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| 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
- Data Analysis with Python Libraries
- 1. Perform basic operations with NumPy arrays
- 2. Utilize the collections module for specialized containers
- 3. Use the math and statistics modules for basic calculations
- 4. Work with the datetime module for date/time data
- Simple Analytical Techniques
- 1. Identify basic patterns and trends in data
- 2. Calculate descriptive statistics (mean, median, mode)
|
| Topic 2: Communicating Insights and Reporting | 12.5% | - Data Storytelling and Reporting
- 1. Structure insights as a narrative
- 2. Create clear and concise analytical reports
- 3. Present insights with visual and verbal techniques
- Data Visualization
- 1. Recognize common visualization types (bar, line, pie charts)
- 2. Select appropriate visuals for different data types
- 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. Define and use functions
- 2. Use variables, data types, and basic operators
- 3. Implement control flow structures (loops, conditionals)
- 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 Ethics and Privacy
- 1. Recognize ethical considerations in data handling
- 2. Understand basic data privacy concepts
- Data Analysis Process and Workflow
- 1. Identify common data sources and collection methods
- 2. Describe the steps of the data analysis process
- 3. Explain the role of data cleaning and preparation
- 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
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q11-Q16):
NEW QUESTION # 11
You are writing a function named process_data()to read and process numerical input from a file. The function must:
- read the file data.txt,
- attempt to convert the first line into an integer,
- handle file, conversion, or index-related exceptions,
- print the value only if no error occurs,
- and always print a final message after execution.
Which implementation correctly and robustly meets all these conditions? Select the best answer.
Answer: C
Explanation:
It wraps file reading and integer conversion in a try block, catches the relevant file, conversion, and indexing exceptions, prints the value only in the no-error path using an else block, and uses a finally block to always print the final message.
NEW QUESTION # 12
An e-commerce site records every product view and purchase.
Which of the following best describes how these logs become marketing insights?
- A. Raw event logs are stored, archived, and indexed - like by user ID and session - which then facilitate audit trails.
- B. Raw event logs are filtered, aggregated, and formatted into summaries - like total visits per category - which then provide traffic reports.
- C. Raw event logs are validated, organized, and compiled into datasets - like timestamped user actions - which then support system debugging.
- D. Raw event logs are structured, analyzed, and interpreted into information - like conversion rates by product - which then builds knowledge of customer preferences.
Answer: D
Explanation:
Marketing insights are produced when raw event logs are structured, analyzed, and interpreted to generate meaningful metrics such as conversion rates. These interpreted results transform data into actionable knowledge about customer behavior and preferences.
NEW QUESTION # 13
Consider a Python program that assigns a string value "5" to a variable and then attempts to multiply it by an integer 3. The programmer expects numeric multiplication. What will actually happen when the code is executed?
Answer: A
Explanation:
In Python, multiplying a string by an integer repeats the string. Therefore, "5" * 3 produces "555", not a numeric result. This behavior differs from arithmetic multiplication and often confuses beginners.
NEW QUESTION # 14
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.split(', ').trim()
- B. fields = line.strip().split(', ')
- C. fields = line.split().strip(', ')
- D. fields = line.replace(', ', '|').split('|')
Answer: B
NEW QUESTION # 15
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(set(colors))
color_counts = np.unique(colors) - 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(unique(colors))
color_counts = Counter(colors)
import numpy as np - D. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
Answer: C
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 # 16
......
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