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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Working with Data and Performing Simple Analyses32.5%- Simple Analytical Techniques
  • 1. Identify basic patterns and trends in data
  • 2. Calculate descriptive statistics (mean, median, mode)
- Data Cleaning and Transformation
  • 1. Handle missing and inconsistent data
  • 2. Perform basic data transformation (filtering, sorting, grouping)
- Data Analysis with Python Libraries
  • 1. Perform basic operations with NumPy arrays
  • 2. Utilize the collections module for specialized containers
  • 3. Work with the datetime module for date/time data
  • 4. Use the math and statistics modules for basic calculations
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
- File Handling
  • 1. Use the csv module for basic CSV operations
  • 2. Read from and write to files (text, CSV)
- Data Structures
  • 1. Work with lists, tuples, dictionaries, and sets
  • 2. Apply common operations and methods to data structures
Introduction to Data and Data Analysis Concepts22.5%- Data Analysis Process and Workflow
  • 1. Describe the steps of the data analysis process
  • 2. Identify common data sources and collection methods
  • 3. Explain the role of data cleaning and preparation
- Data Ethics and Privacy
  • 1. Understand basic data privacy concepts
  • 2. Recognize ethical considerations in data handling
- 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
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 (Q38-Q43):

NEW QUESTION # 38
A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?

Answer: D

Explanation:
The break statement immediately terminates the loop, regardless of remaining iterations. Control is transferred to the first statement following the loop block.


NEW QUESTION # 39
You are working with city names entered by users. These names may contain inconsistent capitalization and unwanted spaces.
To standardize the data, you want to:
- Remove any leading or trailing whitespace, and
- Capitalize the first letter of each word (e.g., convert "new york" to "New York").
For example:
" New york " → "New York"
"lOS ANGELES" → "Los Angeles"
You are given a variable citythat contains the raw input.
Which line of code correctly updates the value of cleaned_cityto apply the required transformation? Select the best answer.

Answer: C

Explanation:
Stripping removes leading and trailing whitespace, and applying title formatting capitalizes the first letter of each word while converting the remaining letters to lowercase, producing the correctly standardized city name.


NEW QUESTION # 40
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: C

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 # 41
You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

Answer: D

Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.


NEW QUESTION # 42
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: C

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 # 43
......

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