Exam PCED-30-02 Syllabus, Valid PCED-30-02 Exam Notes

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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Basics for Data Analysis	32.5%	- Built-in modules for data work 1. math, statistics, datetime, collections, csv - Control flow and functions 1. Conditional statements, loops, iteration 2. Defining and calling functions, parameters, return values 3. Basic exception handling - 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 2: Introduction to Data and Data Analysis Concepts	22.5%	- 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 - 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. Nominal, ordinal, interval, ratio scales 2. Qualitative vs quantitative data
Topic 3: Working with Data and Performing Simple Analysis	25%	- 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 cleaning and preparation 1. Handling missing values, duplicates, and errors 2. Filtering, sorting, transforming data 3. Formatting and standardizing values - Data aggregation and grouping 1. Summarizing and grouping datasets
Topic 4: Data Visualization and Communication	20%	- Interpreting and presenting results 1. Reporting findings clearly and concisely 2. Deriving conclusions and insights - Creating basic visualizations 1. Line charts, bar charts, histograms, pie charts 2. Using text and simple plotting tools - Principles of effective data visualization 1. Choosing appropriate chart types 2. Clarity, simplicity, and accuracy

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Valid PCED-30-02 Exam Notes & PCED-30-02 Latest Exam Discount

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q43-Q48):

NEW QUESTION # 43
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:
from pip install numpy
Then run this code:
import numpy as np
data = np.array([10, 20, 30])
print(np.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:
pip install numpy
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))

Answer: A

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 # 44
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

A. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
B. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.
C. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.
D. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.

Answer: A

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.

NEW QUESTION # 45
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 enable seamless sharing of anonymized citizen information with other governmental bodies for collaborative initiatives.
C. To guarantee the long-term availability and integrity of crucial citizen datasets for future policy formulation and evaluation.
D. To uphold data quality for effective service delivery, safeguard citizen privacy and prevent misuse, and comply with data protection laws.

Answer: D

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 # 46
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. Creating summary tables and charts to describe patterns in historical data.
B. Designing a data pipeline to ingest and process real-time sensor data.
C. Developing machine learning algorithms to predict user behavior.
D. Recommending pricing strategies using predictive modeling and A/B testing.

Answer: A

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 # 47
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 = Counter(colors)
color_counts = sum(np.unique(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 = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
D. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np

Answer: D

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 # 48
......

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