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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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

Question 1

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.

A. It combines automated data collection with representative sampling, reducing the risk of bias.
B. It relies on observational methods, which are more accurate than surveys or sensors.
C. It limits sampling to one demographic, ensuring high-quality data within a specific group.
D. It uses unstructured interviews to validate automated tracking, improving bias detection.


Question 2

A script attempts to divide two numbers using / and //. The values are 7 and 2. The developer wants to understand the difference between true division and floor division. What are the results respectively?

A. 3 and 3.5
B. 3.5 and 3
C. 4 and 3
D. 3 and 4


Question 3

You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

A.

B.

C.

D.


Question 4

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?

A. None
B. False
C. Error
D. True


Question 5

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.)

A. Automatically opting users into data sharing unless they manually change settings.
B. Providing users with a clear explanation of how their data is used and obtaining explicit consent.
C. Limiting data access only to marketing staff to streamline personalization.
D. Implementing encryption for both stored and transmitted customer data.
E. Retaining user data permanently unless users explicitly request deletion.


Solutions:

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

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