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Python Institute PCED-30-02 Exam Overview:

Certification Vendor:Python Institute
Exam Name:Certified Entry-Level Data Analyst with Python
Exam Number:PCED-30-02
Exam Duration:60 + NDA
Exam Price:$69 USD
Exam Format:Multiple-select, Single-select
Available Languages:Spanish, English
Certificate Validity Period:Lifetime
Real Exam Qty:40
Related Certifications:PCEP
PCAD
Passing Score:75%
Sample Questions:Python Institute PCED-30-02 Sample Questions
Exam Way:Online (remote proctored) or at authorized testing centers via TestNow™ platform.
Pre Condition:No formal prerequisites. Recommended: PCEP™ (or equivalent) and foundation in mathematics/statistics.
Official Syllabus URL:https://pythoninstitute.org/pced

Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Working with Data and Performing Simple Analyses32.5%- 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
- Data Cleaning and Transformation
  • 1. Perform basic data transformation (filtering, sorting, grouping)
  • 2. Handle missing and inconsistent data
- Simple Analytical Techniques
  • 1. Calculate descriptive statistics (mean, median, mode)
  • 2. Identify basic patterns and trends in data
Topic 2: Python Basics for Data Analysis32.5%- File Handling
  • 1. Use the csv module for basic CSV operations
  • 2. Read from and write to files (text, CSV)
- Python Fundamentals
  • 1. Define and use functions
  • 2. Implement control flow structures (loops, conditionals)
  • 3. Use variables, data types, and basic operators
- Data Structures
  • 1. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets
Topic 3: Introduction to Data and Data Analysis Concepts22.5%- 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 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. Explain the role of data cleaning and preparation
  • 3. Describe the steps of the data analysis process
Topic 4: Communicating Insights and Reporting12.5%- Data Visualization
  • 1. Recognize common visualization types (bar, line, pie charts)
  • 2. Select appropriate visuals for different data types
  • 3. Interpret simple data visualizations
- Data Storytelling and Reporting
  • 1. Present insights with visual and verbal techniques
  • 2. Structure insights as a narrative
  • 3. Create clear and concise analytical reports

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

Question 1

A teacher wants to compare the number of students in different school clubs:

Which type of chart would be most appropriate to show this comparison? Select the best answer.

A. Bar chart
B. Bubble chart
C. Line chart
D. Pie chart


Question 2

How do the analysis and visualization stages of the data lifecycle typically work together?

A. Analysis summarizes archived data, and visualization transforms it into structured tables for storage.
B. Analysis identifies patterns and insights in processed data, which are then communicated through visualizations for informed decision-making.
C. Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
D. Analysis retrieves raw inputs from dashboards, while visualization uses that input to clean the data.


Question 3

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?

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


Question 4

You are analyzing survey results from students about their favorite colors. The list colorsstores individual responses:

You want to:
- find the number of unique colors mentioned using NumPy, and
- determine how often each color was chosen using Counter.
Which code snippet correctly performs both tasks? Select the best answer.
from numpy import unique

A. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)
B. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
C. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
D. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np


Question 5

A list is reversed using slicing: lst[::-1]. The programmer wants to confirm whether this modifies the original list or creates a new one. What is the correct behavior?

A. Creates new list
B. Modifies original
C. Returns None
D. Error


Solutions:

Question 1
Answer: A
Question 2
Answer: B
Question 3
Answer: B
Question 4
Answer: D
Question 5
Answer: A

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