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Updated: Jul 29, 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
Available Languages:English, Spanish
Certificate Validity Period:Lifetime
Related Certifications:PCEP™ – Certified Entry-Level Python Programmer
PCAP™ – Certified Associate in Python Programming
Exam Duration:60 minutes
Exam Price:USD 69
Passing Score:70%
Exam Format:Single-select questions, Multiple-select questions
Real Exam Qty:40
Recommended Training:Python for Data Analytics 101 (PD101)
PCED Official Study Materials
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: Data Visualization and Communication20%- Interpreting and presenting results
  • 1. Deriving conclusions and insights
  • 2. Reporting findings clearly and concisely
- Principles of effective data visualization
  • 1. Clarity, simplicity, and accuracy
  • 2. Choosing appropriate chart types
- Creating basic visualizations
  • 1. Using text and simple plotting tools
  • 2. Line charts, bar charts, histograms, pie charts
Topic 2: Introduction to Data and Data Analysis Concepts22.5%- 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. Role of data in decision-making and business
  • 3. Difference between data, information, and knowledge
- Data lifecycle and ethical considerations
  • 1. Data collection, storage, processing, usage, and sharing
  • 2. Privacy, security, bias, and fairness in data
- Basic statistical concepts
  • 1. Descriptive vs inferential statistics
  • 2. Population, sample, variable, observation
Topic 3: Python Basics for Data Analysis32.5%- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Core Python syntax and data types
  • 1. Lists, tuples, sets, dictionaries
  • 2. Variables, numbers, strings, booleans
- Control flow and functions
  • 1. Defining and calling functions, parameters, return values
  • 2. Basic exception handling
  • 3. Conditional statements, loops, iteration
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
Topic 4: Working with Data and Performing Simple Analysis25%- Data acquisition and loading
  • 1. Importing data from external sources
  • 2. Reading text, CSV, and structured files
- 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

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

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

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


2. 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.

A) cleaned_city = city.strip().capitalize()
B) cleaned_city = city.upper().replace(" ", "")
C) cleaned_city = city.strip().title()
D) cleaned_city = city.upper().strip()


3. 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?

A) 4 and 3
B) 3.5 and 3
C) 3 and 3.5
D) 3 and 4


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


5. The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)

A) Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
B) Leisure and meals combined equal the time spent at school.
C) Sleep and school take up the majority of time in a day.
D) Commuting is the second most time-consuming activity.
E) School and homework together take up half of the day's time.
F) More time is spent commuting than on leisure.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: C
Question # 3
Answer: B
Question # 4
Answer: C
Question # 5
Answer: A,C

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