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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q44-Q49):

NEW QUESTION # 44
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. Commuting is the second most time-consuming activity.
C. More time is spent commuting than on leisure.
D. School and homework together take up half of the day's time.
E. Leisure and meals combined equal the time spent at school.
F. Sleep and school take up the majority of time in a day.

Answer: A,F

NEW QUESTION # 45
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 = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
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 = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
D. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)

Answer: C

Explanation:
numpy.unique returns the distinct values in the list, and taking its length gives the number of unique colors. Counter(colors) correctly counts how many times each color appears in the responses.

NEW QUESTION # 46
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
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))
C. To install NumPy:
pip install numpy as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.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 # 47
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. Commuting is the second most time-consuming activity.
C. More time is spent commuting than on leisure.
D. School and homework together take up half of the day's time.
E. Leisure and meals combined equal the time spent at school.
F. Sleep and school take up the majority of time in a day.

Answer: A,F

Explanation:
The largest portions of the chart are sleep and school, and together they clearly make up more than half of the day. Leisure appears as a mid-sized segment that is larger than commuting but smaller than school, fitting the description of a moderate portion.

NEW QUESTION # 48
A programmer writes a script to test operator precedence using the expression 3 + 4 * 2 ** 2. The goal is to evaluate the result correctly based on Python's operator hierarchy rules. What result should the script produce?

A. 0
B. 1
C. 2
D. 3

Answer: A

Explanation:
Exponentiation is evaluated first, so 2 ** 2 equals 4. Then multiplication 4 * 4 equals 16. Finally, addition is performed: 3 + 16 results in 19.

NEW QUESTION # 49
......

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