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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Introduction to Data and Data Analysis Concepts22.5%- Basic statistical concepts
  • 1. Descriptive vs inferential statistics
  • 2. Population, sample, variable, observation
- Data types and measurement scales
  • 1. Nominal, ordinal, interval, ratio scales
  • 2. Qualitative vs quantitative data
- Data lifecycle and ethical considerations
  • 1. Privacy, security, bias, and fairness in data
  • 2. Data collection, storage, processing, usage, and sharing
- Definition and classification of data
  • 1. Role of data in decision-making and business
  • 2. Process of turning raw data into insights
  • 3. Difference between data, information, and knowledge
Topic 2: Working with Data and Performing Simple Analysis25%- Data acquisition and loading
  • 1. Importing data from external sources
  • 2. Reading text, CSV, and structured files
- Exploratory data analysis
  • 1. Identifying patterns, trends, and outliers
  • 2. Calculating mean, median, mode, range, variance, standard deviation
- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Data cleaning and preparation
  • 1. Formatting and standardizing values
  • 2. Filtering, sorting, transforming data
  • 3. Handling missing values, duplicates, and errors
Topic 3: Python Basics for Data Analysis32.5%- Control flow and functions
  • 1. Conditional statements, loops, iteration
  • 2. Defining and calling functions, parameters, return values
  • 3. Basic exception handling
- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Core Python syntax and data types
  • 1. Variables, numbers, strings, booleans
  • 2. Lists, tuples, sets, dictionaries
Topic 4: Data Visualization and Communication20%- Interpreting and presenting results
  • 1. Deriving conclusions and insights
  • 2. Reporting findings clearly and concisely
- Principles of effective data visualization
  • 1. Choosing appropriate chart types
  • 2. Clarity, simplicity, and accuracy
- Creating basic visualizations
  • 1. Line charts, bar charts, histograms, pie charts
  • 2. Using text and simple plotting tools

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q27-Q32):

NEW QUESTION # 27
A Python script checks membership using "a" in "apple". The developer wants to understand how membership works for strings. What result will this expression produce?

Answer: A

Explanation:
The in operator checks for substring presence. Since the character "a" exists in "apple", the expression evaluates to True, demonstrating how membership works for strings in Python.


NEW QUESTION # 28
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.

Answer: F

Explanation:
The first summary uses precise statistical language and quantitative detail appropriate for data scientists. The third summary uses simpler, more conversational language that is easier for 12- year-olds to understand.


NEW QUESTION # 29
A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)

Answer: A,B

Explanation:
Encrypting stored and transmitted data protects sensitive information from unauthorized access and supports regulatory requirements for data security. Providing clear explanations of data usage and obtaining explicit consent ensures transparency and aligns with core principles of data protection regulations such as lawful processing and user rights.


NEW QUESTION # 30
A health researcher uses wearable devices to record physical activity and sends a survey to randomly selected participants across age groups.
Why can this approach be effective? Select the best answer.

Answer: D

Explanation:
Combining automated wearable data with a randomly selected survey sample integrates objective measurements with representative participant input, which helps reduce bias and improves the reliability and generalizability of the findings.


NEW QUESTION # 31
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

Answer: A

Explanation:
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].


NEW QUESTION # 32
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