PCED-30-02 Lernressourcen, PCED-30-02 Originale Fragen

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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Visualization and Communication	20%	- Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights - Creating basic visualizations 1. Using text and simple plotting tools 2. Line charts, bar charts, histograms, pie charts - Principles of effective data visualization 1. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy
Python Basics for Data Analysis	32.5%	- Built-in modules for data work 1. math, statistics, datetime, collections, csv - 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. Defining and calling functions, parameters, return values 3. Basic exception handling
Working with Data and Performing Simple Analysis	25%	- Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Filtering, sorting, transforming data 3. Formatting and standardizing values - 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
Introduction to Data and Data Analysis Concepts	22.5%	- Data types and measurement scales 1. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative 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 - Data lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing - Basic statistical concepts 1. Descriptive vs inferential statistics 2. Population, sample, variable, observation

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Python Institute PCED-30-02 Originale Fragen, PCED-30-02 Lernhilfe

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Python Institute PCED - Certified Entry-Level Data Analyst with Python PCED-30-02 Prüfungsfragen mit Lösungen (Q39-Q44):

39. Frage
A Python script checks membership using "a" in "apple". The developer wants to understand how membership works for strings. What result will this expression produce?

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

Antwort: A

Begründung:
The in operator checks for substring presence. Since the character "a" exists in "apple", the expression evaluates to True, demonstrating how membership works for strings in Python.

40. Frage
A loop is designed using range(1, 6, 2) to iterate through numbers. The developer wants to know exactly which values will be generated during execution. Which sequence correctly represents the values produced?

A. 1, 3
B. 1, 3, 5
C. 1, 2, 3, 4, 5
D. 2, 4, 6

Antwort: B

Begründung:
The range(start, stop, step) function generates numbers starting from 1, increasing by 2, and stopping before 6. Therefore, the resulting sequence is 1, 3, and 5.

41. Frage
You are given the following list of daily step counts:
steps = [8230, 9020, 7640, 8760, 10020, 2546, 9817]
Your task is to calculate:
- the standard deviation of the step counts,
- the average rounded up to the nearest whole number, and
- the median of the step counts.
Which code snippet correctly performs all three tasks? Select the best answer.
import statistics

A. import math
print(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics
B. import math
print(statistics.stdev(steps))
print(math.ceil(statistics.mean(steps)))
print(statistics.median(steps))
C. import math
print(statistics.variance(steps))
print(math.ceil(sum(steps) / len(steps)))
print(math.floor(statistics.median(steps)))
import statistics
D. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics

Antwort: B

Begründung:
It uses statistics.stdev() to compute the standard deviation, statistics.mean() to compute the average and math.ceil() to round it up to the nearest whole number, and statistics.median() to compute the median.

42. Frage
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 = np.unique(colors)
color_counts = Counter(set(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 = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np

Antwort: B

Begründung:
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.

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

Antwort: B

Begründung:
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]].

44. Frage
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