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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%	- Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Filtering, sorting, transforming data 3. Formatting and standardizing values - 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 aggregation and grouping 1. Summarizing and grouping datasets
Topic 2: Data Visualization and Communication	20%	- Creating basic visualizations 1. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools - Principles of effective data visualization 1. Clarity, simplicity, and accuracy 2. Choosing appropriate chart types - Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights
Topic 3: Introduction to Data and Data Analysis Concepts	22.5%	- Data lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing - 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 - Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics - Data types and measurement scales 1. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative data
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. Conditional statements, loops, iteration 2. Basic exception handling 3. Defining and calling functions, parameters, return values - 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 (Q51-Q56):

NEW QUESTION # 51
A Python script includes the expression not False and True. The developer is unsure how logical operators are evaluated in terms of precedence. What will be the final Boolean result of this expression?

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

Answer: A

Explanation:
The not operator has higher precedence than and, so not False becomes True first. Then True and True is evaluated, resulting in True as the final Boolean value.

NEW QUESTION # 52
Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

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

Answer: C,D,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 # 53
Consider a Python program that assigns a string value "5" to a variable and then attempts to multiply it by an integer 3. The programmer expects numeric multiplication. What will actually happen when the code is executed?

A. Error
B. 0
C. 1
D. "555"

Answer: D

Explanation:
In Python, multiplying a string by an integer repeats the string. Therefore, "5" * 3 produces "555", not a numeric result. This behavior differs from arithmetic multiplication and often confuses beginners.

NEW QUESTION # 54
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. CSV, because it stores tabular data in plain text and is compatible with spreadsheet applications.
C. XML, because it automatically preserves spreadsheet formatting and charts.
D. JSON, because it stores hierarchical data and is designed for visual presentation in Excel.

Answer: B

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 # 55
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. "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."
B. "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
C. The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
D. "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."

Answer: A

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 # 56
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