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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Basics for Data Analysis	32.5%	- Control flow and functions 1. Conditional statements, loops, iteration 2. Defining and calling functions, parameters, return values 3. Basic exception handling - Built-in modules for data work 1. math, statistics, datetime, collections, csv - 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
Topic 2: Introduction to Data and Data Analysis Concepts	22.5%	- 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 types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - Definition and classification of data 1. Process of turning raw data into insights 2. Role of data in decision-making and business 3. Difference between data, information, and knowledge
Topic 3: 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. Reading text, CSV, and structured files 2. Importing data from external sources - Data aggregation and grouping 1. Summarizing and grouping datasets - Data cleaning and preparation 1. Filtering, sorting, transforming data 2. Handling missing values, duplicates, and errors 3. Formatting and standardizing values
Topic 4: Data Visualization and Communication	20%	- 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 - 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 (Q18-Q23):

NEW QUESTION # 18
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. Error
B. [4,5,1,2,3]
C. [1,2,3,[4,5]]
D. [1,2,3,4,5]

Answer: C

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 # 19
A Python loop uses range(5, 0, -2) to iterate backward. The programmer expects a decreasing sequence. Which values will actually be generated when this loop runs?

A. 5, 3
B. 5, 3, 1
C. 4, 2, 0
D. 5, 4, 3, 2, 1

Answer: B

Explanation:
The range starts at 5 and decreases by 2 each step until it reaches a value greater than 0. The generated sequence is 5, 3, and 1.

NEW QUESTION # 20
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 = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
D. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np

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 # 21
A Python developer is testing truth values of different data types using the bool() function. The expression bool([0]) is evaluated in the script. What result should be expected when this expression is printed?

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

Answer: B

Explanation:
In Python, any non-empty list evaluates to True in a Boolean context. Even though the element inside the list is zero, the list itself is not empty, so the result is True.

NEW QUESTION # 22
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. Descriptive analytics - it reports appointment numbers across past seasons.
B. Predictive analytics - it uses weather and illness trends to forecast future cancellations.
C. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.
D. Diagnostic analytics - it identifies reasons behind patterns in patient behavior.

Answer: D

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 # 23
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