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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Difference between data, information, and knowledge 2. Process of turning raw data into insights 3. Role of data in decision-making and business - 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. Qualitative vs quantitative data 2. Nominal, ordinal, interval, ratio scales
Topic 2: Working with Data and Performing Simple Analysis	25%	- Data cleaning and preparation 1. Filtering, sorting, transforming data 2. Handling missing values, duplicates, and errors 3. Formatting and standardizing values - Exploratory data analysis 1. Calculating mean, median, mode, range, variance, standard deviation 2. Identifying patterns, trends, and outliers - Data acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - Data aggregation and grouping 1. Summarizing and grouping datasets
Topic 3: Python Basics for Data Analysis	32.5%	- Core Python syntax and data types 1. Lists, tuples, sets, dictionaries 2. Variables, numbers, strings, booleans - Control flow and functions 1. Defining and calling functions, parameters, return values 2. Conditional statements, loops, iteration 3. Basic exception handling - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing
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 - 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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

A. Designing a data pipeline to ingest and process real-time sensor data.
B. Creating summary tables and charts to describe patterns in historical data.
C. Recommending pricing strategies using predictive modeling and A/B testing.
D. Developing machine learning algorithms to predict user behavior.

Answer: B

Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.

NEW QUESTION # 21
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().strip(', ')
B. fields = line.split(', ').trim()
C. fields = line.strip().split(', ')
D. fields = line.replace(', ', '|').split('|')

Answer: C

NEW QUESTION # 22
A Python script includes the expression not False and True. The developer is unsure how logical operators are evaluated in terms of precedence. What will be the final Boolean result of this expression?

A. False
B. True
C. None
D. Error

Answer: B

Explanation:
The not operator has higher precedence than and, so not False becomes True first. Then True and True is evaluated, resulting in True as the final Boolean value.

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

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

Answer: D

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

Answer: C

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
......

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