Python Institute PCED-30-02 Exam : PCED - Certified Entry-Level Data Analyst with Python

Exam Code: PCED-30-02
Exam Name: PCED - Certified Entry-Level Data Analyst with Python
Updated: Sep 15, 2026
Q & A: 52 Questions and Answers

About Python Institute PCED-30-02 Exam Questions

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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Python Basics for Data Analysis	32.5%	- Built-in modules for data work 1. math, statistics, datetime, collections, csv - Core Python syntax and data types 1. Lists, tuples, sets, dictionaries 2. Variables, numbers, strings, booleans - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - Control flow and functions 1. Conditional statements, loops, iteration 2. Defining and calling functions, parameters, return values 3. Basic exception handling
Working with Data and Performing Simple Analysis	25%	- 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 - Data cleaning and preparation 1. Formatting and standardizing values 2. Filtering, sorting, transforming data 3. Handling missing values, duplicates, and errors - Data aggregation and grouping 1. Summarizing and grouping datasets
Introduction to Data and Data Analysis Concepts	22.5%	- Data lifecycle and ethical considerations 1. Data collection, storage, processing, usage, and sharing 2. Privacy, security, bias, and fairness in data - Definition and classification of data 1. Difference between data, information, and knowledge 2. Role of data in decision-making and business 3. Process of turning raw data into insights - Basic statistical concepts 1. Descriptive vs inferential statistics 2. Population, sample, variable, observation - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales
Data Visualization and Communication	20%	- Principles of effective data visualization 1. Clarity, simplicity, and accuracy 2. Choosing appropriate chart types - Interpreting and presenting results 1. Deriving conclusions and insights 2. Reporting findings clearly and concisely - Creating basic visualizations 1. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools

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

Question #1

An e-commerce site records every product view and purchase.
Which of the following best describes how these logs become marketing insights?

A. Raw event logs are validated, organized, and compiled into datasets - like timestamped user actions - which then support system debugging.
B. Raw event logs are stored, archived, and indexed - like by user ID and session - which then facilitate audit trails.
C. Raw event logs are filtered, aggregated, and formatted into summaries - like total visits per category - which then provide traffic reports.
D. Raw event logs are structured, analyzed, and interpreted into information - like conversion rates by product - which then builds knowledge of customer preferences.

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #2

You are given the following list of daily step counts:
steps = [8230, 9020, 7640, 8760, 10020, 2546, 9817]
Your task is to calculate:
- the standard deviation of the step counts,
- the average rounded up to the nearest whole number, and
- the median of the step counts.
Which code snippet correctly performs all three tasks? Select the best answer.
import statistics

A. import math
print(statistics.stdev(steps))
print(math.ceil(statistics.mean(steps)))
print(statistics.median(steps))
B. import math
print(statistics.variance(steps))
print(math.ceil(sum(steps) / len(steps)))
print(math.floor(statistics.median(steps)))
import statistics
C. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics
D. import math
print(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics