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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q28-Q33):

NEW QUESTION # 28
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: C

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 # 29
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 limits sampling to one demographic, ensuring high-quality data within a specific group.
B. It combines automated data collection with representative sampling, reducing the risk of bias.
C. It uses unstructured interviews to validate automated tracking, improving bias detection.
D. It relies on observational methods, which are more accurate than surveys or sensors.

Answer: B

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 # 30
A student writes a program using input() to collect user data, then tries to add 5 to the input value without conversion. The program crashes. What is the most likely cause of this behavior?

A. input returns int
B. input returns float
C. input returns string
D. Syntax error

Answer: C

Explanation:
The input() function always returns a string. Attempting to add an integer to a string causes a TypeError. The input must be explicitly converted using int() or float() before performing arithmetic operations.

NEW QUESTION # 31
A dictionary is defined as d = {"a":1, "b":2}. The programmer accesses d["c"]. What happens when this line executes?

A. Returns 0
B. Returns None
C. Raises KeyError
D. Adds key automatically

Answer: C

Explanation:
Accessing a non-existent key using square brackets raises a KeyError. Python does not automatically create missing keys, so the program will terminate unless the exception is handled.

NEW QUESTION # 32
You are analyzing survey results from students about their favorite colors. The list colorsstores individual responses:

You want to:
- find the number of unique colors mentioned using NumPy, and
- determine how often each color was chosen using Counter.
Which code snippet correctly performs both tasks? Select the best answer.
from numpy import unique

A. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
B. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
C. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
D. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)

Answer: A

Explanation:
numpy.unique returns the distinct values in the list, and taking its length gives the number of unique colors. Counter(colors) correctly counts how many times each color appears in the responses.

NEW QUESTION # 33
......

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