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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Python Basics for Data Analysis32.5%- Data Structures
  • 1. Work with lists, tuples, dictionaries, and sets
  • 2. Apply common operations and methods to data structures
- File Handling
  • 1. Use the csv module for basic CSV operations
  • 2. Read from and write to files (text, CSV)
- Python Fundamentals
  • 1. Use variables, data types, and basic operators
  • 2. Define and use functions
  • 3. Implement control flow structures (loops, conditionals)
Introduction to Data and Data Analysis Concepts22.5%- 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
- Data Ethics and Privacy
  • 1. Recognize ethical considerations in data handling
  • 2. Understand basic data privacy concepts
- 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
Communicating Insights and Reporting12.5%- Data Visualization
  • 1. Interpret simple data visualizations
  • 2. Recognize common visualization types (bar, line, pie charts)
  • 3. Select appropriate visuals for different data types
- 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
Working with Data and Performing Simple Analyses32.5%- 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. Calculate descriptive statistics (mean, median, mode)
  • 2. Identify basic patterns and trends in data

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

1. You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

A) The lines are too light and similar in color, making it difficult to distinguish between trends.
B) The chart should be in 3D format to show depth and highlight differences more clearly.
C) The graph doesn't explain what each stress level means or show exact values for each month, which limits interpretation.
D) The font used in the chart is hard to read; using larger, uppercase text would improve clarity.
E) The months should be replaced with week numbers to provide more detailed insight.


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

Which statements are most accurate? (Choose two.)

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


3. You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

A) timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
B) timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
C) timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
D) timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np


4. A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)

A) Automatically opting users into data sharing unless they manually change settings.
B) Implementing encryption for both stored and transmitted customer data.
C) Providing users with a clear explanation of how their data is used and obtaining explicit consent.
D) Limiting data access only to marketing staff to streamline personalization.
E) Retaining user data permanently unless users explicitly request deletion.


5. A Python script evaluates the expression len(" "). The programmer assumes it might be zero because it looks empty. What will the actual result be?

A) None
B) Error
C) 1
D) 0


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B,C
Question # 3
Answer: B
Question # 4
Answer: B,C
Question # 5
Answer: C

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