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| Section | Weight | Objectives |
|---|
| Topic 1: Communicating Insights and Reporting | 12.5% | - Data Storytelling and Reporting
- 1. Present insights with visual and verbal techniques
- 2. Structure insights as a narrative
- 3. Create clear and concise analytical reports
- Data Visualization
- 1. Select appropriate visuals for different data types
- 2. Interpret simple data visualizations
- 3. Recognize common visualization types (bar, line, pie charts)
|
| Topic 2: 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. Identify basic patterns and trends in data
- 2. Calculate descriptive statistics (mean, median, mode)
- Data Analysis with Python Libraries
- 1. Use the math and statistics modules for basic calculations
- 2. Perform basic operations with NumPy arrays
- 3. Work with the datetime module for date/time data
- 4. Utilize the collections module for specialized containers
|
| Topic 3: Introduction to Data and Data Analysis Concepts | 22.5% | - Data Ethics and Privacy
- 1. Understand basic data privacy concepts
- 2. Recognize ethical considerations in data handling
- 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
- Define and Classify Data
- 1. Differentiate structured, semi-structured, and unstructured data
- 2. Classify data as quantitative or qualitative
- 3. Explain how data becomes meaningful
|
| Topic 4: Python Basics for Data Analysis | 32.5% | - Data Structures
- 1. Work with lists, tuples, dictionaries, and sets
- 2. Apply common operations and methods to data structures
- File Handling
- 1. Read from and write to files (text, CSV)
- 2. Use the csv module for basic CSV operations
- Python Fundamentals
- 1. Use variables, data types, and basic operators
- 2. Define and use functions
- 3. Implement control flow structures (loops, conditionals)
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q37-Q42):
NEW QUESTION # 37
The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)
- A. Leisure and meals combined equal the time spent at school.
- B. School and homework together take up half of the day's time.
- C. Commuting is the second most time-consuming activity.
- D. Sleep and school take up the majority of time in a day.
- 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: D,F
NEW QUESTION # 38
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. Syntax error
- B. input returns string
- C. input returns float
- D. input returns int
Answer: B
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 # 39
You are reviewing a report that compares the number of weekly client meetings and project completion rates across teams. The table below summarizes the data for each team:

Which statement best describes the data shown above? Select the best answer.
- A. "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."
- B. "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."
- C. The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
- D. "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
Answer: A
Explanation:
Teams with moderate meeting counts around 5-6 per week (such as Alpha, Gamma, and Theta) completed among the highest numbers of projects, while teams with very high or very low meeting counts tended to complete fewer projects, indicating that moderate meeting frequency is associated with higher project completion.
NEW QUESTION # 40
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().strip(', ')
- B. fields = line.split(', ').trim()
- C. fields = line.replace(', ', '|').split('|')
- D. fields = line.strip().split(', ')
Answer: D
NEW QUESTION # 41
You are working with city names entered by users. These names may contain inconsistent capitalization and unwanted spaces.
To standardize the data, you want to:
- Remove any leading or trailing whitespace, and
- Capitalize the first letter of each word (e.g., convert "new york" to "New York").
For example:
" New york " → "New York"
"lOS ANGELES" → "Los Angeles"
You are given a variable citythat contains the raw input.
Which line of code correctly updates the value of cleaned_cityto apply the required transformation? Select the best answer.
- A. cleaned_city = city.strip().title()
- B. cleaned_city = city.strip().capitalize()
- C. cleaned_city = city.upper().replace(" ", "")
- D. cleaned_city = city.upper().strip()
Answer: A
Explanation:
Stripping removes leading and trailing whitespace, and applying title formatting capitalizes the first letter of each word while converting the remaining letters to lowercase, producing the correctly standardized city name.
NEW QUESTION # 42
......
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