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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Python Basics for Data Analysis32.5%- File Handling
  • 1. Read from and write to files (text, CSV)
  • 2. Use the csv module for basic CSV operations
- Data Structures
  • 1. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets
- Python Fundamentals
  • 1. Define and use functions
  • 2. Use variables, data types, and basic operators
  • 3. Implement control flow structures (loops, conditionals)
Introduction to Data and Data Analysis Concepts22.5%- Define and Classify Data
  • 1. Explain how data becomes meaningful
  • 2. Differentiate structured, semi-structured, and unstructured data
  • 3. Classify data as quantitative or qualitative
- 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
- Data Ethics and Privacy
  • 1. Recognize ethical considerations in data handling
  • 2. Understand basic data privacy concepts
Communicating Insights and Reporting12.5%- 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. Select appropriate visuals for different data types
  • 2. Interpret simple data visualizations
  • 3. Recognize common visualization types (bar, line, pie charts)
Working with Data and Performing Simple Analyses32.5%- 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
- Data Analysis with Python Libraries
  • 1. Utilize the collections module for specialized containers
  • 2. Perform basic operations with NumPy arrays
  • 3. Use the math and statistics modules for basic calculations
  • 4. Work with the datetime module for date/time data

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q35-Q40):

NEW QUESTION # 35
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.

Answer: D

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 # 36
A program uses while False: followed by print statements inside the loop. The developer expects output. What will actually happen during execution?

Answer: D

Explanation:
The condition False is evaluated before entering the loop. Since it is always false, the loop body is never executed, and no output is produced.


NEW QUESTION # 37
A teacher wants to compare the number of students in different school clubs:

Which type of chart would be most appropriate to show this comparison? Select the best answer.

Answer: D

Explanation:
A bar chart is best for comparing quantities across distinct categories, such as the number of students in each club, because it clearly shows differences in counts side by side.


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?

Answer: A

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
How do the analysis and visualization stages of the data lifecycle typically work together?

Answer: D

Explanation:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.


NEW QUESTION # 40
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