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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Introduction to Data and Data Analysis Concepts22.5%- Define and Classify Data
  • 1. Classify data as quantitative or qualitative
  • 2. Differentiate structured, semi-structured, and unstructured data
  • 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. Identify common data sources and collection methods
  • 2. Describe the steps of the data analysis process
  • 3. Explain the role of data cleaning and preparation
Working with Data and Performing Simple Analyses32.5%- 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
- Simple Analytical Techniques
  • 1. Identify basic patterns and trends in data
  • 2. Calculate descriptive statistics (mean, median, mode)
Communicating Insights and Reporting12.5%- Data Visualization
  • 1. Select appropriate visuals for different data types
  • 2. Recognize common visualization types (bar, line, pie charts)
  • 3. Interpret simple data visualizations
- 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
Python Basics for Data Analysis32.5%- Python Fundamentals
  • 1. Define and use functions
  • 2. Use variables, data types, and basic operators
  • 3. Implement control flow structures (loops, conditionals)
- Data Structures
  • 1. Work with lists, tuples, dictionaries, and sets
  • 2. Apply common operations and methods to data structures
- File Handling
  • 1. Read from and write to files (text, CSV)
  • 2. Use the csv module for basic CSV operations

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q42-Q47):

NEW QUESTION # 42
How do the analysis and visualization stages of the data lifecycle typically work together?

Answer: C

Explanation:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.


NEW QUESTION # 43
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?

Answer: C

Explanation:
The not operator has higher precedence than and, so not False becomes True first. Then True and True is evaluated, resulting in True as the final Boolean value.


NEW QUESTION # 44
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

Answer: D

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.


NEW QUESTION # 45
Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

Answer: C,E,F

Explanation:
The slice text[6:8] is "20", which contains only digits, and the string ends with "Final". The first six characters are "Report", which are all alphabetic, and "2024" begins at index 6.


NEW QUESTION # 46
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: A

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 # 47
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