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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
- Data cleaning and preparation
  • 1. Handling missing values, duplicates, and errors
  • 2. Filtering, sorting, transforming data
  • 3. Formatting and standardizing values
- Exploratory data analysis
  • 1. Identifying patterns, trends, and outliers
  • 2. Calculating mean, median, mode, range, variance, standard deviation
- Data acquisition and loading
  • 1. Reading text, CSV, and structured files
  • 2. Importing data from external sources
Topic 2: 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. Reporting findings clearly and concisely
  • 2. Deriving conclusions and insights
Topic 3: Python Basics for Data Analysis32.5%- Core Python syntax and data types
  • 1. Lists, tuples, sets, dictionaries
  • 2. Variables, numbers, strings, booleans
- Control flow and functions
  • 1. Basic exception handling
  • 2. Conditional statements, loops, iteration
  • 3. Defining and calling functions, parameters, return values
- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
Topic 4: Introduction to Data and Data Analysis Concepts22.5%- 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
- Basic statistical concepts
  • 1. Population, sample, variable, observation
  • 2. Descriptive vs inferential statistics
- Data lifecycle and ethical considerations
  • 1. Data collection, storage, processing, usage, and sharing
  • 2. Privacy, security, bias, and fairness in data
- Data types and measurement scales
  • 1. Nominal, ordinal, interval, ratio scales
  • 2. Qualitative vs quantitative data

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q10-Q15):

NEW QUESTION # 10
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: A

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 # 11
A program uses while False: followed by print statements inside the loop. The developer expects output. What will actually happen during execution?

Answer: A

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 # 12
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 # 13
You are given two lists representing daily page views and sign-ups on a website:
views = [120, 130, 128, 700, 115, 123, 119, 680, 122]
signups = [12, 15, 13, 50, 11, 14, 10, 55, 13]
You want to:
- remove outliers (views > 600), and
- calculate correlation between the cleaned lists.
Which code accomplishes this correctly? Select the best answer.

Answer: A

Explanation:
It converts the lists to NumPy arrays, builds a boolean mask based on the views threshold to remove outliers, applies the same mask to both arrays so they stay aligned, and then computes the correlation on the cleaned pairs using np.corrcoef.


NEW QUESTION # 14
A Python developer is testing truth values of different data types using the bool() function. The expression bool([0]) is evaluated in the script. What result should be expected when this expression is printed?

Answer: A

Explanation:
In Python, any non-empty list evaluates to True in a Boolean context. Even though the element inside the list is zero, the list itself is not empty, so the result is True.


NEW QUESTION # 15
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