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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Visualization and Communication	20%	- 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 - Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights
Python Basics for Data Analysis	32.5%	- Core Python syntax and data types 1. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Control flow and functions 1. Conditional statements, loops, iteration 2. Basic exception handling 3. Defining and calling functions, parameters, return values
Introduction to Data and Data Analysis Concepts	22.5%	- Basic statistical concepts 1. Descriptive vs inferential statistics 2. Population, sample, variable, observation - Definition and classification of data 1. Process of turning raw data into insights 2. Role of data in decision-making and business 3. Difference between data, information, and knowledge - Data lifecycle and ethical considerations 1. Privacy, security, bias, and fairness in data 2. Data collection, storage, processing, usage, and sharing - Data types and measurement scales 1. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative data
Working with Data and Performing Simple Analysis	25%	- Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - 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. Handling missing values, duplicates, and errors 3. Filtering, sorting, transforming data

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?

Answer: A

Explanation:
Adding an integer and a float produces a float result, so checking that the type of the sum is float evaluates to True. The variable holding 3.0 is a float, so the isinstance check for float also evaluates to True.

NEW QUESTION # 22
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.

A. cleaned_city = city.strip().capitalize()
B. cleaned_city = city.strip().title()
C. cleaned_city = city.upper().replace(" ", "")
D. cleaned_city = city.upper().strip()

Answer: B

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 # 23
How do the analysis and visualization stages of the data lifecycle typically work together?

A. Analysis retrieves raw inputs from dashboards, while visualization uses that input to clean the data.
B. Analysis identifies patterns and insights in processed data, which are then communicated through visualizations for informed decision-making.
C. Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
D. Analysis summarizes archived data, and visualization transforms it into structured tables for storage.

Answer: B

Explanation:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.

NEW QUESTION # 24
You are analyzing survey results from students about their favorite colors. The list colorsstores individual responses:

You want to:
- find the number of unique colors mentioned using NumPy, and
- determine how often each color was chosen using Counter.
Which code snippet correctly performs both tasks? Select the best answer.
from numpy import unique

A. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
B. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
C. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)
D. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np

Answer: D

Explanation:
numpy.unique returns the distinct values in the list, and taking its length gives the number of unique colors. Counter(colors) correctly counts how many times each color appears in the responses.

NEW QUESTION # 25
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. Functions can only return numeric values or strings.
C. A function cannot call itself or be passed as an argument to itself.
D. passexecutes the function body and returns its result.
E. Default parameters must always come before required ones in the parameter list.
F. If a function does not contain a returnstatement, it returns Noneby default.

Answer: A,F

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 # 26
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