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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Basic exception handling 2. Defining and calling functions, parameters, return values 3. Conditional statements, loops, iteration - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - Built-in modules for data work 1. math, statistics, datetime, collections, csv
Topic 2: Data Visualization and Communication	20%	- Principles of effective data visualization 1. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy - Creating basic visualizations 1. Using text and simple plotting tools 2. Line charts, bar charts, histograms, pie charts - Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights
Topic 3: Introduction to Data and Data Analysis Concepts	22.5%	- Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics - 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
Topic 4: Working with Data and Performing Simple Analysis	25%	- Data cleaning and preparation 1. Formatting and standardizing values 2. Handling missing values, duplicates, and errors 3. Filtering, sorting, transforming data - 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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q44-Q49):

NEW QUESTION # 44
The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)

A. Sleep and school take up the majority of time in a day.
B. Leisure and meals combined equal the time spent at school.
C. Leisure accounts for a moderate portion of the student's daily schedule, larger than commuting but smaller than the time spent at school.
D. School and homework together take up half of the day's time.
E. Commuting is the second most time-consuming activity.
F. More time is spent commuting than on leisure.

Answer: A,C

Explanation:
The largest portions of the chart are sleep and school, and together they clearly make up more than half of the day. Leisure appears as a mid-sized segment that is larger than commuting but smaller than school, fitting the description of a moderate portion.

NEW QUESTION # 45
You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

A. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
B. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
C. timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
D. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np

Answer: A

Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.

NEW QUESTION # 46
A Python loop uses range(5, 0, -2) to iterate backward. The programmer expects a decreasing sequence. Which values will actually be generated when this loop runs?

A. 5, 3, 1
B. 5, 4, 3, 2, 1
C. 5, 3
D. 4, 2, 0

Answer: A

Explanation:
The range starts at 5 and decreases by 2 each step until it reaches a value greater than 0. The generated sequence is 5, 3, and 1.

NEW QUESTION # 47
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. Recommending pricing strategies using predictive modeling and A/B testing.
B. Designing a data pipeline to ingest and process real-time sensor data.
C. Creating summary tables and charts to describe patterns in historical data.
D. Developing machine learning algorithms to predict user behavior.

Answer: C

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 # 48
A dictionary is defined as d = {"a":1, "b":2}. The programmer accesses d["c"]. What happens when this line executes?

A. Raises KeyError
B. Returns 0
C. Adds key automatically
D. Returns None

Answer: A

Explanation:
Accessing a non-existent key using square brackets raises a KeyError. Python does not automatically create missing keys, so the program will terminate unless the exception is handled.

NEW QUESTION # 49
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