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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Python Basics for Data Analysis	32.5%	- 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. Defining and calling functions, parameters, return values 2. Basic exception handling 3. Conditional statements, loops, iteration - Built-in modules for data work 1. math, statistics, datetime, collections, csv
Introduction to Data and Data Analysis Concepts	22.5%	- 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. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales - 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 Visualization and Communication	20%	- Interpreting and presenting results 1. Deriving conclusions and insights 2. Reporting findings clearly and concisely - Principles of effective data visualization 1. Clarity, simplicity, and accuracy 2. Choosing appropriate chart types - Creating basic visualizations 1. Using text and simple plotting tools 2. Line charts, bar charts, histograms, pie charts
Working with Data and Performing Simple Analysis	25%	- Data cleaning and preparation 1. Filtering, sorting, transforming data 2. Formatting and standardizing values 3. Handling missing values, duplicates, and errors - 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 acquisition and loading 1. Importing data from external sources 2. Reading text, CSV, and structured files

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q39-Q44):

NEW QUESTION # 39
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. passexecutes the function body and returns its result.
C. A function cannot call itself or be passed as an argument to itself.
D. Functions can only return numeric values or strings.
E. If a function does not contain a returnstatement, it returns Noneby default.
F. Default parameters must always come before required ones in the parameter list.

Answer: A,E

Explanation:
Positional arguments are matched to parameters by their position, so they must follow the defined order, while keyword arguments are matched by name and can be provided in any order. If a function completes without an explicit return statement, Python returns None automatically.

NEW QUESTION # 40
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.

A. Pie chart
B. Bubble chart
C. Line chart
D. Bar chart

Answer: D

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 # 41
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: D

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 # 42
A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?

A. Loop restarts
B. Error
C. Skips one iteration
D. Loop ends immediately

Answer: D

Explanation:
The break statement immediately terminates the loop, regardless of remaining iterations. Control is transferred to the first statement following the loop block.

NEW QUESTION # 43
You are given the following list of daily step counts:
steps = [8230, 9020, 7640, 8760, 10020, 2546, 9817]
Your task is to calculate:
- the standard deviation of the step counts,
- the average rounded up to the nearest whole number, and
- the median of the step counts.
Which code snippet correctly performs all three tasks? Select the best answer.
import statistics

A. import math
print(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics
B. import math
print(statistics.stdev(steps))
print(math.ceil(statistics.mean(steps)))
print(statistics.median(steps))
C. import math
print(statistics.variance(steps))
print(math.ceil(sum(steps) / len(steps)))
print(math.floor(statistics.median(steps)))
import statistics
D. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics

Answer: B

Explanation:
It uses statistics.stdev() to compute the standard deviation, statistics.mean() to compute the average and math.ceil() to round it up to the nearest whole number, and statistics.median() to compute the median.

NEW QUESTION # 44
......

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