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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Python Basics for Data Analysis32.5%- File Handling
  • 1. Read from and write to files (text, CSV)
  • 2. Use the csv module for basic CSV operations
- 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. Work with lists, tuples, dictionaries, and sets
  • 2. Apply common operations and methods to data structures
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 Analysis with Python Libraries
  • 1. Perform basic operations with NumPy arrays
  • 2. Use the math and statistics modules for basic calculations
  • 3. Utilize the collections module for specialized containers
  • 4. Work with the datetime module for date/time data
- Data Cleaning and Transformation
  • 1. Perform basic data transformation (filtering, sorting, grouping)
  • 2. Handle missing and inconsistent data
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. Structure insights as a narrative
  • 2. Create clear and concise analytical reports
  • 3. Present insights with visual and verbal techniques
Introduction to Data and Data Analysis Concepts22.5%- 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
- Data Ethics and Privacy
  • 1. Recognize ethical considerations in data handling
  • 2. Understand basic data privacy concepts
- Define and Classify Data
  • 1. Explain how data becomes meaningful
  • 2. Classify data as quantitative or qualitative
  • 3. Differentiate structured, semi-structured, and unstructured data

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q43-Q48):

NEW QUESTION # 43
A script attempts to divide two numbers using / and //. The values are 7 and 2. The developer wants to understand the difference between true division and floor division. What are the results respectively?

Answer: D

Explanation:
The / operator performs true division, returning a float result of 3.5. The // operator performs floor division, truncating the decimal part and returning the integer value 3.


NEW QUESTION # 44
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: B

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 # 45
A program compares two values using == and is. The variables a = 256 and b = 256 are defined.
What is the expected result of a is b in most Python implementations?

Answer: C

Explanation:
Python caches small integers for efficiency. Since 256 is typically within the cached range, both variables reference the same object in memory, making a is b evaluate to True.


NEW QUESTION # 46
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 # 47
You are given two lists representing daily page views and sign-ups on a website:
views = [120, 130, 128, 700, 115, 123, 119, 680, 122]
signups = [12, 15, 13, 50, 11, 14, 10, 55, 13]
You want to:
- remove outliers (views > 600), and
- calculate correlation between the cleaned lists.
Which code accomplishes this correctly? Select the best answer.

Answer: A

Explanation:
It converts the lists to NumPy arrays, builds a boolean mask based on the views threshold to remove outliers, applies the same mask to both arrays so they stay aligned, and then computes the correlation on the cleaned pairs using np.corrcoef.


NEW QUESTION # 48
......

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