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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

>> PCAD-31-02 Certification Exam <<

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q125-Q130):

NEW QUESTION # 125
When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

Answer: B


NEW QUESTION # 126
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?

Answer: D


NEW QUESTION # 127
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

Answer: B


NEW QUESTION # 128
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

Answer: A,B


NEW QUESTION # 129
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

Answer: C


NEW QUESTION # 130
......

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