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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Working with Data and Performing Simple Analyses32.5%- 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. Perform basic data transformation (filtering, sorting, grouping)
  • 2. Handle missing and inconsistent data
Topic 2: Communicating Insights and Reporting12.5%- Data Storytelling and Reporting
  • 1. Create clear and concise analytical reports
  • 2. Structure insights as a narrative
  • 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 Analysis32.5%- Python Fundamentals
  • 1. Use variables, data types, and basic operators
  • 2. Implement control flow structures (loops, conditionals)
  • 3. Define and use functions
- Data Structures
  • 1. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets
- File Handling
  • 1. Use the csv module for basic CSV operations
  • 2. Read from and write to files (text, CSV)
Topic 4: 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. 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. Describe the steps of the data analysis process
  • 3. Explain the role of data cleaning and preparation

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

1. Which of the following statements about Python functions are correct? (Choose two.)

A) Positional arguments must be passed in the order defined, while keyword arguments can be passed in any order using their parameter names.
B) A function cannot call itself or be passed as an argument to itself.
C) Default parameters must always come before required ones in the parameter list.
D) If a function does not contain a returnstatement, it returns Noneby default.
E) Functions can only return numeric values or strings.
F) passexecutes the function body and returns its result.


2. What will be the output of the following code?

A) 10 10
B) 10 5
C) 5 5
D) 5 10


3. Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

A) Developing machine learning algorithms to predict user behavior.
B) Creating summary tables and charts to describe patterns in historical data.
C) Designing a data pipeline to ingest and process real-time sensor data.
D) Recommending pricing strategies using predictive modeling and A/B testing.


4. Below are six possible summaries for your presentation:
- Summary 1: "The data revealed a moderate positive correlation between age and recycling frequency. While 72% of students reported recycling paper regularly, only 39% reported recycling plastic."
- Summary 2: "Analysis showed notable differences in recycling habits by material type, with paper being most recycled and plastic least recycled."
- Summary 3: "Older kids recycled more than younger ones. Paper was the most recycled item- plastic got forgotten a lot."
- Summary 4: "Student responses showed positive environmental habits overall, though participation varied by waste type."
- Summary 5: "A lot of students said they recycle. Some didn't answer every question, so we couldn't figure out everything."
- Summary 6: "We asked more than 300 students about recycling. Most said they recycle paper, but way fewer recycle plastic." Which communication style is the best fit for Group A (data scientists) and Group B (12-year- olds), respectively? Select the best answer.

A) Summary 6 for Group A, Summary 3 for Group B
B) Summary 2 for Group A. Summary 6 for Group B
C) Summary 6 for Group A, Summary 5 for Group B
D) Summary 1 for Group A, Summary 3 for Group B
E) Summary 1 for Group A, Summary 6 for Group B
F) Summary 4 for Group A, Summary 3 for Group B


5. 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) "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
D) The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."


Solutions:

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

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