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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
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 aggregation and grouping 1. Summarizing and grouping datasets - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - Data cleaning and preparation 1. Formatting and standardizing values 2. Handling missing values, duplicates, and errors 3. Filtering, sorting, transforming data
Data Visualization and Communication	20%	- Principles of effective data visualization 1. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy - Creating basic visualizations 1. Using text and simple plotting tools 2. Line charts, bar charts, histograms, pie charts - Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights
Python Basics for Data Analysis	32.5%	- 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. 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
Introduction to Data and Data Analysis Concepts	22.5%	- Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics - Definition and classification of data 1. Role of data in decision-making and business 2. Process of turning raw data into insights 3. Difference between data, information, and knowledge - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - Data lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q35-Q40):

NEW QUESTION # 35
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. Sleep and school take up the majority of time in a day.
C. School and homework together take up half of the day's time.
D. Commuting is the second most time-consuming activity.
E. Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
F. More time is spent commuting than on leisure.

Answer: B,E

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 # 36
You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

Answer: A

Explanation:
It first handles the non-numeric cases ("MISSING" and None) in a single condition, preventing invalid comparisons, and then cleanly checks the numeric outlier bounds using an or condition (<
2.5 or > 4.2) before defaulting to "Normal".

NEW QUESTION # 37
Consider the following Python code:

What will be printed when the code above is executed?
40

A. [10, 20, 30, 40]
1
[1, 2, 4, 5, 6]
50
B. [20, 30, 40]
1
[1, 2, 4, 5, 6]
C. [10, 20, 30, 40]
0
[1, 2, 3, 4, 5, 6]
50
D. [20, 30, 40]
1
[1, 2, 4, 5, 6]
50

Answer: B

Explanation:
The last element of the original list is 50. The slice from index 1 up to (but not including) index 4 is
[20, 30, 40]. After removing 30 and appending 60, the list contains one occurrence of 10, and integer division by 10 produces [1, 2, 4, 5, 6].

NEW QUESTION # 38
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.upper().replace(" ", "")
B. cleaned_city = city.upper().strip()
C. cleaned_city = city.strip().capitalize()
D. cleaned_city = city.strip().title()

Answer: D

Explanation:
Stripping removes leading and trailing whitespace, and applying title formatting capitalizes the first letter of each word while converting the remaining letters to lowercase, producing the correctly standardized city name.

NEW QUESTION # 39
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 = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
B. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(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 # 40
......

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