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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 acquisition and loading 1. Importing data from external sources 2. Reading text, CSV, and structured files - Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Filtering, sorting, transforming data 3. Formatting and standardizing values
Topic 2: Data Visualization and Communication	20%	- Principles of effective data visualization 1. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy - 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
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. Conditional statements, loops, iteration 2. Basic exception handling 3. Defining and calling functions, parameters, return values - Core Python syntax and data types 1. Lists, tuples, sets, dictionaries 2. Variables, numbers, strings, booleans
Topic 4: Introduction to Data and Data Analysis Concepts	22.5%	- Data lifecycle and ethical considerations 1. Data collection, storage, processing, usage, and sharing 2. Privacy, security, bias, and fairness in 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. Role of data in decision-making and business 3. Process of turning raw data into insights - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q11-Q16):

NEW QUESTION # 11
A dictionary is defined as d = {"a":1, "b":2}. The programmer accesses d["c"]. What happens when this line executes?

A. Adds key automatically
B. Returns 0
C. Returns None
D. Raises KeyError

Answer: D

Explanation:
Accessing a non-existent key using square brackets raises a KeyError. Python does not automatically create missing keys, so the program will terminate unless the exception is handled.

NEW QUESTION # 12
Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

A. text.capitalize()changes Report2024Finalto REPORT2024FINAL, and text.find("2024")returns 6
B. text[6:8].isdigit()returns True, and text.endswith("Final")returns True
C. text[0:6].isalpha() returns True, and text.find("2024") returns 6
D. text.capitalize()changes Finalto lowercase, and text.find ("L")returns True
E. text.isalpha()returns True, and text.find ("2024")returns True
F. text[-6].isdigit() returns True, text.startswith("r") returns True

Answer: B,C,D

Explanation:
The slice text[6:8] is "20", which contains only digits, and the string ends with "Final". The first six characters are "Report", which are all alphabetic, and "2024" begins at index 6.

NEW QUESTION # 13
A student writes a program using input() to collect user data, then tries to add 5 to the input value without conversion. The program crashes. What is the most likely cause of this behavior?

A. Syntax error
B. input returns int
C. input returns string
D. input returns float

Answer: C

Explanation:
The input() function always returns a string. Attempting to add an integer to a string causes a TypeError. The input must be explicitly converted using int() or float() before performing arithmetic operations.

NEW QUESTION # 14
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.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
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.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np

Answer: A

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 # 15
A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)

A. Implementing encryption for both stored and transmitted customer data.
B. Limiting data access only to marketing staff to streamline personalization.
C. Providing users with a clear explanation of how their data is used and obtaining explicit consent.
D. Retaining user data permanently unless users explicitly request deletion.
E. Automatically opting users into data sharing unless they manually change settings.

Answer: A,C

Explanation:
Encrypting stored and transmitted data protects sensitive information from unauthorized access and supports regulatory requirements for data security. Providing clear explanations of data usage and obtaining explicit consent ensures transparency and aligns with core principles of data protection regulations such as lawful processing and user rights.

NEW QUESTION # 16
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