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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%	- Definition and classification of data 1. Process of turning raw data into insights 2. Difference between data, information, and knowledge 3. Role of data in decision-making and business - Data lifecycle and ethical considerations 1. Data collection, storage, processing, usage, and sharing 2. Privacy, security, bias, and fairness in data - Basic statistical concepts 1. Population, sample, variable, observation 2. Descriptive vs inferential statistics - Data types and measurement scales 1. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative data
Topic 2: Python Basics for Data Analysis	32.5%	- Built-in modules for data work 1. math, statistics, datetime, collections, csv - Control flow and functions 1. Defining and calling functions, parameters, return values 2. Basic exception handling 3. Conditional statements, loops, iteration - Core Python syntax and data types 1. Variables, numbers, strings, booleans 2. Lists, tuples, sets, dictionaries - Introduction to NumPy 1. Arrays, basic operations, indexing, slicing
Topic 3: 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 acquisition and loading 1. Reading text, CSV, and structured files 2. Importing data from external sources - Data cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Formatting and standardizing values 3. Filtering, sorting, transforming data - Data aggregation and grouping 1. Summarizing and grouping datasets
Topic 4: 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. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy - Interpreting and presenting results 1. Deriving conclusions and insights 2. Reporting findings clearly and concisely

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q37-Q42):

NEW QUESTION # 37
You have a list of test scores, where each entry includes a student name and a score. Some students appear more than once. You want to compute the average score for each student and store the results in a dictionary. Here's the partial code block:

Which code correctly replaces the # MISSING CODE comment to calculate the average score for each student? Select the best answer.

Answer: D

Explanation:
It iterates through each record, extracts the name and score, and uses dictionary .get() to accumulate total scores and counts per student safely, initializing missing keys to zero. This correctly enables calculation of each student's average afterward.

NEW QUESTION # 38
You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

A. The graph doesn't explain what each stress level means or show exact values for each month, which limits interpretation.
B. The chart should be in 3D format to show depth and highlight differences more clearly.
C. The lines are too light and similar in color, making it difficult to distinguish between trends.
D. The font used in the chart is hard to read; using larger, uppercase text would improve clarity.
E. The months should be replaced with week numbers to provide more detailed insight.

Answer: C

Explanation:
The lines are very light and visually similar, making it difficult to clearly distinguish between the different stress level trends, which directly reduces the effectiveness of the visualization in communicating patterns over time.

NEW QUESTION # 39
A data analyst exports a cleaned dataset from Python and wants to share it with a colleague for easy use in Excel.
Which file format is best suited for this purpose? Select the best answer.

A. SQL, because it directly converts tabular data into database tables that Excel can open.
B. XML, because it automatically preserves spreadsheet formatting and charts.
C. CSV, because it stores tabular data in plain text and is compatible with spreadsheet applications.
D. JSON, because it stores hierarchical data and is designed for visual presentation in Excel.

Answer: C

Explanation:
CSV stores tabular data in a simple plain-text, comma-separated format that is widely supported by spreadsheet applications like Excel, making it ideal for sharing datasets for easy import and analysis.

NEW QUESTION # 40
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 = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
B. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)
C. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
D. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np

Answer: A

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 # 41
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 as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.mean(data))
B. To install NumPy:
pip install numpy
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.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 # 42
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