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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. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Built-in modules for data work 1. math, statistics, datetime, collections, csv - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing - Control flow and functions 1. Defining and calling functions, parameters, return values 2. Conditional statements, loops, iteration 3. Basic exception handling
Topic 2: Working with Data and Performing Simple Analysis	25%	- 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 - 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
Topic 3: 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. Clarity, simplicity, and accuracy 2. Choosing appropriate chart types - Interpreting and presenting results 1. Deriving conclusions and insights 2. Reporting findings clearly and concisely
Topic 4: 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 - 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 - Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q28-Q33):

NEW QUESTION # 28
Below are six possible summaries for your presentation:
- Summary 1: "The data revealed a moderate positive correlation between age and recycling frequency. While 72% of students reported recycling paper regularly, only 39% reported recycling plastic."
- Summary 2: "Analysis showed notable differences in recycling habits by material type, with paper being most recycled and plastic least recycled."
- Summary 3: "Older kids recycled more than younger ones. Paper was the most recycled item- plastic got forgotten a lot."
- Summary 4: "Student responses showed positive environmental habits overall, though participation varied by waste type."
- Summary 5: "A lot of students said they recycle. Some didn't answer every question, so we couldn't figure out everything."
- Summary 6: "We asked more than 300 students about recycling. Most said they recycle paper, but way fewer recycle plastic." Which communication style is the best fit for Group A (data scientists) and Group B (12-year- olds), respectively? Select the best answer.

A. Summary 4 for Group A, Summary 3 for Group B
B. Summary 6 for Group A, Summary 3 for Group B
C. Summary 2 for Group A. Summary 6 for Group B
D. Summary 6 for Group A, Summary 5 for Group B
E. Summary 1 for Group A, Summary 3 for Group B
F. Summary 1 for Group A, Summary 6 for Group B

Answer: E

Explanation:
The first summary uses precise statistical language and quantitative detail appropriate for data scientists. The third summary uses simpler, more conversational language that is easier for 12- year-olds to understand.

NEW QUESTION # 29
A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)

A. Implementing encryption for both stored and transmitted customer data.
B. Automatically opting users into data sharing unless they manually change settings.
C. Limiting data access only to marketing staff to streamline personalization.
D. Retaining user data permanently unless users explicitly request deletion.
E. Providing users with a clear explanation of how their data is used and obtaining explicit consent.

Answer: A,E

Explanation:
Encrypting stored and transmitted data protects sensitive information from unauthorized access and supports regulatory requirements for data security. Providing clear explanations of data usage and obtaining explicit consent ensures transparency and aligns with core principles of data protection regulations such as lawful processing and user rights.

NEW QUESTION # 30
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
B. 5, 4, 3, 2, 1
C. 5, 3, 1
D. 4, 2, 0

Answer: C

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 # 31
You have collected sales data and want to compute the average using the NumPy library.
Which option correctly shows how to install NumPy and use it in your code to perform this task?

A. To install NumPy:
pip install numpy
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))
B. To install NumPy:
pip install numpy as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.mean(data))
C. To install NumPy:
from pip install numpy
Then run this code:
import numpy as np
data = np.array([10, 20, 30])
print(np.mean(data))

Answer: C

Explanation:
NumPy is installed with pip install numpy, then imported with import numpy as np. After that, you can create an array and compute the mean using np.array(...) and np.mean(...).

NEW QUESTION # 32
You are reviewing a report that compares the number of weekly client meetings and project completion rates across teams. The table below summarizes the data for each team:

Which statement best describes the data shown above? Select the best answer.

A. "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
B. The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
C. "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."
D. "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."

Answer: D

Explanation:
Teams with moderate meeting counts around 5-6 per week (such as Alpha, Gamma, and Theta) completed among the highest numbers of projects, while teams with very high or very low meeting counts tended to complete fewer projects, indicating that moderate meeting frequency is associated with higher project completion.

NEW QUESTION # 33
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