Python Institute PCAD-31-02 Valid Dumps - Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Working with Data Using Python Libraries30%- NumPy Fundamentals
  • 1. Vectorized operations
  • 2. Array indexing and slicing
  • 3. Basic statistical functions
  • 4. NumPy arrays and operations
- Pandas Library
  • 1. DataFrame operations (merge, join, concat)
  • 2. Data selection and filtering
  • 3. Handling missing data
  • 4. GroupBy operations
  • 5. Series and DataFrame structures
- Data Visualization
  • 1. Matplotlib basics
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Seaborn introduction
  • 4. Customizing plots
Topic 2: Data Analysis Fundamentals20%- 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 concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
Topic 3: Python Programming for Data Analysis30%- Python Data Types and Structures
  • 1. Data type conversions
  • 2. Numbers, strings, booleans
  • 3. Lists, tuples, dictionaries, sets
- Control Flow and Functions
  • 1. Return values and scope
  • 2. Function definitions and parameters
  • 3. Loops (for, while)
  • 4. Conditional statements (if, elif, else)
- File Operations
  • 1. Reading from files (text, CSV)
  • 2. Context managers (with statement)
  • 3. Writing to files
Topic 4: Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Descriptive statistics computation
  • 3. Correlation analysis
  • 4. Pattern identification
- Data Analysis Workflow
  • 1. Results interpretation and presentation
  • 2. Analysis and modeling
  • 3. Data exploration and cleaning
  • 4. Problem definition

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

1. When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

A) Drawing one sample and calculating the mean only once
B) Scaling all values between 0 and 1 before resampling
C) Generating multiple datasets of the same size by randomly sampling with replacement
D) Creating samples by removing all duplicates


2. Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A) It overlays multiple plots in the same figure without axes
B) It configures multiple plots in a grid layout within a single figure
C) It links Matplotlib with Seaborn's grid styling
D) It creates a 3D surface plot using pandas


3. Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A) Surveys
B) Sensor logs
C) Transactional data
D) Web scraping


4. What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

A) Seaborn provides high-level abstractions for attractive and informative graphics
B) Seaborn requires no data preprocessing for missing values
C) Seaborn automatically saves plots as image files
D) Seaborn works exclusively with NumPy arrays


5. What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

A) Connect → Execute → Commit → Close
B) Execute → Connect → Commit → Close
C) Commit → Connect → Execute → Close
D) Connect → Commit → Execute → Close


Solutions:

Question # 1
Answer: C
Question # 2
Answer: B
Question # 3
Answer: A
Question # 4
Answer: A
Question # 5
Answer: A

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