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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Communication	20%	- Principles of effective data visualization 1. Clarity, simplicity, and accuracy 2. Choosing appropriate chart types - 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
Topic 2: Working with Data and Performing Simple Analysis	25%	- 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 - Exploratory data analysis 1. Identifying patterns, trends, and outliers 2. Calculating mean, median, mode, range, variance, standard deviation - Data cleaning and preparation 1. Formatting and standardizing values 2. Handling missing values, duplicates, and errors 3. Filtering, sorting, transforming data
Topic 3: 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
Topic 4: 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 lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing - 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 (Q23-Q28):

NEW QUESTION # 23
A health researcher uses wearable devices to record physical activity and sends a survey to randomly selected participants across age groups.
Why can this approach be effective? Select the best answer.

A. It relies on observational methods, which are more accurate than surveys or sensors.
B. It uses unstructured interviews to validate automated tracking, improving bias detection.
C. It combines automated data collection with representative sampling, reducing the risk of bias.
D. It limits sampling to one demographic, ensuring high-quality data within a specific group.

Answer: C

Explanation:
Combining automated wearable data with a randomly selected survey sample integrates objective measurements with representative participant input, which helps reduce bias and improves the reliability and generalizability of the findings.

NEW QUESTION # 24
You are reviewing a report that compares the number of weekly client meetings and project completion rates across teams. The table below summarizes the data for each team:

Which statement best describes the data shown above? Select the best answer.

A. The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
B. "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."
C. "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."
D. "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."

Answer: B

Explanation:
Teams with moderate meeting counts around 5-6 per week (such as Alpha, Gamma, and Theta) completed among the highest numbers of projects, while teams with very high or very low meeting counts tended to complete fewer projects, indicating that moderate meeting frequency is associated with higher project completion.

NEW QUESTION # 25
A Python developer is testing truth values of different data types using the bool() function. The expression bool([0]) is evaluated in the script. What result should be expected when this expression is printed?

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

Answer: C

Explanation:
In Python, any non-empty list evaluates to True in a Boolean context. Even though the element inside the list is zero, the list itself is not empty, so the result is True.

NEW QUESTION # 26
You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

Answer: A

Explanation:
It first handles the non-numeric cases ("MISSING" and None) in a single condition, preventing invalid comparisons, and then cleanly checks the numeric outlier bounds using an or condition (<
2.5 or > 4.2) before defaulting to "Normal".

NEW QUESTION # 27
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

Answer: B

Explanation:
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.

NEW QUESTION # 28
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