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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
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 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. Importing data from external sources 2. Reading text, CSV, and structured files
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 - 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. Population, sample, variable, observation 2. Descriptive vs inferential statistics - Definition and classification of data 1. Process of turning raw data into insights 2. Difference between data, information, and knowledge 3. Role of data in decision-making and business
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
Python Basics for Data Analysis	32.5%	- Control flow and functions 1. Defining and calling functions, parameters, return values 2. Basic exception handling 3. Conditional statements, loops, iteration - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Core Python syntax and data types 1. Lists, tuples, sets, dictionaries 2. Variables, numbers, strings, booleans - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing

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

NEW QUESTION # 40
A script attempts to divide two numbers using / and //. The values are 7 and 2. The developer wants to understand the difference between true division and floor division. What are the results respectively?

A. 3 and 4
B. 4 and 3
C. 3 and 3.5
D. 3.5 and 3

Answer: D

Explanation:
The / operator performs true division, returning a float result of 3.5. The // operator performs floor division, truncating the decimal part and returning the integer value 3.

NEW QUESTION # 41
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, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
B. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
C. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
D. timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np

Answer: C

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 # 42
A data analyst exports a cleaned dataset from Python and wants to share it with a colleague for easy use in Excel.
Which file format is best suited for this purpose? Select the best answer.

A. SQL, because it directly converts tabular data into database tables that Excel can open.
B. JSON, because it stores hierarchical data and is designed for visual presentation in Excel.
C. XML, because it automatically preserves spreadsheet formatting and charts.
D. CSV, because it stores tabular data in plain text and is compatible with spreadsheet applications.

Answer: D

Explanation:
CSV stores tabular data in a simple plain-text, comma-separated format that is widely supported by spreadsheet applications like Excel, making it ideal for sharing datasets for easy import and analysis.

NEW QUESTION # 43
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. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.
C. Descriptive analytics - it reports appointment numbers across past seasons.
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 # 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. Error
B. Modifies original
C. Creates new list
D. Returns None

Answer: C

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
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