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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: 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. Understand basic data privacy concepts 2. Recognize ethical considerations in data handling - Define and Classify Data 1. Classify data as quantitative or qualitative 2. Differentiate structured, semi-structured, and unstructured data 3. Explain how data becomes meaningful
Topic 3: 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. Work with lists, tuples, dictionaries, and sets 2. Apply common operations and methods to data structures - Python Fundamentals 1. Use variables, data types, and basic operators 2. Define and use functions 3. Implement control flow structures (loops, conditionals)
Topic 4: Working with Data and Performing Simple Analyses	32.5%	- Simple Analytical Techniques 1. Identify basic patterns and trends in data 2. Calculate descriptive statistics (mean, median, mode) - Data Cleaning and Transformation 1. Perform basic data transformation (filtering, sorting, grouping) 2. Handle missing and inconsistent data - Data Analysis with Python Libraries 1. Utilize the collections module for specialized containers 2. Work with the datetime module for date/time data 3. Use the math and statistics modules for basic calculations 4. Perform basic operations with NumPy arrays

Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions:

1. 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 = len(set(colors))
color_counts = np.unique(colors)
D) from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np

2. The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)

A) School and homework together take up half of the day's time.
B) Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
C) Sleep and school take up the majority of time in a day.
D) Commuting is the second most time-consuming activity.
E) Leisure and meals combined equal the time spent at school.
F) More time is spent commuting than on leisure.

3. A list is reversed using slicing: lst[::-1]. The programmer wants to confirm whether this modifies the original list or creates a new one. What is the correct behavior?

A) Modifies original
B) Returns None
C) Error
D) Creates new list

4. What will be the output of the following code?

A) 5 10
B) 5 5
C) 10 10
D) 10 5

5. You have a list of test scores, where each entry includes a student name and a score. Some students appear more than once. You want to compute the average score for each student and store the results in a dictionary. Here's the partial code block:

Which code correctly replaces the # MISSING CODE comment to calculate the average score for each student? Select the best answer.