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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis Fundamentals20%- Introduction to Data Analysis
  • 1. Types of data (structured, unstructured, semi-structured)
  • 2. Data analysis process lifecycle
  • 3. Data analysis concepts and terminology
- Data Collection and Preparation
  • 1. Data sources and acquisition methods
  • 2. Data cleaning and preprocessing basics
  • 3. Data import/export operations
Topic 2: Working with Data Using Python Libraries30%- Data Visualization
  • 1. Matplotlib basics
  • 2. Customizing plots
  • 3. Seaborn introduction
  • 4. Creating basic charts (line, bar, scatter, histogram)
- NumPy Fundamentals
  • 1. Basic statistical functions
  • 2. Array indexing and slicing
  • 3. Vectorized operations
  • 4. NumPy arrays and operations
- Pandas Library
  • 1. Handling missing data
  • 2. GroupBy operations
  • 3. Series and DataFrame structures
  • 4. Data selection and filtering
  • 5. DataFrame operations (merge, join, concat)
Topic 3: Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Descriptive statistics computation
  • 3. Pattern identification
  • 4. Correlation analysis
- Data Analysis Workflow
  • 1. Data exploration and cleaning
  • 2. Problem definition
  • 3. Analysis and modeling
  • 4. Results interpretation and presentation
Topic 4: Python Programming for Data Analysis30%- File Operations
  • 1. Reading from files (text, CSV)
  • 2. Writing to files
  • 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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

Answer: A,D


NEW QUESTION # 72
When fetching DATETIME values from a SQL database into Python, which Python data type is most appropriate for storing and manipulating these values?

Answer: B


NEW QUESTION # 73
Which characteristics are typically associated with unstructured data formats?
(Choose two)

Answer: C,D


NEW QUESTION # 74
When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

Answer: D


NEW QUESTION # 75
Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

Answer: C,D


NEW QUESTION # 76
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