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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Introduction to Data and Data Analysis Concepts	22.5%	- Definition and classification of data 1. Difference between data, information, and knowledge 2. Process of turning raw data into insights 3. Role of data in decision-making and business - Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - Data lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing
Topic 2: 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
Topic 3: Working with Data and Performing Simple Analysis	25%	- Data cleaning and preparation 1. Filtering, sorting, transforming data 2. Formatting and standardizing values 3. Handling missing values, duplicates, and errors - Data aggregation and grouping 1. Summarizing and grouping datasets - Exploratory data analysis 1. Identifying patterns, trends, and outliers 2. Calculating mean, median, mode, range, variance, standard deviation - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources
Topic 4: Python Basics for Data Analysis	32.5%	- Core Python syntax and data types 1. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Control flow and functions 1. Basic exception handling 2. Defining and calling functions, parameters, return values 3. Conditional statements, loops, iteration - Built-in modules for data work 1. math, statistics, datetime, collections, csv - 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 (Q14-Q19):

NEW QUESTION # 14
You are given a short Python script that uses both arithmetic and assignment operators. The variable x is initialized as 4, then updated using x *= 2 + 3. What will be printed when the final value of x is displayed?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
Python evaluates the addition first because of operator precedence. The expression 2 + 3 equals
5, then x *= 5 multiplies the current value of x (4) by 5, resulting in a final value of 20.

NEW QUESTION # 15
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.isalpha()returns True, and text.find ("2024")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[6:8].isdigit()returns True, and text.endswith("Final")returns True
F. text[-6].isdigit() returns True, text.startswith("r") returns True

Answer: C,D,E

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 # 16
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, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
B. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
C. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
D. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 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 # 17
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

A. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
B. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.
C. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.
D. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.

Answer: A

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.

NEW QUESTION # 18
You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

A. The graph doesn't explain what each stress level means or show exact values for each month, which limits interpretation.
B. The months should be replaced with week numbers to provide more detailed insight.
C. The chart should be in 3D format to show depth and highlight differences more clearly.
D. The lines are too light and similar in color, making it difficult to distinguish between trends.
E. The font used in the chart is hard to read; using larger, uppercase text would improve clarity.

Answer: D

Explanation:
The lines are very light and visually similar, making it difficult to clearly distinguish between the different stress level trends, which directly reduces the effectiveness of the visualization in communicating patterns over time.

NEW QUESTION # 19
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