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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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

1. A variable is assigned using chained assignment: a = b = c = 10. The developer then changes b =
5. What will be the value of a after this operation?

A) 10
B) 5
C) Error
D) None


2. A Python loop uses range(5, 0, -2) to iterate backward. The programmer expects a decreasing sequence. Which values will actually be generated when this loop runs?

A) 5, 4, 3, 2, 1
B) 4, 2, 0
C) 5, 3, 1
D) 5, 3


3. You are reviewing a report that compares the number of weekly client meetings and project completion rates across teams. The table below summarizes the data for each team:

Which statement best describes the data shown above? Select the best answer.

A) The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
B) "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."
C) "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."
D) "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."


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

A) Infinite loop
B) No execution
C) Prints once
D) Error


5. 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?

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


Solutions:

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

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