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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q45-Q50):

NEW QUESTION # 45
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

Answer: A

Explanation:
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].


NEW QUESTION # 46
You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

Answer: C


NEW QUESTION # 47
A program uses while False: followed by print statements inside the loop. The developer expects output. What will actually happen during execution?

Answer: D

Explanation:
The condition False is evaluated before entering the loop. Since it is always false, the loop body is never executed, and no output is produced.


NEW QUESTION # 48
You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

Answer: C

Explanation:
Stripping removes leading and trailing whitespace from the line, and then splitting on the comma- space delimiter correctly separates the values into a list for further formatting.


NEW QUESTION # 49
A Python script evaluates the expression len(" "). The programmer assumes it might be zero because it looks empty. What will the actual result be?

Answer: B

Explanation:
A space is still a valid character in a string. Therefore, the string contains one character, and len("
") returns 1 instead of 0.


NEW QUESTION # 50
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