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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Control flow and functions 1. Basic exception handling 2. Conditional statements, loops, iteration 3. Defining and calling functions, parameters, return values
Topic 2: Working with Data and Performing Simple Analysis	25%	- Exploratory data analysis 1. Identifying patterns, trends, and outliers 2. Calculating mean, median, mode, range, variance, standard deviation - Data cleaning and preparation 1. Formatting and standardizing values 2. Handling missing values, duplicates, and errors 3. Filtering, sorting, transforming data - Data aggregation and grouping 1. Summarizing and grouping datasets - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources
Topic 3: Introduction to Data and Data Analysis Concepts	22.5%	- Data types and measurement scales 1. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative data - 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. 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
Topic 4: Data Visualization and Communication	20%	- Creating basic visualizations 1. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools - 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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q47-Q52):

NEW QUESTION # 47
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 = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
B. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(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 = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np

Answer: B

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 # 48
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?

A. ythn
B. yth
C. ytho
D. tho

Answer: B

Explanation:
Slicing includes the start index but excludes the end index. Characters at indices 1, 2, and 3 are selected, producing the substring "yth".

NEW QUESTION # 49
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 and 4
C. 4 and 3
D. 3.5 and 3

Answer: D

Explanation:
The / operator performs true division, returning a float result of 3.5. The // operator performs floor division, truncating the decimal part and returning the integer value 3.

NEW QUESTION # 50
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

A. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.
B. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.
C. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
D. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.

Answer: C

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.

NEW QUESTION # 51
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?

A. fields = line.split(', ').trim()
B. fields = line.replace(', ', '|').split('|')
C. fields = line.split().strip(', ')
D. fields = line.strip().split(', ')

Answer: D

NEW QUESTION # 52
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