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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. Defining and calling functions, parameters, return values 2. Conditional statements, loops, iteration 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%	- 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 - 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
Topic 3: Data Visualization and Communication	20%	- 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 - Creating basic visualizations 1. Using text and simple plotting tools 2. Line charts, bar charts, histograms, pie charts
Topic 4: 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 - Exploratory data analysis 1. Identifying patterns, trends, and outliers 2. Calculating mean, median, mode, range, variance, standard deviation - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q38-Q43):

NEW QUESTION # 38
Consider the following Python code:

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

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

Answer: D

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 # 39
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, 4, 3, 2, 1
B. 4, 2, 0
C. 5, 3
D. 5, 3, 1

Answer: D

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 # 40
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 ends immediately
B. Loop restarts
C. Error
D. Skips one iteration

Answer: A

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 # 41
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?

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

Answer: B

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 # 42
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 # 43
......

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