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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Introduction to Data and Data Analysis Concepts22.5%- Data Ethics and Privacy
  • 1. Recognize ethical considerations in data handling
  • 2. Understand basic data privacy concepts
- 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 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
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. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets
- 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 Visualization
  • 1. Select appropriate visuals for different data types
  • 2. Interpret simple data visualizations
  • 3. Recognize common visualization types (bar, line, pie charts)
- 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
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 Cleaning and Transformation
  • 1. Handle missing and inconsistent data
  • 2. Perform basic data transformation (filtering, sorting, grouping)
- Data Analysis with Python Libraries
  • 1. Utilize the collections module for specialized containers
  • 2. Work with the datetime module for date/time data
  • 3. Perform basic operations with NumPy arrays
  • 4. Use the math and statistics modules for basic calculations

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q49-Q54):

NEW QUESTION # 49
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: A

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 # 50
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: A

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 # 51
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

Answer: D

Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.


NEW QUESTION # 52
You have a list of test scores, where each entry includes a student name and a score. Some students appear more than once. You want to compute the average score for each student and store the results in a dictionary. Here's the partial code block:

Which code correctly replaces the # MISSING CODE comment to calculate the average score for each student? Select the best answer.

Answer: A

Explanation:
It iterates through each record, extracts the name and score, and uses dictionary .get() to accumulate total scores and counts per student safely, initializing missing keys to zero. This correctly enables calculation of each student's average afterward.


NEW QUESTION # 53
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 # 54
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