Microsoft Power BI Data Analyst Professional Certificate

Microsoft Power BI Data Analyst Professional Certificate

 

What you'll learn

Learn to use Power BI to connect to data sources and transform them into meaningful insights.  

Prepare Excel data for analysis in Power BI using the most common formulas and functions in a worksheet.     

Learn to use the visualization and report capabilities of Power BI to create compelling reports and dashboards.  

Demonstrate your new skills with a capstone project and prepare for the industry-recognized Microsoft PL-300 Certification exam.  

Join Free: Microsoft Power BI Data Analyst Professional Certificate

Professional Certificate - 8 course series

Learners who complete this program will receive a 50% discount voucher to take the PL-300 Certification Exam. 

Business Intelligence analysts are highly sought after as more organizations rely on data-driven decision-making. Microsoft Power BI is the leading data analytics, business intelligence, and reporting tool in the field, used by 97% of Fortune 500 companies to make decisions based on data-driven insights and analytics.1 Prepare for a new career in this high-growth field with professional training from Microsoft — an industry-recognized leader in data analytics and business intelligence.

Through a mix of videos, assessments, and hands-on activities, you will engage with the key concepts of Power BI, transforming data into meaningful insights and creating compelling reports and dashboards. You will learn to prepare data in Excel for analysis in Power BI, form data models using the Star schema, perform calculations in DAX, and more.

In your final project, you will showcase your new Power BI and data analysis skills using a real-world scenario. When you complete this Professional Certificate, you’ll have tangible examples to talk about in your job interviews and you’ll also be prepared to take the industry-recognized PL-300: Microsoft Power BI Data Analyst certification exam.

1Microsoft named a Leader in the 2023 Gartner® Magic Quadrant™ for Analytics and BI Platforms (April 2023)

Applied Learning Project

This program has been uniquely mapped to key job skills required in a Power BI data analyst role. In each course, you’ll be able to consolidate what you have learned by completing a project that simulates a real-world data analysis scenario using Power BI. You’ll also complete a final capstone project where you’ll showcase all your new Power BI data analytical skills.

The projects will include:

● A real-world scenario where you connect to data sources and transform data into an optimized data model for data analysis. 

● A real-world scenario where you demonstrate data storytelling through dashboards, reports and charts to solve business challenges and identify new opportunities.

A real-world capstone project where you analyze the performance of a multinational business and prepare executive dashboards and reports.

To round off your learning, you’ll take a mock exam that has been set up in a similar style to the industry-recognized Exam PL-300: Microsoft Power BI Data Analyst.

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Data Analysis with R Programming

 

What you'll learn

Describe the R programming language and its programming environment.

Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.

Describe the options for generating visualizations in R.

Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.

Join Free: Data Analysis with R Programming

There are 5 modules in this course

This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R, and the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

- Examine the benefits of using the R programming language.

- Discover how to use RStudio to apply R to your analysis. 

- Explore the fundamental concepts associated with programming in R. 

- Understand the contents and components of R packages including the Tidyverse package.

- Gain an understanding of dataframes and their use in R.

- Discover the options for generating visualizations in R.

- Learn about R Markdown for documenting R programming.

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IBM Data Science Professional Certificate

 

What you'll learn

Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles

Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL

Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines

Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers

Join Free: IBM Data Science Professional Certificate

Professional Certificate - 10 course series

Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 5 months. No prior knowledge of computer science or programming languages is required. 

Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater. 

You’ll learn in-demand skills used by professional data scientists including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. You’ll also work with the latest languages, tools,and libraries including Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more.

Upon completing the full program, you will have built a portfolio of data science projects to provide you with the confidence to excel in your interviews. You will also receive access to join IBM’s Talent Network where you’ll see job opportunities as soon as they are posted, recommendations matched to your skills and interests, and tips and tricks to help you stand apart from the crowd. 

This program is ACE® and FIBAA recommended —when you complete, you can earn up to 12 college credits and 6 ECTS credits.

Applied Learning Project

This Professional Certificate has a strong emphasis on applied learning and includes a series of hands-on labs in the IBM Cloud that give you practical skills with applicability to real jobs.

Tools you’ll use: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio

Libraries you’ll use: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.

Projects you’ll complete:

Extract and graph financial data with the Pandas Python library

Use SQL to query census, crime, and school demographic data sets

Wrangle data, graph plots, and create regression models to predict housing prices with data science Python libraries

Create a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability

Apply and compare machine learning classification algorithms to predict whether a loan case will be paid off or not

Train and compare machine learning models to predict if a space launch can reuse the first stage of a rocket

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Indian Flag using NumPy and Matplotlib in Python

 

Code : 

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.patches as patches

def draw_tricolor_flag():

    # Create figure and axes

    fig, ax = plt.subplots()

    # Draw tricolor bands

    colors = ['#138808', '#ffffff', '#FF6103']

    for i, color in enumerate(colors):

        rect = patches.Rectangle((0, 2*i+1), width=9, height=2, facecolor=color, edgecolor='grey')

        ax.add_patch(rect)

    # Draw Ashoka Chakra circle

    chakra_radius = 0.8

    ax.plot(4.5, 4, marker='o', markerfacecolor='#000080', markersize=9.5)

    chakra = patches.Circle((4.5, 4), chakra_radius, color='#000080', fill=False, linewidth=7)

    ax.add_artist(chakra)

    # Draw 24 spokes in Ashoka Chakra

    for i in range(24):

        angle1 = np.pi * i / 12 - np.pi / 48

        angle2 = np.pi * i / 12 + np.pi / 48

        spoke = patches.Polygon([[4.5, 4],

                                 [4.5 + chakra_radius / 2 * np.cos(angle1),

                                  4 + chakra_radius / 2 * np.sin(angle1)],

                                 [4.5 + chakra_radius * np.cos(np.pi * i / 12),

                                  4 + chakra_radius * np.sin(np.pi * i / 12)],

                                 [4.5 + chakra_radius / 2 * np.cos(angle2),

                                  4 + chakra_radius / 2 * np.sin(angle2)]],

                                fill=True, closed=True, color='#000080')

        ax.add_patch(spoke)

    # Set equal axis and display the plot

    ax.axis('equal')

    plt.show()

# Call the function to draw the tricolor flag

draw_tricolor_flag()

#clcoding.com

Explanation:

Imports:

numpy for numerical operations.

matplotlib.pyplot for creating plots.

matplotlib.patches for creating shapes like rectangles and polygons.

Function Definition (draw_tricolor_flag):

Creates a figure and axes for the plot.

Drawing Tricolor Bands:

Three rectangles are drawn to represent the tricolor bands of the flag using the colors green ('#138808'), white ('#ffffff'), and saffron ('#FF6103').

Drawing Ashoka Chakra Circle:

A circle is drawn at the center of the plot representing the Ashoka Chakra. It is outlined with a blue color ('#000080').

Drawing 24 Spokes in Ashoka Chakra:

A loop calculates the coordinates for each spoke and uses patches.Polygon to draw each spoke. The spokes are drawn in blue ('#000080').

Setting Equal Axis and Displaying the Plot:

The axis is set to be equal, ensuring an equal aspect ratio, and the plot is displayed.

The comment #clcoding.com at the end of your code appears to be a website or identifier but doesn't affect the code's functionality.

Code Explanation 

Imports:

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.patches as patches

numpy is imported as np for numerical operations.

matplotlib.pyplot is imported as plt for creating plots.

matplotlib.patches is imported as patches for creating shapes like rectangles and polygons.

Function Definition:

def draw_tricolor_flag():

This defines a function named draw_tricolor_flag.

Creating Figure and Axes:

fig, ax = plt.subplots()

This creates a figure and axes for the plot.

Drawing Tricolor Bands:

colors = ['#138808', '#ffffff', '#FF6103']

for i, color in enumerate(colors):

    rect = patches.Rectangle((0, 2*i+1), width=9, height=2, facecolor=color, edgecolor='grey')

    ax.add_patch(rect)

It defines three colors for the tricolor bands and iterates through them, drawing rectangles for each color.

Drawing Ashoka Chakra Circle:

chakra_radius = 0.8

ax.plot(4.5, 4, marker='o', markerfacecolor='#000080', markersize=9.5)

chakra = patches.Circle((4.5, 4), chakra_radius, color='#000080', fill=False, linewidth=7)

ax.add_artist(chakra)

It draws a circle at the center of the plot representing the Ashoka Chakra.

Drawing 24 Spokes in Ashoka Chakra:

for i in range(24):

    angle1 = np.pi * i / 12 - np.pi / 48

    angle2 = np.pi * i / 12 + np.pi / 48

    spoke = patches.Polygon([[4.5, 4], 

                             [4.5 + chakra_radius / 2 * np.cos(angle1), 

                              4 + chakra_radius / 2 * np.sin(angle1)], 

                             [4.5 + chakra_radius * np.cos(np.pi * i / 12), 

                              4 + chakra_radius * np.sin(np.pi * i / 12)], 

                             [4.5 + chakra_radius / 2 * np.cos(angle2), 

                              4 + chakra_radius / 2 * np.sin(angle2)]], 

                            fill=True, closed=True, color='#000080')

    ax.add_patch(spoke)

It uses a loop to draw 24 spokes in the Ashoka Chakra.

Setting Equal Axis and Displaying the Plot:

ax.axis('equal')

plt.show()

It ensures that the aspect ratio of the plot is equal and then displays the plot.

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How much do you know about Python Modules and packages?

 a. A function can belong to a module and the module can belong to a

package.

Answer

True

b. A package can contain one or more modules in it.

Answer

True

c. Nested packages are allowed.

Answer

True

d. Contents of sys.path variable cannot be modified.

Answer

False

e. In the statement import a.b.c, c cannot be a function.

Answer

True

f. It is a good idea to use * to import all the functions/classes defined in a

module.

Answer

True

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Microsoft Azure Fundamentals AZ-900 Exam Prep Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from Microsoft

Join Free: Microsoft Azure Fundamentals AZ-900 Exam Prep Specialization

Specialization - 4 course series

Cloud computing is rapidly expanding into all areas of businesses, creating new and exciting career opportunities. These opportunities cover a broad range of roles, from developers and architects to security professionals and data scientists. This program will give you the fundamental knowledge, skills, and confidence to begin your Microsoft Azure certification journey.

This Microsoft Azure Fundamentals AZ-900 Exam Prep Specialization consists of four courses that will act as a bedrock of fundamental knowledge to prepare you for the AZ-900 certification exam and for a career in the cloud. The content of this program is tightly aligned to the AZ-900 exam objective domains.

This program will provide foundational level knowledge on Microsoft Azure concepts; core Microsoft Azure services; core solutions and management tools; general security and network security; governance, privacy, and compliance features; Microsoft Azure cost management, and service level agreements. Ideal for IT personnel just beginning to work with Microsoft Azure or anyone wanting to learn about it.

This Specialization will prepare you to take the AZ-900: Microsoft Azure Fundamentals exam. Upon completion of the Specialization, you will be offered a discount to the Microsoft Azure Fundamentals Certification Exam to be redeemed at Pearson Vue, Microsoft's proctor exam site. Limited discount vouchers are available on first-come-first-serve basis. Coursera and Microsoft may end the offer at any time. 

Applied Learning Project

Learners will engage in interactive exercises throughout this program that offers opportunities to practice and implement what they are learning. They use the Microsoft Learn Sandbox. This a free environment that allows learners to explore Microsoft Azure and get hands-on with live Microsoft Azure resources and services.

For example, when they learn about creating a SQL database, they will work in a temporary Azure environment called the Sandbox. The beauty about this is that you will be working with real technology but in a controlled environment, which allows you to apply what you learn, and at your own pace.

You will need a Microsoft account to sign into the Sandbox. If you don't have one, you can create one for free. The Learn Sandbox allows free, fixed-time access to a cloud subscription with no credit card required. Learners can safely explore, create, and manage resources without the fear of incurring costs or "breaking production".

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Azure Data Lake Storage Gen2 and Data Streaming Solution

 

What you'll learn

How to use Azure Data Lake Storage to make processing Big Data analytical solutions more efficient. 

How to set up a stream analytics job to stream data and manage a running job

How to describe the concepts of event processing and streaming data and how this applies to Azure Stream Analytics 

How to use Advanced Threat Protection to proactively monitor your system and describe the various ways to upload data to Data Lake Storage Gen 2

Join Free: Azure Data Lake Storage Gen2 and Data Streaming Solution

There are 4 modules in this course

In this course, you will see how Azure Data Lake Storage can make processing Big Data analytical solutions more efficient and how easy it is to set up. You will also explore how it fits into common architectures, as well as the different methods of uploading the data to the data store. You will examine the myriad of security features that will ensure your data is secure. Learn the concepts of event processing and streaming data and how this applies to Azure Stream Analytics. You will then set up a stream analytics job to stream data, and learn how to manage and monitor a running job.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the ninth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Prepare for DP-203: Data Engineering on Microsoft Azure Exam

 

What you'll learn

How to refresh and test your knowledge of the skills mapped to all the main topics covered in the DP-203 exam.

How to demonstrate proficiency in the skills measured in Exam DP-203: Data Engineering on Microsoft Azure

How to outline the key points covered in the Microsoft Data Engineer Associate Specialization

How to describe best practices for preparing for the Exam DP-203: Data Engineering on Microsoft Azure

Join Free: Prepare for DP-203: Data Engineering on Microsoft Azure Exam

There are 3 modules in this course

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the DP-203 Microsoft Azure Data Fundamentals certification exam. 

You will refresh your knowledge of how to use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. You will test your knowledge in a practice exam​ mapped to all the main topics covered in the DP-203 exam, ensuring you’re well prepared for certification success. 

You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the DP-203 proctored exam. By the end of this course, you will be ready to sign-up for and take the DP-203 exam.​

This is the last course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Prepare for DP-100: Data Science on Microsoft Azure Exam

 

What you'll learn

Outline the key points covered in the Data Science on Microsoft Azure Exam course

Describe best practices for preparing for the Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Demonstrate proficiency in the skills measured in the DP-100: Designing and Implementing a Data Science Solution on Azure

Join Free: Prepare for DP-100: Data Science on Microsoft Azure Exam

There are 6 modules in this course

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the DP-100 Azure Data Scientist Associate certification exam. 

You will refresh your knowledge of how to plan and create a suitable working environment for data science workloads on Azure, run data experiments, and train predictive models. In addition, you will recap on how to manage, optimize, and deploy machine learning models into production.

You will test your knowledge in a practice exam​ mapped to all the main topics covered in the DP-100 exam, ensuring you’re well prepared for certification success.

You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the DP-100 proctored exam. By the end of this course, you will be ready to sign-up for and take the DP-100 exam.​

This is the fifth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azure certification exam.

The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. 

This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

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Microsoft Azure Databricks for Data Engineering

 

What you'll learn

How to work with large amounts of data from multiple sources in different raw formats

How to create production workloads on Azure Databricks with Azure Data Factory

How to build and query a Delta Lake 

How to perform data transformations in DataFrame. How to understand the architecture of an Azure Databricks Spark Cluster and Spark Jobs 

Join Free: Microsoft Azure Databricks for Data Engineering

There are 9 modules in this course

In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

You will discover the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. You will come to understand the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark. You will also be introduced to the architecture of an Azure Databricks Spark Cluster and Spark Jobs. You will work with large amounts of data from multiple sources in different raw formats.  you will learn how Azure Databricks supports day-to-day data-handling functions, such as reads, writes, and queries.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the eighth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Data Integration with Microsoft Azure Data Factory

 

What you'll learn

How to create and manage data pipelines in the cloud 

How to integrate data at scale with Azure Synapse Pipeline and Azure Data Factory

Join Free: Data Integration with Microsoft Azure Data Factory

There are 8 modules in this course

In this course, you will learn how to create and manage data pipelines in the cloud using Azure Data Factory.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services. It is ideal for anyone interested in preparing for the DP-203: Data Engineering on Microsoft Azure exam (beta). 

This is the third course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Data Storage in Microsoft Azure

 

What you'll learn

You will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for your data.

Design and implement data storage and data security

Design and develop data processing

Monitor and optimize data storage and data processing

Join Free: Data Storage in Microsoft Azure

There are 5 modules in this course

Azure provides a variety of ways to store data: unstructured, archival, relational, and more. In this course, you will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud.

This course part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). 

This is the second in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Microsoft Azure Data Engineering Associate (DP-203) Professional Certificate

 

Advance your career with in-demand skills

Receive professional-level training from Microsoft

Demonstrate your technical proficiency

Earn an employer-recognized certificate from Microsoft

Prepare for an industry certification exam

Join Free: Microsoft Azure Data Engineering Associate (DP-203) Professional Certificate

Professional Certificate - 10 course series

This Professional Certificate is intended for data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure. 

This Professional Certificate will help you develop expertise in designing and implementing data solutions that use Microsoft Azure data services. You will learn how to integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. 

This program consists of 10 courses to help prepare you to take Exam DP-203: Data Engineering on Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Professional Certificate, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure.

Applied Learning Project

Learners will engage in interactive exercises throughout this program that offers opportunities to practice and implement what they are learning. They use the Microsoft Learn Sandbox. This is a free environment that allows learners to explore Microsoft Azure and get hands-on with live Microsoft Azure resources and services.

For example, when you learn about integrating, transforming, and consolidating data; you will work in a temporary Azure environment called the Sandbox or directly in the Azure Portal. The beauty about this is that you will be working with real technology but in a controlled environment, which allows you to apply what you learn, and at your own pace.

You will need a Microsoft account. If you don't have one, you can create one for free. The Learn Sandbox allows free, fixed-time access to a cloud subscription with no credit card required. Learners can safely explore, create, and manage resources without the fear of incurring costs or "breaking production".

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Data Engineering with MS Azure Synapse Apache Spark Pools

 

What you'll learn

How to perform data engineering with Azure Synapse Apache Spark Pools to boost the performance of big-data analytic applications

How to ingest data using Apache Spark Notebooks in Azure Synapse Analytics

How to transform data using DataFrames in Apache Spark Pools in Azure Synapse Analytics

How to monitor and manage data engineering workloads with Apache Spark in Azure Synapse Analytics

Join Free: Data Engineering with MS Azure Synapse Apache Spark Pools

There are 3 modules in this course

In this course, you will learn how to perform data engineering with Azure Synapse Apache Spark Pools, which enable you to boost the performance of big-data analytic applications by in-memory cluster computing.

You will learn how to differentiate between Apache Spark, Azure Databricks, HDInsight, and SQL Pools and understand the use-cases of data-engineering with Apache Spark in Azure Synapse Analytics. You will also learn how to ingest data using Apache Spark Notebooks in Azure Synapse Analytics and transform data using DataFrames in Apache Spark Pools in Azure Synapse Analytics. You will integrate SQL and Apache Spark pools in Azure Synapse Analytics. You will also learn how to monitor and manage data engineering workloads with Apache Spark in Azure Synapse Analytics.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the sixth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Create Machine Learning Models in Microsoft Azure

 

What you'll learn

How to plan and create a working environment for data science workloads on Azure 

How to run data experiments and train predictive models

Join Free: Create Machine Learning Models in Microsoft Azure

There are 3 modules in this course

Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models.

This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure. You will learn how to run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production.

From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy -to-digest conceptual content and interactive Jupyter notebooks.

If you already have some idea what machine learning is about or you have a strong mathematical background this course is perfect for you. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks. It's also a good place to start if you plan to move beyond classic machine learning and get an education in deep learning and neural networks, which we only introduce here.

This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at cloud scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure . Each course teaches you the concepts and skills that are measured by the exam.

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Python Coding challenge - Day 118 | What is the output of the following Python Code?

 

Code:

def test(i, j):

    if i == 0:

        return j

    else:

        return test(i - 1, i + j)

print(test(2, 5))

Solution and Explanation : 

Here's what happens when you call test(2, 5):

i is initially 2, and since it's not 0, the else branch is executed.

It calls test(i - 1, i + j), where i - 1 is 1 and i + j is 7 (2 + 5).

In the new call, i is now 1, and again, the else branch is executed. It calls test(i - 1, i + j) again.

In the next call, i becomes 0. Now, the condition if i == 0 is true, and it returns j which is 1 + 7 = 8.

So, the final result of test(2, 5) is 8. 

The function essentially calculates the sum of consecutive numbers starting from j and going down to 1, based on the value of i.

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Random Models, Nested and Split-plot Designs

 

What you'll learn

Design and analyze experiments where some of the factors are random

Design and analyze experiments where there are nested factors or hard-to-change factors

Analyze experiments with covariates

Design and analyze experiments with nonnormal response distributions

Join Free: Random Models, Nested and Split-plot Designs

There are 3 modules in this course

Many experiments involve factors whose levels are chosen at random. A well-know situation is the study of measurement systems to determine their capability.  This course presents the design and analysis of these types of experiments, including modern methods for estimating the components of variability in these systems. The course also covers experiments with nested factors, and experiments with hard-to-change factors that require split-plot designs. We also provide an overview of designs for experiments with response distributions from nonnormal response distributions and experiments with covariates.

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Response Surfaces, Mixtures, and Model Building

 

What you'll learn

Conduct experiments w/computer models and understand how least squares regression is used to build an empirical model from experimental design data

Understand the response surface methodology strategy to conduct experiments where system optimization is the objective

Recognize how the response surface approach can be used for experiments where the factors are the components of a mixture

Recognize where the objective of the experiment is to minimize the variability transmitted into the response from uncontrollable factors

Join Free: Response Surfaces, Mixtures, and Model Building

There are 4 modules in this course

Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response.  This course provides design and optimization tools to answer that questions using the response surface framework.  Other related topics include design and analysis of computer experiments, experiments with mixtures, and experimental strategies to reduce the effect of uncontrollable factors on unwanted variability in the response.

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Factorial and Fractional Factorial Designs

 

What you'll learn

Conduct a factorial experiment in blocks and construct and analyze a fractional factorial design

Apply the factorial concept to experiments with several factors

Use the analysis of variance for factorial designs

Use the 2^k system of factorial designs

Join Free: Factorial and Fractional Factorial Designs

There are 4 modules in this course

Many experiments in engineering, science and business involve several factors.  This course is an introduction to these types of multifactor experiments.  The appropriate experimental strategy for these situations is based on the factorial design, a type of experiment where factors are varied together.  This course focuses on designing these types of experiments and on using the ANOVA for analyzing the resulting data.  These types of experiments often include nuisance factors, and  the blocking principle can be used in factorial designs to handle these situations.  As the number of factors of interest grows full factorials become too expensive and fractional versions of the factorial design are useful.  This course will  cover the benefits of fractional factorials, along with methods for constructing and analyzing the data from these experiments.

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Experimental Design Basics

 

What you'll learn

By the end of this course, you will be able to:

Approach complex industrial and business research problems and address them through a rigorous, statistically sound experimental strategy

Use modern software to effectively plan experiments

Analyze the resulting data of an experiment, and communicate the results effectively to decision-makers.

Join Free: Experimental Design Basics

There are 5 modules in this course

This is a basic course in designing experiments and analyzing the resulting data. The course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all aspects of today’s industrial and business environment. Applications from various fields will be illustrated throughout the course.  Computer software packages (JMP, Design-Expert, Minitab) will be used to implement the methods presented and will be illustrated extensively. 

All experiments are designed experiments; some of them are poorly designed, and others are well-designed. Well-designed experiments allow you to obtain reliable, valid results faster, easier, and with fewer resources than with poorly-designed experiments. You will learn how to plan, conduct and analyze experiments efficiently in this course.

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Design of Experiments Specialization

 

What you'll learn

Plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain valid objective conclusions.

Use response surface methods for system optimization as a follow-up to successful screening.

Use experimental design tools for computer experiments, both deterministic and stochastic computer models.

Use software tools to create custom designs based on optimal design methodology for situations where standard designs are not easily applicable.

Join Free: Design of Experiments Specialization

Specialization - 4 course series

Learn modern experimental strategy, including factorial and fractional factorial experimental designs, designs for screening many factors, designs for optimization experiments, and designs for complex experiments such as those with hard-to-change factors and unusual responses. There is thorough coverage of modern data analysis techniques for experimental design, including software.  Applications include electronics and semiconductors, automotive and aerospace, chemical and process industries, pharmaceutical and bio-pharm, medical devices, and many others.

You can see an overview of the specialization from Dr. Montgomery here.

Applied Learning Project

Participants will complete a project that is typically based around their own work environment, and can use this to effectively demonstrate the application of experimental design methodology. The structure of the course and the step-by-stem process taught in the course is designed to ensure participant success.

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Learn to Program: The Fundamentals

 

There are 7 modules in this course

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

Join free : Learn to Program: The Fundamentals

Skills you'll gain

• Python Syntax And Semantics

• Computer Programming

• Python Programming

• Idle (Python)

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Cheat sheet for Python list

List Basics:

Creating a List:

my_list = [1, 2, 3, 'four', 5.0]

Accessing Elements:

first_element = my_list[0]

last_element = my_list[-1]

Slicing:

sliced_list = my_list[1:4]  # Returns elements at index 1, 2, and 3

List Operations:

Appending and Extending:

my_list.append(6)        # Adds 6 to the end

my_list.extend([7, 8])   # Extends with elements 7 and 8

Inserting at a Specific Position:

my_list.insert(2, 'inserted')  # Inserts 'inserted' at index 2

Removing Elements:

my_list.remove('four')  # Removes the first occurrence of 'four'

popped_element = my_list.pop(2)  # Removes and returns element at index 2

Sorting:

my_list.sort()   # Sorts the list in ascending order

my_list.reverse()  # Reverses the order of elements

List Functions:

Length and Count:

length = len(my_list)          # Returns the number of elements

count_of_element = my_list.count(2)  # Returns the count of occurrences of 2

Index and In:

index_of_element = my_list.index('four')  # Returns the index of 'four'

is_present = 5 in my_list       # Returns True if 5 is in the list, False otherwise

List Comprehensions:

Creating a New List:

squared_numbers = [x**2 for x in range(5)]

Conditional List Comprehension:

even_numbers = [x for x in range(10) if x % 2 == 0]

Miscellaneous:

Copying a List:

copied_list = my_list.copy()

Clearing a List:

my_list.clear()  # Removes all elements from the list

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Python Coding challenge - Day 117 | What is the output of the following Python Code?

 

The above code uses the symmetric difference operator (^) between two sets. The symmetric difference of two sets is the set of elements that are in either of the sets, but not in both.

Here's the output of the given code:

set1 = {1, 1, 2}

set2 = {2, 3, 4}

result = set1 ^ set2

print(result)

Output:

{1, 3, 4}

In the result set, you can see that it contains elements 1, 3, and 4, which are present in either set1 or set2 but not in both. Additionally, duplicate elements are automatically removed in a set, so even though set1 contains two occurrences of the element 1, it appears only once in the result set.

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Python Quick Interview Guide: Top Expert-Led Coding Interview Question Bank for Python Aspirants

 

Quick solutions to frequently asked algorithm and data structure questions.

Key Features

● Learn how to crack the Data structure and Algorithms Code test using the top 75 questions/solutions discussed in the book.

● Refresher on Python data structures and writing clean, actionable python codes.

● Simplified solutions on translating business problems into executable programs and applications.

Description

Python is the most popular programming language, and hence, there is a huge demand for Python programmers. Even if you have learnt Python or have done projects on AI, you cannot enter the top companies unless you have cleared the Algorithms and data Structure coding test.

This book presents 75 most frequently asked coding questions by top companies of the world. It not only focuses on the solution strategy, but also provides you with the working code. This book will equip you with the skills required for developing and analyzing algorithms for various situations. This book teaches you how to measure Time Complexity, it then provides solutions to questions on the Linked list, Stack, Hash table, and Math. Then you can review questions and solutions based on graph theory and application techniques. Towards the end, you will come across coding questions on advanced topics such as Backtracking, Greedy, Divide and Conquer, and Dynamic Programming.

After reading this book, you will successfully pass the python interview with high confidence and passion for exploring python in future.

What you will learn

● Design an efficient algorithm to solve the problem.

● Learn to use python tricks to make your program competitive.

● Learn to understand and measure time and space complexity.

● Get solutions to questions based on Searching, Sorting, Graphs, DFS, BFS, Backtracking, Dynamic programming.

Who this book is for

This book will help professionals and beginners clear the Data structures and Algorithms coding test. Basic knowledge of Python and Data Structures is a must.

Table of Contents

1. Lists, binary search and strings

2. Linked lists and stacks

3. Hash table and maths

4. Trees and graphs

5. Depth first search

6. Breadth first search

7. Backtracking

8. Greedy and divide and conquer algorithms

9. Dynamic programming

About the Author

Professor Shyamkant Limaye spent 18 years in the computer industry and 30 years in teaching electronics engineering students. His experience includes a two-year stint as a system analyst in the USA. In 1971, he graduated from Visvesvaraya National Institute of Technology in Electrical Engineering with a gold medal. He did masters from IIT Kanpur and Doctorate in electronics from RTM Nagpur University. He has guided ten students for PhD. He published a text book on VHDL programming in 2007. He has also published a thriller novel titled “Dual reality” in 2011. Currently, he is a Professor in the Electronics and Telecomm Department at St. Vincent Pallotti College of Engineering and Technology, Nagpur.

Hard Copy : Python Quick Interview Guide: Top Expert-Led Coding Interview Question Bank for Python Aspirants (English Edition)

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Jai Shree Ram using Python Code

from turtle import *

title('Jai Shree Ram')

bgcolor('black')

pensize(5)

pencolor("ORANGE")

Ram_naam = ["जय श्री राम"] * 50

angle = 360 / 50

penup()

sety(-1)

for _ in range(51):

    left(angle)

    forward(260)

    if Ram_naam:

        write(Ram_naam.pop(), align="right", font=('Arial', 12, "bold"))

    backward(260)

penup()    

goto(-40, -20)

pendown()

write("|| जय श्री राम ||", font=("Arial", 50, "normal"), align="center")

hideturtle()

done()

#clcoding.com

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Top 20 Python Dictionary Questions with answer

 

Question 1:

What is a dictionary in Python?

a) A collection of ordered elements

b) A collection of unordered elements

c) A single element

d) A data type

Question 2:

How do you create an empty dictionary in Python?

a) empty_dict = {}

b) empty_dict = dict()

c) empty_dict = new dict()

d) Both a and b

Question 3:

How do you access the value associated with a specific key in a dictionary?

a) dictionary.value(key)

b) dictionary[key]

c) dictionary.get(key)

d) dictionary.retrieve(key)

Question 4:

What is the purpose of the len() function when used with a dictionary?

a) It returns the total number of key-value pairs in the dictionary

b) It returns the last key in the dictionary

c) It returns the length of each value in the dictionary

d) It returns the sum of all values in the dictionary

Question 5:

How do you add a new key-value pair to a dictionary?

a) dictionary.add(key, value)

b) dictionary[key] = value

c) dictionary.insert(key, value)

d) dictionary.append(key, value)

Question 6:

What is the key difference between a dictionary and a list in Python?

a) Dictionaries are ordered, while lists are unordered

b) Dictionaries are mutable, while lists are immutable

c) Dictionaries can contain only numeric elements

d) Dictionaries are unordered and do not allow duplicate keys

Question 7:

How do you check if a key is present in a dictionary?

a) key in dictionary

b) dictionary.contains(key)

c) dictionary.exists(key)

d) key.exists(dictionary)

Question 8:

What does the dictionary.keys() method return?

a) The values of the dictionary

b) The keys of the dictionary

c) The key-value pairs of the dictionary

d) The length of the dictionary

Question 9:

How do you remove a key-value pair from a dictionary?

a) dictionary.remove(key)

b) dictionary.discard(key)

c) dictionary.delete(key)

d) All of the above

Question 10:

Which method is used to retrieve the value associated with a key, and if the key is not present, it returns a default value?

a) dictionary.get(key, default)

b) dictionary.retrieve(key, default)

c) dictionary.value(key, default)

d) dictionary.fetch(key, default)

Question 11:

What is the purpose of the pop() method in Python dictionaries?

a) Adds an element to the dictionary

b) Removes the last element from the dictionary and returns its value

c) Removes the first occurrence of the specified element

d) Removes the key-value pair for a specified key

Question 12:

How do you update the value associated with a key in a dictionary?

a) dictionary.update(key, new_value)

b) dictionary[key] = new_value

c) dictionary.modify(key, new_value)

d) dictionary.change_value(key, new_value)

Question 13:

What is the output of the following code?

my_dict = {"a": 1, "b": 2, "c": 3}

del my_dict["b"]

print(my_dict)

a) {"a": 1, "b": 2, "c": 3}

b) {"a": 1, "c": 3}

c) {"a": 1, "b": 2}

d) Raises an error

Question 14:

How do you iterate over the keys of a dictionary?

a) for key in dictionary.keys():

b) for key in dictionary:

c) for key in dictionary.values():

d) for key in dictionary.items():

Question 15:

What is the purpose of the values() method in Python dictionaries?

a) Returns the keys of the dictionary

b) Returns the values of the dictionary

c) Returns the key-value pairs of the dictionary

d) Returns the length of the dictionary

Question 16:

Which method is used to clear all key-value pairs from a dictionary?

a) dictionary.clear()

b) dictionary.remove_all()

c) dictionary.delete()

d) dictionary.empty()

Question 17:

What is the purpose of the items() method in Python dictionaries?

a) Returns the keys of the dictionary

b) Returns the values of the dictionary

c) Returns the key-value pairs of the dictionary

d) Returns the length of the dictionary

Question 18:

What is the output of the following code?

my_dict = {"apple": 3, "banana": 5, "cherry": 2}

sorted_dict = dict(sorted(my_dict.items()))

print(sorted_dict)

a) {"apple": 3, "banana": 5, "cherry": 2}

b) {"cherry": 2, "apple": 3, "banana": 5}

c) {"banana": 5, "apple": 3, "cherry": 2}

d) Raises an error

Question 19:

How do you create a dictionary with keys as numbers from 1 to 5 and values as their squares?

a) squares = {i: i ** 2 for i in range(1, 6)}

b) squares = {i: i * i for i in range(1, 6)}

c) squares = {i: i ** 2 for i in [1, 2, 3, 4, 5]}

d) All of the above

Question 20:

What is the purpose of the copy() method in Python dictionaries?

a) Creates a shallow copy of the dictionary

b) Creates a deep copy of the dictionary

c) Returns the reversed dictionary

d) Appends a copy of the dictionary to itself

Answer Key:

1. b) A collection of unordered elements
2. d) Both a and b
3. b) dictionary[key]
4. a) It returns the total number of key-value pairs in the dictionary
5. b) dictionary[key] = value
6. d) Dictionaries are unordered and do not allow duplicate keys
7. a) key in dictionary
8. b) The keys of the dictionary
9. d) All of the above
10. a) dictionary.get(key, default)
11. b) Removes the last element from the dictionary and returns its value
12. b) dictionary[key] = new_value
13. b) {"a": 1, "c": 3}
14. b) for key in dictionary:
15. b) Returns the values of the dictionary
16. a) dictionary.clear()
17. c) Returns the key-value pairs of the dictionary
18. b) {"cherry": 2, "apple": 3, "banana": 5}
19. a) squares = {i: i ** 2 for i in range(1, 6)}
20. a) Creates a shallow copy of the dictionary

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Top 20 Python Set Questions with answer

Question 1:

What is a set in Python?

a) A collection of ordered elements

b) A collection of unordered elements

c) A single element

d) A data type

Question 2:

How do you create an empty set in Python?

a) set()

b) empty_set = {}

c) empty_set = set()

d) Both b and c

Question 3:

How do you add an element to a set in Python?

a) set.insert(element)

b) set.add(element)

c) set.append(element)

d) set.include(element)

Question 4:

What is the key difference between a set and a list in Python?

a) Sets are ordered, while lists are unordered

b) Sets are mutable, while lists are immutable

c) Sets can contain only numeric elements

d) Sets are unordered and do not allow duplicate elements

Question 5:

How do you check if an element is present in a set?

a) element in set

b) set.contains(element)

c) set.exists(element)

d) element.exists(set)

Question 6:

What happens when you try to add a duplicate element to a set?

a) The element is added successfully

b) Python raises an exception

c) The duplicate element is ignored, and the set remains unchanged

d) The set is automatically sorted

Question 7:

How do you remove an element from a set?

a) set.remove(element)

b) set.delete(element)

c) set.pop(element)

d) set.discard(element)

Question 8:

What is the purpose of the len() function when used with a set?

a) It returns the total number of elements in the set

b) It returns the last element of the set

c) It returns the length of each element in the set

d) It returns the sum of all elements in the set

Question 9:

How do you create a set with elements from 1 to 5 in Python?

a) set = {1, 2, 3, 4, 5}

b) set = range(1, 6)

c) set = set(1, 6)

d) set = {range(1, 6)}

Question 10:

What is the purpose of the pop() method in Python sets?

a) Removes the last element from the set

b) Removes a random element from the set and returns it

c) Removes the first occurrence of the specified element

d) Sorts the elements of the set

Question 11:

What is the difference between a set and a frozenset in Python?

a) Sets are mutable, while frozensets are immutable

b) Sets are unordered, while frozensets are ordered

c) Sets can contain only numeric elements

d) Sets allow duplicate elements, while frozensets do not

Question 12:

Which of the following statements is true regarding the union of two sets?

a) The union operator for sets is +

b) The union of two sets is the intersection of their elements

c) The union of two sets contains all unique elements from both sets

d) The union of two sets results in an empty set

Question 13:

What is the purpose of the clear() method in Python sets?

a) Clears all elements from the set

b) Returns a clear copy of the set

c) Clears only the first element from the set

d) Clears the set if a specific element is provided

Question 14:

Which method is used to find the intersection of two sets in Python?

a) set.intersection(set2)

b) set.intersect(set2)

c) set.common(set2)

d) set.and(set2)

Question 15:

What is the output of the following code?

set1 = {1, 2, 3}

set2 = {3, 4, 5}

result = set1.union(set2)

print(result)

a) {1, 2, 3, 4, 5}

b) {1, 2, 3}

c) {3, 4, 5}

d) {1, 2, 4, 5}

Question 16:

How do you check if a set is a subset of another set?

a) set.is_subset(other_set)

b) set.subset_of(other_set)

c) set.issubset(other_set)

d) set.contains_subset(other_set)

Question 17:

What does the difference() method do when applied to two sets?

a) Returns the union of the two sets

b) Returns the intersection of the two sets

c) Returns the difference between the two sets

d) Returns the symmetric difference between the two sets

Question 18:

What is the purpose of the symmetric_difference() method in Python sets?

a) Returns the union of the two sets

b) Returns the intersection of the two sets

c) Returns the difference between the two sets

d) Returns the symmetric difference between the two sets

Question 19:

What is the output of the following code?

set1 = {1, 2, 3}

set2 = {3, 4, 5}

result = set1.difference(set2)

print(result)

a) {1, 2, 3, 4, 5}

b) {1, 2}

c) {3}

d) {4, 5}

Question 20:

What is the purpose of the issuperset() method in Python sets?

a) Checks if the set is a proper superset of another set

b) Checks if the set is a subset of another set

c) Checks if the set is equal to another set

d) Checks if the set contains all elements of another set

Answer Key:

1. b) A collection of unordered elements
2. c) empty_set = set()
3. b) set.add(element)
4. d) Sets are unordered and do not allow duplicate elements
5. a) element in set
6. c) The duplicate element is ignored, and the set remains unchanged
7. a) set.remove(element)
8. a) It returns the total number of elements in the set
9. a) set = {1, 2, 3, 4, 5}
10. b) Removes a random element from the set and returns it
11. a) Sets are mutable, while frozensets are immutable
12. c) The union of two sets contains all unique elements from both sets
13. a) Clears all elements from the set
14. a) set.intersection(set2)
15. a) {1, 2, 3, 4, 5}
16. c) set.issubset(other_set)
17. d) Returns the symmetric difference between the two sets
18. d) Returns the symmetric difference between the two sets
19. b) {1, 2}
20. a) Checks if the set is a proper superset of another set

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