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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
- Data Ethics and Privacy
  • 1. Understand basic data privacy concepts
  • 2. Recognize ethical considerations in data handling
- Define and Classify Data
  • 1. Explain how data becomes meaningful
  • 2. Differentiate structured, semi-structured, and unstructured data
  • 3. Classify data as quantitative or qualitative
Topic 2: 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. Create clear and concise analytical reports
  • 2. Present insights with visual and verbal techniques
  • 3. Structure insights as a narrative
Topic 3: Working with Data and Performing Simple Analyses32.5%- Data Cleaning and Transformation
  • 1. Perform basic data transformation (filtering, sorting, grouping)
  • 2. Handle missing and inconsistent data
- Simple Analytical Techniques
  • 1. Calculate descriptive statistics (mean, median, mode)
  • 2. Identify basic patterns and trends in data
- Data Analysis with Python Libraries
  • 1. Work with the datetime module for date/time data
  • 2. Utilize the collections module for specialized containers
  • 3. Perform basic operations with NumPy arrays
  • 4. Use the math and statistics modules for basic calculations
Topic 4: Python Basics for Data Analysis32.5%- Python Fundamentals
  • 1. Use variables, data types, and basic operators
  • 2. Define and use functions
  • 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)
- Data Structures
  • 1. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets

Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions:

1. 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.

A)

B)

C)

D)


2. 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.

A)

B)

C)

D)


3. A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?

A) ythn
B) ytho
C) tho
D) yth


4. A Python script includes the expression not False and True. The developer is unsure how logical operators are evaluated in terms of precedence. What will be the final Boolean result of this expression?

A) True
B) None
C) Error
D) False


5. 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?

A) fields = line.split(', ').trim()
B) fields = line.split().strip(', ')
C) fields = line.replace(', ', '|').split('|')
D) fields = line.strip().split(', ')


Solutions:

Question # 1
Answer: A
Question # 2
Answer: C
Question # 3
Answer: D
Question # 4
Answer: A
Question # 5
Answer: D

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