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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Python Basics for Data Analysis	32.5%	- Core Python syntax and data types 1. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - 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
Introduction to Data and Data Analysis Concepts	22.5%	- Basic statistical concepts 1. Descriptive vs inferential statistics 2. Population, sample, variable, observation - 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 - Data lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing - Data types and measurement scales 1. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative 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
Working with Data and Performing Simple Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends, and outliers 2. Calculating mean, median, mode, range, variance, standard deviation - Data cleaning and preparation 1. Filtering, sorting, transforming data 2. Handling missing values, duplicates, and errors 3. Formatting and standardizing values - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - Data aggregation and grouping 1. Summarizing and grouping datasets

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q22-Q27):

NEW QUESTION # 22
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

A. [1,2,3,4,5]
B. [1,2,3,[4,5]]
C. Error
D. [4,5,1,2,3]

Answer: B

Explanation:
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].

NEW QUESTION # 23
A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?

A. Loop restarts
B. Error
C. Skips one iteration
D. Loop ends immediately

Answer: D

Explanation:
The break statement immediately terminates the loop, regardless of remaining iterations. Control is transferred to the first statement following the loop block.

NEW QUESTION # 24
A healthcare provider analyzes patient records to determine that appointment cancellations spike during weather alerts and flu season.
What type of analytics is this?

A. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.
B. Descriptive analytics - it reports appointment numbers across past seasons.
C. Diagnostic analytics - it identifies reasons behind patterns in patient behavior.
D. Predictive analytics - it uses weather and illness trends to forecast future cancellations.

Answer: C

Explanation:
It examines existing data to understand why cancellations increase, identifying underlying causes such as weather alerts and flu season, which characterizes diagnostic analytics.

NEW QUESTION # 25
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().strip(', ')
B. fields = line.strip().split(', ')
C. fields = line.replace(', ', '|').split('|')
D. fields = line.split(', ').trim()

Answer: B

NEW QUESTION # 26
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 = len(set(colors))
color_counts = np.unique(colors)
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 = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
D. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np

Answer: D

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 # 27
......

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