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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Working with Data and Performing Simple Analyses32.5%- Simple Analytical Techniques
  • 1. Identify basic patterns and trends in data
  • 2. Calculate descriptive statistics (mean, median, mode)
- Data Analysis with Python Libraries
  • 1. Use the math and statistics modules for basic calculations
  • 2. Perform basic operations with NumPy arrays
  • 3. Work with the datetime module for date/time data
  • 4. Utilize the collections module for specialized containers
- Data Cleaning and Transformation
  • 1. Perform basic data transformation (filtering, sorting, grouping)
  • 2. Handle missing and inconsistent data
Communicating Insights and Reporting12.5%- Data Visualization
  • 1. Recognize common visualization types (bar, line, pie charts)
  • 2. Interpret simple data visualizations
  • 3. Select appropriate visuals for different data types
- Data Storytelling and Reporting
  • 1. Structure insights as a narrative
  • 2. Present insights with visual and verbal techniques
  • 3. Create clear and concise analytical reports
Python Basics for Data Analysis32.5%- Data Structures
  • 1. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets
- Python Fundamentals
  • 1. Define and use functions
  • 2. Use variables, data types, and basic operators
  • 3. Implement control flow structures (loops, conditionals)
- File Handling
  • 1. Use the csv module for basic CSV operations
  • 2. Read from and write to files (text, CSV)
Introduction to Data and Data Analysis Concepts22.5%- Data Ethics and Privacy
  • 1. Understand basic data privacy concepts
  • 2. Recognize ethical considerations in data handling
- Data Analysis Process and Workflow
  • 1. Identify common data sources and collection methods
  • 2. Explain the role of data cleaning and preparation
  • 3. Describe the steps of the data analysis process
- Define and Classify Data
  • 1. Classify data as quantitative or qualitative
  • 2. Explain how data becomes meaningful
  • 3. Differentiate structured, semi-structured, and unstructured data

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q38-Q43):

NEW QUESTION # 38
You want to safely read the contents of a file named records.txtand store them in a list. Your requirements are:
- The file should be automatically closed after reading,
- The code should check whether the file exists before opening it, and
- It should handle missing file errors gracefully.
Which of the following is the most appropriate way to accomplish this task in Python? Select the best answer.

Answer: D

Explanation:
It uses a context manager to automatically close the file after reading and wraps the operation in a try-except block to gracefully handle a missing file error, assigning an empty list if the file is not found.


NEW QUESTION # 39
You are writing a function named process_data()to read and process numerical input from a file. The function must:
- read the file data.txt,
- attempt to convert the first line into an integer,
- handle file, conversion, or index-related exceptions,
- print the value only if no error occurs,
- and always print a final message after execution.
Which implementation correctly and robustly meets all these conditions? Select the best answer.

Answer: C

Explanation:
It wraps file reading and integer conversion in a try block, catches the relevant file, conversion, and indexing exceptions, prints the value only in the no-error path using an else block, and uses a finally block to always print the final message.


NEW QUESTION # 40
You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

Answer: B

Explanation:
It first handles the non-numeric cases ("MISSING" and None) in a single condition, preventing invalid comparisons, and then cleanly checks the numeric outlier bounds using an or condition (<
2.5 or > 4.2) before defaulting to "Normal".


NEW QUESTION # 41
A script defines a variable x = None and checks its truth value using a conditional statement. The developer wants to understand how Python evaluates None in Boolean contexts. What will bool(x) return?

Answer: B

Explanation:
The value None represents the absence of a value and is treated as False in Boolean expressions. Therefore, calling bool(None) returns False, similar to empty containers or zero values.


NEW QUESTION # 42
A program compares two values using == and is. The variables a = 256 and b = 256 are defined.
What is the expected result of a is b in most Python implementations?

Answer: C

Explanation:
Python caches small integers for efficiency. Since 256 is typically within the cached range, both variables reference the same object in memory, making a is b evaluate to True.


NEW QUESTION # 43
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