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Updated: Sep 02, 2026

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Python Institute PCED-30-02 Exam Overview:

Certification Vendor:Python Institute (OpenEDG)
Exam Name:PCED™ – Certified Entry-Level Data Analyst with Python
Exam Number:PCED-30-02
Passing Score:70%
Related Certifications:PCEP™ – Certified Entry-Level Python Programmer
PCAP™ – Certified Associate in Python Programming
Real Exam Qty:40
Available Languages:Spanish, English
Exam Format:Single-select questions, Multiple-select questions
Exam Price:USD 69
Exam Duration:60 minutes
Certificate Validity Period:Lifetime
Recommended Training:PCED Official Study Materials
Python for Data Analytics 101 (PD101)
Exam Registration:Official Registration Page
Sample Questions:Python Institute PCED-30-02 Sample Questions
Exam Way:Online proctored exam via OpenEDG TestNow™ platform
Pre Condition:No formal prerequisites; basic familiarity with Python or programming recommended
Official Syllabus URL:https://pythoninstitute.org/pced-exam-syllabus

Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Introduction to Data and Data Analysis Concepts22.5%- Basic statistical concepts
  • 1. Population, sample, variable, observation
  • 2. Descriptive vs inferential statistics
- Data lifecycle and ethical considerations
  • 1. Privacy, security, bias, and fairness in data
  • 2. Data collection, storage, processing, usage, and sharing
- Data types and measurement scales
  • 1. Nominal, ordinal, interval, ratio scales
  • 2. Qualitative vs quantitative data
- Definition and classification of data
  • 1. Process of turning raw data into insights
  • 2. Difference between data, information, and knowledge
  • 3. Role of data in decision-making and business
Topic 2: Data Visualization and Communication20%- Creating basic visualizations
  • 1. Using text and simple plotting tools
  • 2. Line charts, bar charts, histograms, pie charts
- Interpreting and presenting results
  • 1. Deriving conclusions and insights
  • 2. Reporting findings clearly and concisely
- Principles of effective data visualization
  • 1. Choosing appropriate chart types
  • 2. Clarity, simplicity, and accuracy
Topic 3: Working with Data and Performing Simple Analysis25%- Data cleaning and preparation
  • 1. Filtering, sorting, transforming data
  • 2. Handling missing values, duplicates, and errors
  • 3. Formatting and standardizing values
- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Exploratory data analysis
  • 1. Calculating mean, median, mode, range, variance, standard deviation
  • 2. Identifying patterns, trends, and outliers
- Data acquisition and loading
  • 1. Importing data from external sources
  • 2. Reading text, CSV, and structured files
Topic 4: Python Basics for Data Analysis32.5%- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Control flow and functions
  • 1. Basic exception handling
  • 2. Conditional statements, loops, iteration
  • 3. Defining and calling functions, parameters, return values
- Core Python syntax and data types
  • 1. Lists, tuples, sets, dictionaries
  • 2. Variables, numbers, strings, booleans

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

Question 1

Consider a Python program that assigns a string value "5" to a variable and then attempts to multiply it by an integer 3. The programmer expects numeric multiplication. What will actually happen when the code is executed?

A. "555"
B. Error
C. 15
D. 8


Question 2

You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A. fields = line.replace(', ', '|').split('|')
B. fields = line.strip().split(', ')
C. fields = line.split().strip(', ')
D. fields = line.split(', ').trim()


Question 3

A healthcare provider analyzes patient records to determine that appointment cancellations spike during weather alerts and flu season.
What type of analytics is this?

A. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.
B. Descriptive analytics - it reports appointment numbers across past seasons.
C. Predictive analytics - it uses weather and illness trends to forecast future cancellations.
D. Diagnostic analytics - it identifies reasons behind patterns in patient behavior.


Question 4

What will be the output of the following code?

A. 10 5
B. 5 10
C. 5 5
D. 10 10


Question 5

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.

A. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
B. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.
C. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.
D. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.


Solutions:

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

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