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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Basics for Data Analysis	32.5%	- Built-in modules for data work 1. math, statistics, datetime, collections, csv - 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. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing
Topic 2: Working with Data and Performing Simple Analysis	25%	- Data acquisition and loading 1. Importing data from external sources 2. Reading text, CSV, and structured files - 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. Formatting and standardizing values 3. Handling missing values, duplicates, and errors - Data aggregation and grouping 1. Summarizing and grouping datasets
Topic 3: 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 - Data types and measurement scales 1. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics - 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
Topic 4: Data Visualization and Communication	20%	- Principles of effective data visualization 1. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy - Creating basic visualizations 1. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools - Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q13-Q18):

NEW QUESTION # 13
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 limits sampling to one demographic, ensuring high-quality data within a specific group.
C. It uses unstructured interviews to validate automated tracking, improving bias detection.
D. It combines automated data collection with representative sampling, reducing the risk of bias.

Answer: D

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 # 14
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

A. Creating summary tables and charts to describe patterns in historical data.
B. Designing a data pipeline to ingest and process real-time sensor data.
C. Developing machine learning algorithms to predict user behavior.
D. Recommending pricing strategies using predictive modeling and A/B testing.

Answer: A

Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.

NEW QUESTION # 15
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: D

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 # 16
Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

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

Answer: A,E,F

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 # 17
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 # 18
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