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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Basics for Data Analysis	32.5%	- File Handling 1. Use the csv module for basic CSV operations 2. Read from and write to files (text, CSV) - Data Structures 1. Apply common operations and methods to data structures 2. Work with lists, tuples, dictionaries, and sets - Python Fundamentals 1. Define and use functions 2. Implement control flow structures (loops, conditionals) 3. Use variables, data types, and basic operators
Topic 2: Working with Data and Performing Simple Analyses	32.5%	- Data Analysis with Python Libraries 1. Work with the datetime module for date/time data 2. Utilize the collections module for specialized containers 3. Use the math and statistics modules for basic calculations 4. Perform basic operations with NumPy arrays - Simple Analytical Techniques 1. Identify basic patterns and trends in data 2. Calculate descriptive statistics (mean, median, mode) - Data Cleaning and Transformation 1. Handle missing and inconsistent data 2. Perform basic data transformation (filtering, sorting, grouping)
Topic 3: Communicating Insights and Reporting	12.5%	- Data Visualization 1. Select appropriate visuals for different data types 2. Recognize common visualization types (bar, line, pie charts) 3. Interpret simple data visualizations - Data Storytelling and Reporting 1. Structure insights as a narrative 2. Present insights with visual and verbal techniques 3. Create clear and concise analytical reports
Topic 4: Introduction to Data and Data Analysis Concepts	22.5%	- Data Ethics and Privacy 1. Recognize ethical considerations in data handling 2. Understand basic data privacy concepts - Define and Classify Data 1. Explain how data becomes meaningful 2. Differentiate structured, semi-structured, and unstructured data 3. Classify data as quantitative or qualitative - Data Analysis Process and Workflow 1. Describe the steps of the data analysis process 2. Explain the role of data cleaning and preparation 3. Identify common data sources and collection methods

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q21-Q26):

NEW QUESTION # 21
What will be the output of the following code?

A. 5 5
B. 10 5
C. 10 10
D. 5 10

Answer: A

Explanation:
The function declares the variable as global and assigns it the value 5. When the function is called, it updates the global variable and returns 5, so both the returned value and the global variable printed afterward are 5.

NEW QUESTION # 22
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. Developing machine learning algorithms to predict user behavior.
B. Designing a data pipeline to ingest and process real-time sensor data.
C. Recommending pricing strategies using predictive modeling and A/B testing.
D. Creating summary tables and charts to describe patterns in historical data.

Answer: D

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 # 23
Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?

Answer: C

Explanation:
Adding an integer and a float produces a float result, so checking that the type of the sum is float evaluates to True. The variable holding 3.0 is a float, so the isinstance check for float also evaluates to True.

NEW QUESTION # 24
An e-commerce site records every product view and purchase.
Which of the following best describes how these logs become marketing insights?

A. Raw event logs are stored, archived, and indexed - like by user ID and session - which then facilitate audit trails.
B. Raw event logs are structured, analyzed, and interpreted into information - like conversion rates by product - which then builds knowledge of customer preferences.
C. Raw event logs are validated, organized, and compiled into datasets - like timestamped user actions - which then support system debugging.
D. Raw event logs are filtered, aggregated, and formatted into summaries - like total visits per category - which then provide traffic reports.

Answer: B

Explanation:
Marketing insights are produced when raw event logs are structured, analyzed, and interpreted to generate meaningful metrics such as conversion rates. These interpreted results transform data into actionable knowledge about customer behavior and preferences.

NEW QUESTION # 25
A Python script checks membership using "a" in "apple". The developer wants to understand how membership works for strings. What result will this expression produce?

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

Answer: B

Explanation:
The in operator checks for substring presence. Since the character "a" exists in "apple", the expression evaluates to True, demonstrating how membership works for strings in Python.

NEW QUESTION # 26
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