace NULL with zero before importing
C. Use == None directly in SQL queries
D. Use Python's None and SQL's IS NULL clause

Answer: D

NEW QUESTION # 81
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A. numpy.outer()
B. array1.dot(array2)
C. numpy.matrix()
D. array1 * array2

Answer: D

NEW QUESTION # 82
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

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

Answer: B,C

NEW QUESTION # 83
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