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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Introduction to Data and Data Analysis Concepts	22.5%	- Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - 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 - 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
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. 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
Working with Data and Performing Simple Analysis	25%	- Data aggregation and grouping 1. Summarizing and grouping datasets - Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Formatting and standardizing values 3. Filtering, sorting, transforming data - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - Exploratory data analysis 1. Identifying patterns, trends, and outliers 2. Calculating mean, median, mode, range, variance, standard deviation
Data Visualization and Communication	20%	- Interpreting and presenting results 1. Deriving conclusions and insights 2. Reporting findings clearly and concisely - Creating basic visualizations 1. Using text and simple plotting tools 2. Line charts, bar charts, histograms, pie charts - 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 (Q43-Q48):

NEW QUESTION # 43
Which of the following statements about Python functions are correct? (Choose two.)

A. passexecutes the function body and returns its result.
B. Default parameters must always come before required ones in the parameter list.
C. If a function does not contain a returnstatement, it returns Noneby default.
D. Positional arguments must be passed in the order defined, while keyword arguments can be passed in any order using their parameter names.
E. A function cannot call itself or be passed as an argument to itself.
F. Functions can only return numeric values or strings.

Answer: C,D

Explanation:
Positional arguments are matched to parameters by their position, so they must follow the defined order, while keyword arguments are matched by name and can be provided in any order. If a function completes without an explicit return statement, Python returns None automatically.

NEW QUESTION # 44
A list is reversed using slicing: lst[::-1]. The programmer wants to confirm whether this modifies the original list or creates a new one. What is the correct behavior?

A. Returns None
B. Error
C. Modifies original
D. Creates new list

Answer: D

Explanation:
Slicing creates a new list object rather than modifying the original. The expression lst[::-1] returns a reversed copy, leaving the original list unchanged.

NEW QUESTION # 45
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.strip().split(', ')
B. fields = line.split().strip(', ')
C. fields = line.replace(', ', '|').split('|')
D. fields = line.split(', ').trim()

Answer: A

NEW QUESTION # 46
A variable is assigned using chained assignment: a = b = c = 10. The developer then changes b =
5. What will be the value of a after this operation?

A. 0
B. Error
C. 1
D. None

Answer: C

Explanation:
Integers are immutable in Python. Reassigning b to a new value does not affect a or c, which still reference the original integer value 10.

NEW QUESTION # 47
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 = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
C. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)
D. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np

Answer: B

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 # 48
......

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