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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: 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 cleaning and preparation 1. Filtering, sorting, transforming data 2. Handling missing values, duplicates, and errors 3. Formatting and standardizing values - Data aggregation and grouping 1. Summarizing and grouping datasets - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources
Topic 3: Python Basics for Data Analysis	32.5%	- Built-in modules for data work 1. math, statistics, datetime, collections, csv - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - Control flow and functions 1. Basic exception handling 2. Conditional statements, loops, iteration 3. Defining and calling functions, parameters, return values - Core Python syntax and data types 1. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries
Topic 4: Introduction to Data and Data Analysis Concepts	22.5%	- Basic statistical concepts 1. Descriptive vs inferential statistics 2. Population, sample, variable, observation - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - 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. Process of turning raw data into insights 2. Role of data in decision-making and business 3. Difference between data, information, and knowledge

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PCED - Certified Entry-Level Data Analyst with Python exam pdf guide & PCED-30-02 prep sure exam

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q40-Q45):

NEW QUESTION # 40
A government agency collects citizen data for various public services.
What is the primary reason for emphasizing careful data management throughout the entire data lifecycle in this context? Select the best answer.

A. To uphold data quality for effective service delivery, safeguard citizen privacy and prevent misuse, and comply with data protection laws.
B. To enable seamless sharing of anonymized citizen information with other governmental bodies for collaborative initiatives.
C. To streamline the initial capture of citizen details and minimize the administrative burden on both staff and the public.
D. To guarantee the long-term availability and integrity of crucial citizen datasets for future policy formulation and evaluation.

Answer: A

Explanation:
Careful data management across the entire lifecycle ensures high data quality for reliable public services, protects sensitive citizen information from misuse, and maintains compliance with legal and regulatory data protection requirements.

NEW QUESTION # 41
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. Predictive analytics - it uses weather and illness trends to forecast future cancellations.
B. Diagnostic analytics - it identifies reasons behind patterns in patient behavior.
C. Descriptive analytics - it reports appointment numbers across past seasons.
D. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.

Answer: B

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 # 42
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.split(', ').trim()
D. fields = line.replace(', ', '|').split('|')

Answer: B

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

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

Answer: D,F

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
You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

A. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
B. timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
C. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
D. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)

Answer: D

Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.

NEW QUESTION # 45
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