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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 - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Core Python syntax and data types 1. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Control flow and functions 1. Basic exception handling 2. Conditional statements, loops, iteration 3. Defining and calling functions, parameters, return values
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. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools
Working with Data and Performing Simple Analysis	25%	- Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Filtering, sorting, transforming data 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. Importing data from external sources 2. Reading text, CSV, and structured files - Data aggregation and grouping 1. Summarizing and grouping datasets
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. Role of data in decision-making and business 2. Process of turning raw data into insights 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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q18-Q23):

NEW QUESTION # 18
A list is reversed using slicing: lst[::-1]. The programmer wants to confirm whether this modifies the original list or creates a new one. What is the correct behavior?

A. Error
B. Modifies original
C. Creates new list
D. Returns None

Answer: C

Explanation:
Slicing creates a new list object rather than modifying the original. The expression lst[::-1] returns a reversed copy, leaving the original list unchanged.

NEW QUESTION # 19
What will be the output of the following code?

A. 10 5
B. 10 10
C. 5 5
D. 5 10

Answer: C

Explanation:
The function declares the variable as global and assigns it the value 5. When the function is called, it updates the global variable and returns 5, so both the returned value and the global variable printed afterward are 5.

NEW QUESTION # 20
The following chart shows how a student spends 24 hours in a day:

Which statements are most accurate? (Choose two.)

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

Answer: D,F

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 # 21
A government agency collects citizen data for various public services.
What is the primary reason for emphasizing careful data management throughout the entire data lifecycle in this context? Select the best answer.

A. To streamline the initial capture of citizen details and minimize the administrative burden on both staff and the public.
B. To guarantee the long-term availability and integrity of crucial citizen datasets for future policy formulation and evaluation.
C. To uphold data quality for effective service delivery, safeguard citizen privacy and prevent misuse, and comply with data protection laws.
D. To enable seamless sharing of anonymized citizen information with other governmental bodies for collaborative initiatives.

Answer: C

Explanation:
Careful data management across the entire lifecycle ensures high data quality for reliable public services, protects sensitive citizen information from misuse, and maintains compliance with legal and regulatory data protection requirements.

NEW QUESTION # 22
A healthcare provider analyzes patient records to determine that appointment cancellations spike during weather alerts and flu season.
What type of analytics is this?

A. Predictive analytics - it uses weather and illness trends to forecast future cancellations.
B. Descriptive analytics - it reports appointment numbers across past seasons.
C. Diagnostic analytics - it identifies reasons behind patterns in patient behavior.
D. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.

Answer: C

Explanation:
It examines existing data to understand why cancellations increase, identifying underlying causes such as weather alerts and flu season, which characterizes diagnostic analytics.

NEW QUESTION # 23
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