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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q43-Q48):

NEW QUESTION # 43
A programmer writes a script to test operator precedence using the expression 3 + 4 * 2 ** 2. The goal is to evaluate the result correctly based on Python's operator hierarchy rules. What result should the script produce?

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

Answer: A

Explanation:
Exponentiation is evaluated first, so 2 ** 2 equals 4. Then multiplication 4 * 4 equals 16. Finally, addition is performed: 3 + 16 results in 19.

NEW QUESTION # 44
You are given two lists representing daily page views and sign-ups on a website:
views = [120, 130, 128, 700, 115, 123, 119, 680, 122]
signups = [12, 15, 13, 50, 11, 14, 10, 55, 13]
You want to:
- remove outliers (views > 600), and
- calculate correlation between the cleaned lists.
Which code accomplishes this correctly? Select the best answer.

Answer: A

Explanation:
It converts the lists to NumPy arrays, builds a boolean mask based on the views threshold to remove outliers, applies the same mask to both arrays so they stay aligned, and then computes the correlation on the cleaned pairs using np.corrcoef.

NEW QUESTION # 45
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. The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
C. "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
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 # 46
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

A. [1,2,3,4,5]
B. [4,5,1,2,3]
C. [1,2,3,[4,5]]
D. Error

Answer: C

Explanation:
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].

NEW QUESTION # 47
You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A. fields = line.replace(', ', '|').split('|')
B. fields = line.split().strip(', ')
C. fields = line.strip().split(', ')
D. fields = line.split(', ').trim()

Answer: C

Explanation:
Stripping removes leading and trailing whitespace from the line, and then splitting on the comma- space delimiter correctly separates the values into a list for further formatting.

NEW QUESTION # 48
......

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