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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q12-Q17):

NEW QUESTION # 12
A script defines a variable x = None and checks its truth value using a conditional statement. The developer wants to understand how Python evaluates None in Boolean contexts. What will bool(x) return?

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

Answer: B

Explanation:
The value None represents the absence of a value and is treated as False in Boolean expressions. Therefore, calling bool(None) returns False, similar to empty containers or zero values.

NEW QUESTION # 13
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?

A. ytho
B. ythn
C. yth
D. tho

Answer: C

Explanation:
Slicing includes the start index but excludes the end index. Characters at indices 1, 2, and 3 are selected, producing the substring "yth".

NEW QUESTION # 14
A health researcher uses wearable devices to record physical activity and sends a survey to randomly selected participants across age groups.
Why can this approach be effective? Select the best answer.

A. It relies on observational methods, which are more accurate than surveys or sensors.
B. It uses unstructured interviews to validate automated tracking, improving bias detection.
C. It limits sampling to one demographic, ensuring high-quality data within a specific group.
D. It combines automated data collection with representative sampling, reducing the risk of bias.

Answer: D

Explanation:
Combining automated wearable data with a randomly selected survey sample integrates objective measurements with representative participant input, which helps reduce bias and improves the reliability and generalizability of the findings.

NEW QUESTION # 15
The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)

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

Answer: D,E

NEW QUESTION # 16
You have collected sales data and want to compute the average using the NumPy library.
Which option correctly shows how to install NumPy and use it in your code to perform this task?

A. To install NumPy:
from pip install numpy
Then run this code:
import numpy as np
data = np.array([10, 20, 30])
print(np.mean(data))
B. To install NumPy:
pip install numpy as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.mean(data))
C. To install NumPy:
pip install numpy
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))

Answer: A

Explanation:
NumPy is installed with pip install numpy, then imported with import numpy as np. After that, you can create an array and compute the mean using np.array(...) and np.mean(...).

NEW QUESTION # 17
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