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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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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: D

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
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: C

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 # 29
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?

Answer: A

Explanation:
Stripping removes leading and trailing whitespace from the line, and then splitting on the comma- space delimiter correctly separates the values into a list for further formatting.


NEW QUESTION # 30
You have a list of test scores, where each entry includes a student name and a score. Some students appear more than once. You want to compute the average score for each student and store the results in a dictionary. Here's the partial code block:

Which code correctly replaces the # MISSING CODE comment to calculate the average score for each student? Select the best answer.

Answer: C

Explanation:
It iterates through each record, extracts the name and score, and uses dictionary .get() to accumulate total scores and counts per student safely, initializing missing keys to zero. This correctly enables calculation of each student's average afterward.


NEW QUESTION # 31
A program compares two values using == and is. The variables a = 256 and b = 256 are defined.
What is the expected result of a is b in most Python implementations?

Answer: D

Explanation:
Python caches small integers for efficiency. Since 256 is typically within the cached range, both variables reference the same object in memory, making a is b evaluate to True.


NEW QUESTION # 32
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