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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q70-Q75):

NEW QUESTION # 70
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

Answer: A


NEW QUESTION # 71
Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)

Answer: B,D


NEW QUESTION # 72
Which term best describes the process of combining customer data from multiple systems into a single unified dataset?

Answer: C


NEW QUESTION # 73
Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)

Answer: A,D


NEW QUESTION # 74
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

Answer: A,C


NEW QUESTION # 75
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