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| Section | Weight | Objectives |
|---|
| Working with Data and Performing Simple Analyses | 32.5% | - Data Analysis with Python Libraries
- 1. Utilize the collections module for specialized containers
- 2. Use the math and statistics modules for basic calculations
- 3. Perform basic operations with NumPy arrays
- 4. Work with the datetime module for date/time data
- Simple Analytical Techniques
- 1. Calculate descriptive statistics (mean, median, mode)
- 2. Identify basic patterns and trends in data
- Data Cleaning and Transformation
- 1. Handle missing and inconsistent data
- 2. Perform basic data transformation (filtering, sorting, grouping)
|
| Python Basics for Data Analysis | 32.5% | - Data Structures
- 1. Apply common operations and methods to data structures
- 2. Work with lists, tuples, dictionaries, and sets
- Python Fundamentals
- 1. Use variables, data types, and basic operators
- 2. Implement control flow structures (loops, conditionals)
- 3. Define and use functions
- File Handling
- 1. Use the csv module for basic CSV operations
- 2. Read from and write to files (text, CSV)
|
| Communicating Insights and Reporting | 12.5% | - Data Storytelling and Reporting
- 1. Create clear and concise analytical reports
- 2. Present insights with visual and verbal techniques
- 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
|
| 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. Classify data as quantitative or qualitative
- 2. Explain how data becomes meaningful
- 3. Differentiate structured, semi-structured, and unstructured data
- Data Analysis Process and Workflow
- 1. Identify common data sources and collection methods
- 2. Explain the role of data cleaning and preparation
- 3. Describe the steps of the data analysis process
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q49-Q54):
NEW QUESTION # 49
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.
- A. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.
- B. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
- C. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.
- D. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.
Answer: B
Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.
NEW QUESTION # 50
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. School and homework together take up half of the day's time.
- C. Sleep and school take up the majority of time in a day.
- D. Leisure and meals combined equal the time spent at school.
- E. More time is spent commuting than on leisure.
- F. Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
Answer: C,F
Explanation:
The largest portions of the chart are sleep and school, and together they clearly make up more than half of the day. Leisure appears as a mid-sized segment that is larger than commuting but smaller than school, fitting the description of a moderate portion.
NEW QUESTION # 51
A script defines a variable x = None and checks its truth value using a conditional statement. The developer wants to understand how Python evaluates None in Boolean contexts. What will bool(x) return?
- A. False
- B. True
- C. None
- D. Error
Answer: A
Explanation:
The value None represents the absence of a value and is treated as False in Boolean expressions. Therefore, calling bool(None) returns False, similar to empty containers or zero values.
NEW QUESTION # 52
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. School and homework together take up half of the day's time.
- C. Sleep and school take up the majority of time in a day.
- D. Leisure and meals combined equal the time spent at school.
- E. More time is spent commuting than on leisure.
- F. Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
Answer: C,F
NEW QUESTION # 53
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.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as 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.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
Answer: B
Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.
NEW QUESTION # 54
......
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