Quiz Python Institute - PCED-30-02 - Newest PCED - Certified Entry-Level Data Analyst with Python PDF VCE

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| Section | Weight | Objectives |
|---|
| Topic 1: Python Basics for Data Analysis | 32.5% | - 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. Lists, tuples, sets, dictionaries
- 2. Variables, numbers, strings, booleans
- Built-in modules for data work
- 1. math, statistics, datetime, collections, csv
- Introduction to NumPy
- 1. Arrays, basic operations, indexing, slicing
|
| Topic 2: Working with Data and Performing Simple Analysis | 25% | - Exploratory data analysis
- 1. Calculating mean, median, mode, range, variance, standard deviation
- 2. Identifying patterns, trends, and outliers
- Data cleaning and preparation
- 1. Filtering, sorting, transforming data
- 2. Handling missing values, duplicates, and errors
- 3. Formatting and standardizing values
- Data aggregation and grouping
- 1. Summarizing and grouping datasets
- Data acquisition and loading
- 1. Reading text, CSV, and structured files
- 2. Importing data from external sources
|
| Topic 3: Data Visualization and Communication | 20% | - Interpreting and presenting results
- 1. Deriving conclusions and insights
- 2. Reporting findings clearly and concisely
- 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
|
| Topic 4: Introduction to Data and Data Analysis Concepts | 22.5% | - Data lifecycle and ethical considerations
- 1. Data collection, storage, processing, usage, and sharing
- 2. Privacy, security, bias, and fairness in data
- Data types and measurement scales
- 1. Qualitative vs quantitative data
- 2. Nominal, ordinal, interval, ratio scales
- Basic statistical concepts
- 1. Population, sample, variable, observation
- 2. Descriptive vs inferential statistics
- Definition and classification of data
- 1. Difference between data, information, and knowledge
- 2. Role of data in decision-making and business
- 3. Process of turning raw data into insights
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q45-Q50):
NEW QUESTION # 45
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?
- A. yth
- B. ythn
- C. tho
- D. ytho
Answer: A
Explanation:
Slicing includes the start index but excludes the end index. Characters at indices 1, 2, and 3 are selected, producing the substring "yth".
NEW QUESTION # 46
Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?
Answer: B
Explanation:
Adding an integer and a float produces a float result, so checking that the type of the sum is float evaluates to True. The variable holding 3.0 is a float, so the isinstance check for float also evaluates to True.
NEW QUESTION # 47
Consider a Python program that assigns a string value "5" to a variable and then attempts to multiply it by an integer 3. The programmer expects numeric multiplication. What will actually happen when the code is executed?
Answer: B
Explanation:
In Python, multiplying a string by an integer repeats the string. Therefore, "5" * 3 produces "555", not a numeric result. This behavior differs from arithmetic multiplication and often confuses beginners.
NEW QUESTION # 48
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. 4, 2, 0
- B. 5, 3
- C. 5, 3, 1
- D. 5, 4, 3, 2, 1
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 # 49
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 = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np - B. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(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 # 50
......
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