# Happy New Year 2024

Free Code:

from colorama import Fore
def heart_shape(msg=" Happy New Year 2024"):
lines = []
for y in range(15, -15, -1):
line = ""
for x in range(-30, 30):
f = ((x * 0.05) ** 2 + (y * 0.1) ** 2 - 1) ** 3 - (x * 0.05) ** 2 * (y * 0.1) ** 3
line += msg[(x - y) % len(msg)] if f <= 0 else " "
lines.append(line)
print(Fore.BLUE+"\n".join(lines))
heart_shape()  # Call the function to create the heart
#clcoding.com

# Python Coding challenge - Day 105 | What is the output of the following Python Code?

The given code creates a set s using a set comprehension to check if each element in the list lst is even (True) or not (False). The condition n % 2 == 0 is used to determine if an element is even. Here's the code and its output:

lst = [2, 7, 8, 6, 5, 5, 4, 4, 8]
s = {True if n % 2 == 0 else False for n in lst}
print(s)

Output:

{False, True}

In this case, the set s contains both True and False because there are even and odd numbers in the list. The set comprehension creates a set of unique values based on the condition specified.

# Using comprehension how will you convert

Using comprehension how will you convert

{'a' : 1, 'b' : 2, 'c' : 3, 'd' : 4, 'e' : 5}

into

{'A' : 100, 'B' : 200, 'C' : 300, 'D' : 400, 'E' : 500}?

### Output

d = {'a' : 1, 'b' : 2, 'c' : 3, 'd' : 4, 'e' : 5}

d = {key.upper( ) : value * 100 for (key, value) in d.items( )}

print(d)

### Another Method :

original_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}

converted_dict = {key.upper(): value * 100 for key, value in original_dict.items()}

print(converted_dict)

The upper() method is used to convert the keys to uppercase, and the values are multiplied by 100. The resulting dictionary is {'A': 100, 'B': 200, 'C': 300, 'D': 400, 'E': 500}.

# Happy New Year 2024 using Python

Code:

import time

import random

import pyfiglet as pf

from pyfiglet import Figlet

from termcolor import colored

text = "Happy New Year 2024"

color_list = ['red', 'green', 'blue', 'yellow']

data_list = []

with open('texts.txt') as f:

data_list = [line.strip() for line in f]

happy_new_year_art = pf.figlet_format(text)

for i in range(0, 1):

if i % 2 == 0:

f = Figlet(font=random.choice(data_list))

text_art = colored(f.renderText(text), random.choice(color_list))

else:

text_art = happy_new_year_art

print("\n", text_art)

#clcoding.com

# Python Coding challenge - Day 104 | What is the output of the following Python Code?

print(max(min(False, False), 1, True))

1

Explanation:

The min function returns the smallest value among its arguments. Since False is considered 0 in Python, the min of False and False is still False. Then, the max function returns the largest value among False, 1, and True. Since True is equivalent to 1 in Python and 1 is greater than 0, the max function returns 1.

# Python Coding challenge - Day 103 | What is the output of the following Python Code?

### Code :

print(min(max(False,-3,-4), 2,7))

Let's break down the expression step by step:

max(False, -3, -4): This evaluates to False because False is treated as 0 in numerical comparisons, and 0 is not greater than -3 or -4.

min(False, 2, 7): This evaluates to False because False is treated as 0 in numerical comparisons, and 0 is the minimum among False, 2, and 7.

So, the output of the expression will be False.

# Artificial Intelligence on Microsoft Azure

## What you'll learn

How to identify guiding principles for responsible AI

How to identify features of common AI workloads

## Join Free:Artificial Intelligence on Microsoft Azure

### There is 1 module in this course

Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.

In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI. You’ll see some of the ways that AI can be used and explore the principles of responsible AI that can help you understand some of the challenges facing developers as they try to create ethical AI solutions.

This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the first course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains.  This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them.

This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.  To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology,  general technology concepts, including concepts of machine learning and artificial intelligence.

# AI, Business & the Future of Work

## What you'll learn

How AI can give you better information upon which to make better decisions.

How AI will impact your industry so that you can avoid the pitfalls and seize the benefits.

## There are 4 modules in this course

This course from Lunds university will help you understand and use AI so that you can transform your organisation to be more efficient, more sustainable and thus innovative.

The lives of people all over the world are increasingly enhanced and shaped by artificial intelligence. To organisations there are tremendous opportunities, but also risks, so where do you start to plan for AI, business and the future of work?

Whether you are in the public or private sector, in a large organisation or a small shop, AI has a growing impact on your business. Most organisations don’t have a strategy in place for how to make AI work for them.

The teacher, Anamaria Dutceac Segesten, will guide you through the topics with short lectures, interviews and interactive exercises meant to get you thinking about your own context.

12 industry professionals, AI experts and thought leaders from different industries have been interviewed and will complement the short lectures to give you a broad overview of perspectives on the topics. You will meet:

Kerstin Enflo
Professor in Economic History
Lund University

Dr. Irene Ek
Founder
Digital Institute

Samuel Engblom
Policy Director
The Swedish Confederation of Professional Employees

Pelle Kimvall
AFRY X

Joakim Wernberg
Research Director, Digitalisation and Tech Policy
Swedish Entrepreneurship Forum

Marcus Henriksson
Empathic

Johan GrundstrÃ¶m Eriksson
Board Advisor, Innovation Management & Corporate Governance
Founder & Chairman, aiRikr Innovation AB

Jakob Svensson
Professor in Media and Communication Studies
MalmÃ¶ University

Ulrik Franke
Senior Researcher
RISE Research Institutes of Sweden

BjÃ¶rn Lorentzon
Sympa

Anna FellÃ¤nder
Founder
AI Sustainability Center

Prof. Fredrik Heintz
Associate Professor of Computer Science

# AI Product Management Specialization

## What you'll learn

Identify when and how machine learning can applied to solve problems

Apply human-centered design practices to design AI product experiences that protect privacy and meet ethical standards

Lead machine learning projects using the data science process and best practices from industry

## Specialization - 3 course series

Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems.  This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.

This Specialization provides a foundational understanding of how machine learning works and when and how it can be applied to solve problems.  Learners will build skills in applying the data science process and industry best practices to lead machine learning projects, and develop competency in designing human-centered AI products which ensure privacy and ethical standards. The courses in this Specialization focus on the intuition behind these technologies, with no programming required, and merge theory with practical information including best practices from industry.  Professionals and aspiring professionals from a diverse range of industries and functions, including product managers and product owners, engineering team leaders, executives, analysts and others will find this program valuable.

Applied Learning Project

Learners will implement three projects throughout the course of this Specialization:

1) In Course 1, you will complete a hands-on project where you will create a machine learning model to solve a simple problem (no coding necessary) and assess your model's performance.

2) In Course 2, you will identify and frame a problem of interest, design a machine learning system which can help solve it, and begin the development of a project plan.

3) In Course 3, you will perform a basic user experience design exercise for your ML-based solution and analyze the relevant ethical and privacy considerations of the project.

# AI Foundations for Everyone Specialization

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 IBM

## Specialization - 4 course series

Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance.

This specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills. It is designed to give you a firm understanding of what is AI, its applications and use cases across various industries. You will become acquainted with terms like Machine Learning, Deep Learning and Neural Networks.

Furthermore, it will familiarize you with IBM Watson AI services that enable any business to quickly and easily employ pre-built AI smarts to their products and solutions. You will also learn about creating intelligent virtual assistants and how they can be leveraged in different scenarios.

By the end of this specialization, learners will have had hands-on interactions with several AI environments and applications, and have built and deployed an AI enabled chatbot on a website – without any coding.

### Applied Learning Project

Learners will perform several no-code hands-on exercises in each of the  three courses. At the end of the last course, learners would have developed,  tested, and deployed a Watson AI powered customer service chatbot on a website to delight their clients.

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 University of Pennsylvania

## Specialization - 4 course series

This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business.

By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business.

For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor

### Applied Learning Project

Each course module in this Specialization culminates in an assessment, with two courses including peer-review exercises. These assessments are designed to check learners' knowledge and to provide an opportunity for learners to apply course concepts such as data analytics, machine learning tools, and people management best practices with AI algorithms.

The assessments will be cumulative and cover the application of artificial intelligence, ethical governance rules, Big Data management, the customer journey, fraud prevention, and personalization technology in order to develop and implement a successful AI strategy for your business.

# Python Coding challenge - Day 102 | What is the output of the following Python Code?

### Code :

my_tuple = (5, 12, 19, 3, 25)

tup = my_tuple[-2::-2]

print(tup)

#### Let's break down the code:

Sure! The output of the code is:

(3, 12)

Explanation:

You created a tuple named my_tuple with five elements: 5, 12, 19, 3, and 25.

Then, you created another tuple named tup by slicing my_tuple from the second-last element (-2) to the first element (-1) with a step size of -2 (meaning you iterate from the second-last element to the first element, reversing the order every two elements).

Finally, you printed the tup tuple, which contains the elements extracted from my_tuple: (3, 12).

# Python Coding challenge - Day 101| What is the output of the following Python Code?

### Code :

numbers = [12, 5, 8, 13, 4]
Num = numbers[::2]
print(Num)

### Solution and Explanation:

The expression numbers[::2] is a slicing operation with the following parameters:

The starting index is omitted, so it starts from the beginning of the list.
The stopping index is omitted, so it goes until the end of the list.
The step is 2, which means it selects every second element.
So, numbers[::2] will select elements from the original list with a step of 2. Let's break it down:

Element at index 0: 12
Element at index 2: 8
Element at index 4: 4
Therefore, the result will be a new list containing the elements [12, 8, 4]. When you print the result, you'll get:

[12, 8, 4]

# Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate

## What you'll learn

Manage Azure resources for machine learning

Run experiments and train models

Deploy and operationalize ethical machine learning solutions

## Join Free:Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate

### Professional Certificate - 5 course series

This Professional Certificate 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. This Professional Certificate teaches learners how to create end-to-end solutions in Microsoft Azure. They 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.

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 Professional Certificate 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.

By the end of this program, you will be ready to take the DP-100: Designing and Implementing a Data Science Solution on 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 will work directly in the Azure Portal and 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 training a deep neural network; you 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. 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".

# AI for Medicine Specialization

## What you'll learn

Diagnose diseases from x-rays and 3D MRI brain images

Predict patient survival rates more accurately using tree-based models

Estimate treatment effects on patients using data from randomized trials

Automate the task of labeling medical datasets using natural language processing

## Join Free:AI for Medicine Specialization

### Specialization - 3 course series

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases.  If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the
Deep Learning Specialization

Applied Learning Project

Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You’ll start by learning the nuances of working with 2D and 3D medical image data. You’ll then apply tree-based models to improve patient survival estimates. You’ll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you’ll explore how natural language extraction can more efficiently label medical datasets.

# Data Privacy Fundamentals

## What you'll learn

Identify foundational understanding of digital age privacy concepts and theories

Identify privacy implications of modern digital technology

Identify the rules and frameworks for data privacy in the age of technology

## There are 3 modules in this course

This course is designed to introduce data privacy to a wide audience and help each participant see how data privacy has evolved as a compelling concern to public and private organizations as well as individuals. In this course, you will hear from legal and technical experts and practitioners who encounter data privacy issues daily. This course will review theories of data privacy as well as data privacy in the context of social media and artificial intelligence. It will also explore data privacy issues in journalism, surveillance, new technologies like facial recognition and biometrics. Completion of the course will enable the participant to be eligible for CPE credit.

# Industrial IoT Markets and Security

## What you'll learn

What Industry 4.0 is and what factors have enabled the IIoT.

Key skills to develop to be employed in the IIoT space.

What platforms are, and also market information on Software and Services.

What the top application areas are (examples include manufacturing and oil & gas).

## Join Free:Industrial IoT Markets and Security

### There are 5 modules in this course

This course can also be taken for academic credit as ECEA 5385, part of CU Boulder’s Master of Science in Electrical Engineering degree.

Developing tomorrow's industrial infrastructure is a significant challenge. This course goes beyond the hype of consumer IoT to emphasize a much greater space for potential embedded system applications and growth: The Industrial Internet of Things (IIoT), also known as Industry 4.0. Cisco’s CEO stated: “IoT overall is a \$19 Trillion market. IIoT is a significant subset including digital oilfield, advanced manufacturing, power grid automation, and smart cities”.

This is part 1 of the specialization. The primary objective of this specialization is to closely examine emerging markets, technology trends, applications and skills required by engineering students, or working engineers, exploring career opportunities in the IIoT space. The structure of the course is intentionally wide and shallow: We will cover many topics, but will not go extremely deep into any one topic area, thereby providing a broad overview of the immense landscape of IIoT. There is one exception: We will study security in some depth as this is the most important topic for all "Internet of Things" product development.

In this course students will learn :
* What Industry 4.0 is and what factors have enabled the IIoT
* Key skills to develop to be employed in the IIoT space
* What platforms are, and also market information on Software and Services
* What the top application areas are (examples include manufacturing and oil & gas)
* What the top operating systems are that are used in IIoT deployments
* About networking and wireless communication protocols used in IIoT deployments
* About computer security; encryption techniques and secure methods for insuring data integrity and authentication

# Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

## What you'll learn

Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and more

Plan ML: Determine the way machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there

Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues

Lead ML: Manage a machine learning project, from the generation of predictive models to their launch

## Join Free:Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

### There are 4 modules in this course

Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate.

But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This means that two different species must cooperate in harmony: the business leader and the quant.

This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment.

Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within - and successfully produces value for - business operations. If you're more a quant than a business leader, you'll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don't usually go there. But know this: The soft skills are often the hard ones.

After this course, you will be able to:

- Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more.

- Plan ML: Determine the way in which machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there.

- Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues.

- Lead ML: Manage a machine learning project, from the generation of predictive models to their launch.

- Prep data for ML: Oversee the data preparation, which is directly informed by business priorities.

- Evaluate ML: Report on the performance of predictive models in business terms, such as profit and ROI.

- Regulate ML: Manage ethical pitfalls, such as when predictive models reveal sensitive information about individuals, including whether they're pregnant, will quit their job, or may be arrested - aka AI ethics.

NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike by contextualizing the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact. There are no exercises involving coding or the use of machine learning software.

WHO IT'S FOR. This concentrated entry-level program is for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll do so in the role of enterprise leader or quant. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants - as well as data scientists.

LIKE A UNIVERSITY COURSE. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course.

IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel - a winner of teaching awards when he was a professor at Columbia University - this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.

VENDOR-NEUTRAL. This specialization includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.

PREREQUISITES. Before this course, learners should take the first of this specialization's three courses, "The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats."

# How much do you know about Python Comprehension?

a. Tuple comprehension offers a fast and compact way to generate a tuple.

True

b. List comprehension and dictionary comprehension can be nested.

True

c. A list being used in a list comprehension cannot be modified when it is

being iterated.

True

d. Sets being immutable cannot be used in comprehension.

False

e. Comprehensions can be used to create a list, set or a dictionary.

True

# Data Science Methodology

### What you'll learn

Describe what a data science methodology is and why data scientists need a methodology.

Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.

Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.

Determine appropriate data sources for your data science analysis methodology.

## There are 4 modules in this course

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems.

Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.

# Communicating Data Science Results

This course is part of the Data Science at Scale Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

## Join Free:Communicating Data Science Results

### There are 3 modules in this course

Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to \$50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit.

While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so.

Making predictions is not enough!  Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions.  Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex.  In this course you will learn to recognize, design, and use effective visualizations.

Just because you can make a prediction and convince others to act on it doesn’t mean you should.  In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice.   You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists.  You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both.

Learning Goals: After completing this course, you will be able to:
1. Design and critique visualizations
2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science
3. Use cloud computing to analyze large datasets in a reproducible way.

# Excel to MySQL: Analytic Techniques for Business Specialization

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 Duke University

## Join Free:Excel to MySQL: Analytic Techniques for Business Specialization

### Specialization - 5 course series

Formulate data questions, explore and visualize large datasets, and inform strategic decisions.
In this Specialization, you’ll learn to frame business challenges as data questions. You’ll use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. In the final Capstone Project, you’ll apply your skills to explore and justify improvements to a real-world business process.

The Capstone Project focuses on optimizing revenues from residential property, and Airbnb, our Capstone’s official Sponsor, provided input on the project design. Airbnb is the world’s largest marketplace connecting property-owner hosts with travelers to facilitate short-term rental transactions. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion.

# Increasing Real Estate Management Profits: Harnessing Data Analytics

This course is part of the Excel to MySQL: Analytic Techniques for Business Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

## Join Free:Increasing Real Estate Management Profits: Harnessing Data Analytics

### There are 7 modules in this course

In this final course you will complete a Capstone Project using data analysis to recommend a method for improving profits for your company, Watershed Property Management, Inc. Watershed is responsible for managing thousands of residential rental properties throughout the United States. Your job is to persuade Watershed’s management team to pursue a new strategy for managing its properties that will increase their profits. To do this, you will: (1) Elicit information about important variables relevant to your analysis; (2) Draw upon your new MySQL database skills to extract relevant data from a real estate database; (3) Implement data analysis in Excel to identify the best opportunities for Watershed to increase revenue and maximize profits, while managing any new risks; (4) Create a Tableau dashboard to show Watershed executives the results of a sensitivity analysis; and (5) Articulate a significant and innovative business process change for Watershed based on your data analysis, that you will recommend to company executives.

Airbnb, our Capstone’s official Sponsor, provided input on the project design. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion.

# Excel Skills for Data Analytics and Visualization Specialization

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 Macquarie University

## Join Free:Excel Skills for Data Analytics and Visualization Specialization

#### Specialization - 3 course series

As data becomes the modern currency, so the ability to quickly and accurately analyse data has become of paramount importance. Therefore, data analytics and visualization are two of the most sought after skills for high paying jobs with strong future growth prospects. According to an
IBM report
, the Excel tools for data analytics and visualization are among the top 10 competencies projected to show double-digit growth in their demand. This course will help you develop your analytical and visualization skills so that you not only improve your current work performance but also expand your future job prospects. For those in business and data analysis who want to master advanced Excel and beginner Power BI

Upon completing this specialization, you will be able to bring data to life using advanced Excel functions, creative visualizations, and powerful automation features. These courses will equip you with a comprehensive set of tools for transforming, linking, and analysing data. You will master a broad range of charts and create stunning interactive dashboards. Finally, you will explore a new dimension in Excel with PowerPivot, Get and Transform, and DAX.  Harnessing the power of an underlying database engine, we will remove the 1,048,576 row limitation, completely automate data transformation, create data models to effectively link data, and open the gateway to Power Business Intelligence.

Applied Learning Project

Working with datasets similar to those typically found in a business, you will use powerful Excel tools to wrangle the data into shape, create useful visualizations, and prepare dashboards and report to share your results. You will learn to create a data workflow to automate your analysis and make the results flexible and reproducible.

# Python Coding challenge - Day 100 | What is the output of the following Python Code?

## Code :

def fun(a, *args, s = '!') :

print(a, s)

for i in args :

print(i, s)

fun(100)

### Solution and Explanation:

Function Definition:

def fun(a, *args, s = '!') :

def fun(..): Defines a function named fun.
a: A required positional argument.
*args: A special syntax to accept an arbitrary number of additional positional arguments, gathered as a tuple within the function.
s = '!': An optional keyword argument with a default value of '!'.
Function Body:

print(a, s)
for i in args :
print(i, s)

print(a, s): Prints the value of a followed by the value of s.
for i in args :: Iterates through each argument in the args tuple.
print(i, s): Prints each argument from args followed by the value of s.
Function Call:

fun(100)

Calls the fun function with a single argument, 100.

Output:
100 !

Explanation:

The function call fun(100) assigns 100 to a.
Since no additional positional arguments are provided, args remains an empty tuple.
The first print statement outputs "100 !".
The for loop doesn't execute because args is empty.

# How much do you know about Python Dictionary? ðŸ§µ:

a. Dictionary elements can be accessed using position-based index.

False

b. Dictionaries are immutable.

False

c. Insertion order is preserved by a dictionary.

False

d. The very first key - value pair in a dictionary d can be accessed using the

expression d[0].

False

e. courses.clear( ) will delete the dictionary object called courses.

False

f. It is possible to nest dictionaries.

True

g. It is possible to hold multiple values against a key in a dictionary

True

# Create font art using python - Merry Christmas

Free Code :

from colorama import Fore

import pyfiglet

font = pyfiglet.figlet_format('Merry Christmas ')

print(Fore.GREEN+font)

#clcoding.com

### Let's break down the code step by step:

Importing Libraries:

from colorama import Fore

import pyfiglet

The colorama library is used for adding color to the output text in the console.

The pyfiglet library is used for creating ASCII art text.

Creating ASCII Art:

font = pyfiglet.figlet_format('Merry Christmas ')

The pyfiglet.figlet_format function is used to convert the text "Merry Christmas" into ASCII art format using a specific font. In this case, it uses the default font.

Printing in Green:

print(Fore.GREEN + font)

Fore.GREEN sets the text color to green using Colorama.

font contains the ASCII art text generated by PyFiglet.

The print statement then outputs the combined result, which is the ASCII art text in green.

To run this code, you'll need to have the colorama and pyfiglet libraries installed. You can install them using the following commands:

pip install colorama

pip install pyfiglet

After installing the required libraries, you can run the script to see the "Merry Christmas" message in green ASCII art in your terminal.

## What you'll learn

Structure campaigns in Meta Ads Manager

This course is part of the Meta Social Media Marketing Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Meta

# Meta AR Developer Professional Certificate

## What you'll learn

Learn Meta Spark AR to optimize and test Spark AR effects, push content to Instagram using Spark AR Hub, and more.

Create a web AR application in PlayCanvas using JavaScript, and use Blender to modify 3D content.

Create AR games in Unity using C#; edit, import, and animate 3D content, including rigged animations.

Put together a job portfolio for an AR developer interview.

## Join Free:Meta AR Developer Professional Certificate

### Professional Certificate - 7 course series

Augmented Reality is projected to be a
\$88 billion
industry by 2026. This program was designed by experts at Meta and will help you master AR development skills by covering nuances of AR in marketing, web AR, and AR in games. You’ll get hands-on experience with popular tools including Unity, Spark AR, and Playcanvas, using JavaScript and C#.

This program is designed for computer science graduates, software developers, web developers, 3D artists, or game developers who want to advance their skill set for the in-demand field of AR Development.

You will learn tools and concepts like Spark AR Fundamentals, game creation in Spark AR, HTML5 WebGL, Javascript in PlayCanvas, AR with PlayCanvas, asset creation and integration in Unity, C# Basics in Unity, creation and deployment on an AR Game using Vuforia and more.

By the end of the program, you will be able to:

Describe AR’s defining characteristics, affordances and capabilities

Create content in Meta Spark and push it to Instagram using Meta Spark Hub

Create a web AR application using PlayCanvas

Write and debug simple Unity scripts

Create a Unity AR game using C# in the AR Foundation and Vuforia

Applied Learning Project

This program includes more than 100 hours of learning filled with hands-on activities that will prepare you for jobs in AR development across sectors such as marketing, education, gaming and entertainment.

Through a mix of videos, assessments, readings and hands-on projects, you’ll be introduced to the world of augmented reality development. You’ll apply your new skills to a number of projects including:

Create multiple animated AR effects using Meta Spark

Develop a web-based game in which you interact with a 3D heart—a great addition to your professional portfolio

Set up an AR environment in Unity using AR Foundation to create a modern, AR version of the classic Asteroids arcade game

Configure Unity editors, gain an understanding of the Vuforia Engine and build an AR bowling game that can be played on any plane surface

# Meta iOS Developer Professional Certificate

## What you'll learn

Gain the skills required for an entry-level career as an iOS developer.

Learn how to create applications for iOS systems and how to manage the lifecycle of a mobile app.

Learn programming fundamentals, how to create a user interface (UI) and best practices for designing the UI.

Create a portfolio with projects that show your ability to publish, deploy and maintain iOS apps as well as cross-platform apps using React Native.

## Join Free: Meta iOS Developer Professional Certificate

### Professional Certificate - 12 course series

Have you ever wanted to build outstanding mobile apps like Facebook and Instagram?

This Professional Certificate will teach you how to build applications for iOS devices and start a new career as an iOS Developer. By the end of this program, you’ll be able to create and run a mobile app powered by iOS operating systems.

In this program, you’ll learn:

Essential iOS programming concepts and the tools needed to develop applications

Create user interfaces (UIs) for mobile apps using SwiftUI

Manage the lifecycle and data collections of mobile applications

Work with web technologies and manage data on iOS applications

Build an iOS app

Create cross-platform applications using React Native

Prepare for technical interviews for iOS developer roles

Any third-party trademarks and other intellectual property (including logos and icons) referenced in the learning experience remain the property of their respective owners. Unless specifically identified as such, Coursera’s use of third-party intellectual property does not indicate any relationship, sponsorship, or endorsement between Coursera and the owners of these trademarks or other intellectual property.

Applied Learning Project

Engage in hands-on activities and learn how to implement concepts through applied learning structures.

Create a protocol in Swift

Build a UI for an application in Swift

Develop a native iOS mobile application

In the final course, you will also complete a Capstone project that will require you to utilize your new skillset by building an app. You can add this project to your portfolio and showcase your work during job interviews.

# Meta React Native Specialization

## What you'll learn

Gain the skills required to create apps across different platforms and devices.

Learn programming fundamentals, how to create a user interface (UI) and best practices for designing the UI.

Become an expert in React Native, React, JavaScript, GitHub repositories and version control.

Walk away with a project-based portfolio that demonstrates your skills to employers.

## Join Free:Meta React Native Specialization

### Specialization - 8 course series

If you want to learn how to create apps for Android and iOS devices, this course is right for you. This program is taught by industry-recognized experts at Meta.

Cross-platform mobile developers build and write code for apps that are hosted on mobile devices powered by multiple operating systems. They do everything from creating the app to debugging it after deployment. They design interactive and attractive user interfaces (UIs) to ensure the best possible end-user or customer experiences.

This specialization can be a stepping stone for building a successful career as an iOS or Android developer.

In this program, you’ll learn:

Essential cross-platform programming concepts and the tools needed to develop apps.

Work with web technologies such as HTML, CSS and JavaScript.

Manage data across multiple mobile operating systems including iOS and Android.

In-demand skills to develop, test and maintain cross-platform mobile apps using React and React Native.

GitHub repositories for version control and content management systems (CMS).

By the end, you’ll put your new skills to work by completing a real-world portfolio project. You’ll build a dynamic mobile app using a responsive design that you can showcase during a job interview. Plus, you’ll get support in your job search.

Please note that the launch date, program content, and course titles are subject to change.

Applied Learning Project

Throughout the program, you’ll engage in a number of practical lab-based exercises that offer opportunities to practice and implement what you are learning. You’ll complete hands-on projects that you can showcase during job interviews and on relevant social networks.

At the end of each course, you’ll complete an assignment to test your new skills. There are various assignments in which you’ll use a lab environment or a web application to perform tasks such as:

Use JavaScript and data from multiple sources to dynamically control a web app.

Manage a project using version control in Git and GitHub.

Build apps using React, routing, hooks, and data fetching.

At the end of the program, there will be a Capstone project where you will bring your new skillset together to create a mobile app.

# Meta Android Developer Professional Certificate

## What you'll learn

Gain the skills required for an entry-level career as an Android developer.

Learn how to create applications for Android including how to build and manage the lifecycle of a mobile app using Android Studio.

Learn coding in Kotlin and the programming fundamentals for how to create the user interface (UI) and best practices for design.

Create cross-platform mobile applications using React Native. Demonstrate your new skills by creating a job-ready portfolio you can show during interviews.

## Join Free: Meta Android Developer Professional Certificate

### Prepare for a career in Android Development

Earn an employer-recognized certificate from Meta
Qualify for in-demand job titles: Android Developer, Mobile Applications Developer, Mobile Developer

# Python Coding challenge - Day 99 | What is the output of the following Python Code?

The discard() method in Python is used to remove a specified element from a set. If the element is not present in the set, discard() does nothing and does not raise an error.

In the code

s = {1, 3, 7, 6, 5}

print(s)

The element 4 is not present in the set s, so the discard() method will do nothing. The output will be the original set:

{1, 3, 5, 6, 7}

The set s remains unchanged because the attempt to discard the non-existent element 4 has no effect.

# How much do you know about Python tuple?

num1 = num2 = (10, 20, 30, 40, 50)

print(isinstance(num1, tuple))

The above code  creates a tuple (10, 20, 30, 40, 50) and assigns it to both num1 and num2. Then, it checks if num1 is an instance of the tuple class using the isinstance() function and prints the result.

The correct output of the code will be:

True

This is because both num1 and num2 refer to the same tuple object, and since that object is indeed a tuple, the isinstance() function returns True.

num1 = num2 = (10, 20, 30, 40, 50)

print(num1 is num2)

The above code  checks if num1 and num2 refer to the same object in memory using the is keyword. Since both num1 and num2 are assigned the same tuple (10, 20, 30, 40, 50), which is an immutable object, the result will be True. Here's the correct output:

True

This is because both variables (num1 and num2) point to the same memory location where the tuple is stored.

num1 = num2 = (10, 20, 30, 40, 50)

print(num1 is not num2)

The code checks if num1 and num2 do not refer to the same object in memory using the is not comparison. Since both num1 and num2 are assigned the same tuple (10, 20, 30, 40, 50), the result will be False. Here's the correct output:

False

This is because both variables (num1 and num2) point to the same memory location where the tuple is stored, so the is not comparison returns False.

num1 = num2 = (10, 20, 30, 40, 50)

print(20 in num1)

The code checks if the value 20 is present in the tuple assigned to the variable num1. Since 20 is one of the values in the tuple (10, 20, 30, 40, 50), the result will be True. Here's the correct output:

True

The in keyword is used to check membership, and it returns True if the specified value is found in the sequence (in this case, the tuple num1).

num1 = num2 = (10, 20, 30, 40, 50)

print(30 not in num2)

The code checks if the value 30 is not present in the tuple assigned to the variable num2. Since 30 is one of the values in the tuple (10, 20, 30, 40, 50), the result will be False. Here's the correct output:

False

The not in keyword is used to check if a value is not present in a sequence. In this case, 30 is present in the tuple num2, so the expression evaluates to False.

# Python Coding challenge - Day 98 | What is the output of the following Python Code?

### Code :

s = { }
t = {1, 4, 5, 2, 3}
print(type(s), type(t))

### Solution and Explanation :

In the code snippet you provided, you have defined two different sets, s and t, and then printed their types. Let me explain the code step by step:

s = {}

Here, you have defined an empty set. However, the syntax you used ({}) actually creates an empty dictionary in Python, not an empty set. To create an empty set, you should use the set() constructor like this:

s = set()

Now, let's move to the second part of the code:

t = {1, 4, 5, 2, 3}

Here, you have defined a set t with the elements 1, 4, 5, 2, and 3.

Finally, you printed the types of s and t:

print(type(s), type(t))

This will output the types of s and t. If you correct the creation of the empty set as mentioned above, the output will be:

<class 'set'> <class 'set'>

This indicates that both s and t are of type set

# Merry Christmas using Python ðŸ§¡

Code :

from colorama import Fore

def heart_shape(msg="Merry Christmas"):

lines = []

for y in range(15, -15, -1):

line = ""

for x in range(-30, 30):

f = ((x * 0.05) ** 2 + (y * 0.1) ** 2 - 1) ** 3 - (x * 0.05) ** 2 * (y * 0.1) ** 3

line += msg[(x - y) % len(msg)] if f <= 0 else " "

lines.append(line)

print(Fore.RED+"\n".join(lines))

print(Fore.GREEN+msg)

heart_shape()  # Call the function to create the heart

#clcoding.com

### Explnation of the code in details :

This code generates a text-based heart shape using the Colorama library for colored output in the terminal. Here's a breakdown:

Imports:
from colorama import Fore
The code imports the Fore class from the Colorama library, which is used to set text color in the terminal.

Function Definition:

def heart_shape(msg="Merry Christmas"):
The code defines a function named heart_shape that takes an optional parameter msg with a default value of "Merry Christmas".

Creating the Heart Shape:

lines = []
for y in range(15, -15, -1):
line = ""
for x in range(-30, 30):
f = ((x * 0.05) ** 2 + (y * 0.1) ** 2 - 1) ** 3 - (x * 0.05) ** 2 * (y * 0.1) ** 3
line += msg[(x - y) % len(msg)] if f <= 0 else " "
lines.append(line)
The nested loops iterate over y-coordinates and x-coordinates to create a heart shape. The mathematical expression within the inner loop defines the shape. If f is less than or equal to 0, it adds a character from the message; otherwise, it adds a space.

Printing the Heart Shape in Red:

print(Fore.RED + "\n".join(lines))
The heart shape is printed in red by concatenating the lines into a single string using "\n".join(lines) and using Fore.RED from Colorama.

Printing the Message in Green:

print(Fore.GREEN + msg)
The message is printed in green using Fore.GREEN from Colorama.

Function Invocation:

heart_shape()
The function is called without passing any arguments, so it uses the default message "Merry Christmas". The heart shape and message are printed with colored text in the terminal.

Note: The comment #clcoding.com at the end is a comment and doesn't affect the code's functionality. It seems to be a reference to a website.

# How much do you know about Python?

a. Python is free to use and distribute.

True

b. Same Python program can work on different OS - microprocessor combinations.

True

c. It is possible to use C++ or Java libraries in a Python program.

True

d. In Python type of the variable is decided based on its usage.

True

e. Python cannot be used for building GUI applications.

False

f. Python supports functional, procedural, object-oriented and eventdriven programming models.

True

g. GUI applications are based on event-driven programming model.

True

h. Functional programming model consists of interaction of multiple objects.