Learn Python Programming: An in-depth introduction to the fundamentals of Python, 3rd Edition

 

Get up and running with Python 3.9 through concise tutorials and practical projects in this fully updated third edition.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Extensively revised with richer examples, Python 3.9 syntax, and new chapters on APIs and packaging and distributing Python code

Discover how to think like a Python programmer

Learn the fundamentals of Python through real-world projects in API development, GUI programming, and data science

Book Description

Learn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries.

This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter.

The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book.

Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned.

What you will learn

Get Python up and running on Windows, Mac, and Linux

Write elegant, reusable, and efficient code in any situation

Avoid common pitfalls like duplication, complicated design, and over-engineering

Understand when to use the functional or object-oriented approach to programming

Build a simple API with FastAPI and program GUI applications with Tkinter

Get an initial overview of more complex topics such as data persistence and cryptography

Fetch, clean, and manipulate data, making efficient use of Python’s built-in data structures

Who this book is for

This book is for everyone who wants to learn Python from scratch, as well as experienced programmers looking for a reference book. Prior knowledge of basic programming concepts will help you follow along, but it’s not a prerequisite.

Table of Contents

A Gentle Introduction to Python

Built-In Data Types

Conditionals and Iteration

Functions, the Building Blocks of Code

Comprehensions and Generators

OOP, Decorators, and Iterators

Exceptions and Context Managers

Files and Data Persistence

Cryptography and Tokens

Testing

Debugging and Profiling

GUIs and Scripting

Data Science in Brief

Introduction to API Development

Packaging Python Applications

Hard Copy : Learn Python Programming: An in-depth introduction to the fundamentals of Python, 3rd Edition

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Python Object-Oriented Programming: Build robust and maintainable object-oriented Python applications and libraries, 4th Edition

 

A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniques

Key Features

Build an intuitive understanding of object-oriented design, from introductory to mature programs

Learn the ins and outs of Python syntax, libraries, and best practices

Examine a machine-learning case study at the end of each chapter

Book Description

Object-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning.

Steven and Dusty provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python's classes and data structures to facilitate good design. In addition, the book also features an in-depth look at Python's exception handling and how functional programming intersects with OOP. Two very powerful automated testing systems, unittest and pytest, are introduced. The final chapter provides a detailed discussion of Python's concurrent programming ecosystem.

By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs.

What you will learn

Implement objects in Python by creating classes and defining methods

Extend class functionality using inheritance

Use exceptions to handle unusual situations cleanly

Understand when to use object-oriented features, and more importantly, when not to use them

Discover several widely used design patterns and how they are implemented in Python

Uncover the simplicity of unit and integration testing and understand why they are so important

Learn to statically type check your dynamic code

Understand concurrency with asyncio and how it speeds up programs

Who this book is for

If you are new to object-oriented programming techniques, or if you have basic Python skills and wish to learn how and when to correctly apply OOP principles in Python, this is the book for you. Moreover, if you are an object-oriented programmer coming from other languages or seeking a leg up in the new world of Python, you will find this book a useful introduction to Python. Minimal previous experience with Python is necessary.

Table of Contents

Object-Oriented Design

Objects in Python

When Objects Are Alike

Expecting the Unexpected

When to Use Object-Oriented Programming

Abstract Base Classes and Operator Overloading

Python Data Structures

The Intersection of Object-Oriented and Functional Programming

Strings, Serialization, and File Paths

The Iterator Pattern

Common Design Patterns

Advanced Design Patterns

Testing Object-Oriented Programs

Concurrency

Hard Copy: Python Object-Oriented Programming: Build robust and maintainable object-oriented Python applications and libraries, 4th Edition

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Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities

 

Games and activities that teach kids ages 10+ to code with Python

Learning to code isn't as hard as it sounds—you just have to get started! Coding for Kids: Python starts kids off right with 50 fun, interactive activities that teach them the basics of the Python programming language. From learning the essential building blocks of programming to creating their very own games, kids will progress through unique lessons packed with helpful examples—and a little silliness!

Kids will follow along by starting to code (and debug their code) step by step, seeing the results of their coding in real time. Activities at the end of each chapter help test their new knowledge by combining multiple concepts. For young programmers who really want to show off their creativity, there are extra tricky challenges to tackle after each chapter. All kids need to get started is a computer and this book.

This beginner's guide to Python for kids includes:

50 Innovative exercises—Coding concepts come to life with game-based exercises for creating code blocks, drawing pictures using a prewritten module, and more.

Easy-to-follow guidance—New coders will be supported by thorough instructions, sample code, and explanations of new programming terms.

Engaging visual lessons—Colorful illustrations and screenshots for reference help capture kids' interest and keep lessons clear and simple.

Encourage kids to think independently and have fun learning an amazing new skill with this coding book for kids.

Hard Copy : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities

Sprial Bound : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities [Spiral-bound] Adrienne Tacke

PDF : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities [Spiral-bound] Adrienne Tacke

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Image Processing in Python using Pillow

Image Processing in Python
#original Image

from PIL import Image

Image.open('clcodingmr.jpg')

1. Image Resizing:

from PIL import Image

def resize_image(image_path, output_path, width, height):

    image = Image.open(image_path)

    resized_image = image.resize((width, height))

    resized_image.save(output_path)

# Example usage:

resize_image('clcodingmr.jpg', 'resized_output.jpg', 300, 200)

# Now, open and show the resized image

Image.open('clcodingmr.jpg')

Image.open('resized_output.jpg')

​

2. Image Rotation with Pillow:

from PIL import Image

​

def rotate_image(image_path, output_path, angle):

    image = Image.open(image_path)

    rotated_image = image.rotate(angle)

    rotated_image.save(output_path)

​

# Example usage:

rotate_image('clcodingmr.jpg', 'rotated_output.jpg', 45)

Image.open('rotated_output.jpg')

3. Image Translation (using crop) with Pillow:

from PIL import Image

​

def translate_image(image_path, output_path, tx, ty):

    image = Image.open(image_path)

    translated_image = image.crop((tx, ty, image.width, image.height))

    translated_image.save(output_path)

​

# Example usage:

translate_image('clcodingmr.jpg', 'translated_output.jpg', 50, 30)

Image.open('translated_output.jpg')

4. Image Shearing (using affine transform) with Pillow:

Image.open('sheared_output.jpg')

from PIL import Image, ImageOps

​

def shear_image(image_path, output_path, shear_factor):

    image = Image.open(image_path)

    shear_matrix = [1, shear_factor, 0, 0, 1, 0]

    sheared_image = image.transform(image.size, Image.AFFINE, shear_matrix)

    sheared_image.save(output_path)

​

# Example usage:

shear_image('clcodingmr.jpg', 'sheared_output.jpg', 0.2)

Image.open('sheared_output.jpg')

5. Image Normalization (simple contrast adjustment) with Pillow:

from PIL import Image

​

def normalize_image(image_path, output_path):

    image = Image.open(image_path)

    normalized_image = ImageOps.autocontrast(image)

    normalized_image.save(output_path)

​

# Example usage:

normalize_image('clcodingmr.jpg', 'normalized_output.jpg')

Image.open('normalized_output.jpg')

​

6. Image Blurring (using a filter) with Pillow:

from PIL import Image, ImageFilter

​

def blur_image(image_path, output_path, radius):

    image = Image.open(image_path)

    blurred_image = image.filter(ImageFilter.GaussianBlur(radius))

    blurred_image.save(output_path)

​

# Example usage:

blur_image('clcodingmr.jpg', 'blurred_output.jpg', 5)

Image.open('blurred_output.jpg')

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Google Project Management: Professional Certificate

 

What you'll learn

Gain an immersive understanding of the practices and skills needed to succeed in an entry-level project management role

Learn how to create effective project documentation and artifacts throughout the various phases of a project

Learn the foundations of Agile project management, with a focus on implementing Scrum events, building Scrum artifacts, and understanding Scrum roles

Practice strategic communication, problem-solving, and stakeholder management through real-world scenarios

Join Free: 

Professional Certificate - 6 course series

Prepare for a new career in the high-growth field of project management, no experience required. Get professional training designed by Google and get on the fastrack to a competitively paid job.

Project managers are natural problem-solvers. They set the plan and guide teammates, and manage changes, risks, and stakeholders.

Over 6 courses, gain in-demand skills that will prepare you for an entry-level job. Learn from Google employees whose foundations in project management served as launchpads for their own careers. At under 10 hours per week, you can complete in less than six months.

This program qualifies you for over 100 hours of project management education, which helps prepare you for 

Project Management Institute

 Certifications like the globally-recognized 

Certified Associate in Project Management (CAPM)®

Join FREE : Google Project Management: Professional Certificate

Applied Learning Project

This program includes over 140 hours of instruction and hundreds of practice-based assessments which will help you simulate real-world project management scenarios that are critical for success in the workplace.

The content is highly interactive and exclusively developed by Google employees with decades of experience in program and project management.

Skills you’ll gain will include: Creating risk management plans; Understanding process improvement techniques; Managing escalations, team dynamics, and stakeholders; Creating budgets and navigating procurement; Utilizing  project management software, tools, and templates; Practicing Agile project management, with an emphasis on Scrum.

Through a mix of videos, assessments, and hands-on activities, you’ll get introduced to initiating, planning, and running both traditional and Agile projects. You’ll develop a toolbox to demonstrate your understanding of key project management elements, including managing a schedule, budget, and team.

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10 different data charts using Python

 # 10 different data charts using Python

pip install matplotlib seaborn

# 1. Line Chart:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 12, 5, 8, 3]

plt.plot(x, y)

plt.title('Line Chart')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.show()

#clcoding.com

# 2. Bar Chart:

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values = [25, 40, 30, 20]

plt.bar(categories, values)

plt.title('Bar Chart')

plt.xlabel('Categories')

plt.ylabel('Values')

plt.show()

#clcoding.com

# 3. Pie Chart:

import matplotlib.pyplot as plt

labels = ['Category A', 'Category B', 'Category C']

sizes = [30, 45, 25]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')

plt.title('Pie Chart')

plt.show()

#clcoding.com

# 4. Histogram:

import matplotlib.pyplot as plt

import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=30, edgecolor='black')

plt.title('Histogram')

plt.xlabel('Values')

plt.ylabel('Frequency')

plt.show()

#clcoding.com

# 5. Scatter Plot:

import matplotlib.pyplot as plt

import numpy as np

x = np.random.rand(50)

y = 2 * x + 1 + 0.1 * np.random.randn(50)

plt.scatter(x, y)

plt.title('Scatter Plot')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.show()

#clcoding.com

# 6. Box Plot:

import seaborn as sns

import numpy as np

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

sns.boxplot(data=data)

plt.title('Box Plot')

plt.xlabel('Category')

plt.ylabel('Values')

plt.show()

#clcoding.com

# 7. Violin Plot:

import seaborn as sns

import numpy as np

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

sns.violinplot(data=data)

plt.title('Violin Plot')

plt.xlabel('Category')

plt.ylabel('Values')

plt.show()

#clcoding.com

# 8. Heatmap:

import seaborn as sns

import numpy as np

data = np.random.rand(10, 10)

sns.heatmap(data, annot=True)

plt.title('Heatmap')

plt.show()

#clcoding.com

# 9. Area Chart:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y1 = [10, 15, 25, 30, 35]

y2 = [5, 10, 20, 25, 30]

plt.fill_between(x, y1, y2, color='skyblue', alpha=0.4)

plt.title('Area Chart')

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.show()

#clcoding.com

# 10. Radar Chart:

import matplotlib.pyplot as plt

import numpy as np

labels = np.array([' A', ' B', ' C', ' D', ' E'])

data = np.array([4, 5, 3, 4, 2])

angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False)

data = np.concatenate((data, [data[0]]))

angles = np.concatenate((angles, [angles[0]]))

plt.polar(angles, data, marker='o')

plt.fill(angles, data, alpha=0.25)

plt.title('Radar Chart')

plt.show()

#clcoding.com

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Build Your Own Programming Language: A programmer's guide to designing compilers, DSLs and interpreters for solving modern computing problems, 2nd Edition

 

Written by the creator of the Unicon programming language, this book will show you how to implement programming languages to reduce the time and cost of creating applications for new or specialized areas of computing.

Key Features

• Solve pain points in your application domain by building a custom programming language
• Learn how to create parsers, code generators, semantic analyzers, and interpreters
• Target bytecode, native code, and preprocess or transpile code into another high level language

Book Description

The need for different types of computer languages is growing, as is the need for domain-specific languages. Building your own programming language has its advantages, as it can be your antidote to the ever-increasing complexity of software.

In this book, you'll start with implementing the frontend of a compiler for your language, including a lexical analyzer and parser, including the handling of parse errors. The book then covers a series of traversals of syntax trees, culminating with code generation for a bytecode virtual machine or native code. You’ll also manage data structures and output code when writing a preprocessor or a transpiler.

Moving ahead, you'll learn how domain-specific language features are often best represented by operators and functions that are built into the language, rather than library functions. We'll conclude with how to implement garbage collection. Throughout the book, Dr. Jeffery weaves in his experience from building the Unicon programming language to give better context to the concepts. Relevant examples are provided in Unicorn and Java so that you can follow the code of your choice. In this edition, code examples have been extended and further tested.

By the end of this book, you'll be able to build and deploy your own domain-specific languages, capable of compiling and running programs.

What you will learn

• Perform requirements analysis for the new language and design language syntax and semantics
• Write lexical and context-free grammar rules for common expressions and control structures
• Develop a scanner that reads source code and generate a parser that checks syntax
• Build key data structures in a compiler and use your compiler to build a syntax-coloring code editor
• Write tree traversals that insert information into the syntax tree
• Implement a bytecode interpreter and run bytecode generated by your compiler
• Write native code and run it after assembling and linking using system tools
• Preprocess and transpile code from your language into another high level language
• Implement garbage collection in your language

Who This Book Is For

This book is for software developers interested in the idea of inventing their own language or developing a domain-specific language. Computer science students taking compiler construction courses will also find this book highly useful as a practical guide to language implementation to supplement more theoretical textbooks. We assume most readers will have intermediate or better proficiency in a high level programming language such as Java or C++.

Table of Contents

1. Why Build Another Programming Language?
2. Programming Language Design
3. Scanning Source Code
4. Parsing
5. Syntax Trees
6. Symbol Tables
7. Checking Base Types
8. Checking Types on Function Calls and Structure Accesses
9. Intermediate Code Generation
10. Syntax Coloring in an IDE
11. Preprocessors and Transpilers
12. Bytecode Interpreters
13. Generating Bytecode
14. Native Code Generation
15. Built in Operators and Functions
16. Control Structures   

Hard Copy : Build Your Own Programming Language: A programmer's guide to designing compilers, DSLs and interpreters for solving modern computing problems, 2nd Edition

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How much do you know about functional programming in Python?

 

a. lambda function cannot be used with reduce( ) function.

Answer

False

b. lambda, map( ), filter( ), reduce( ) can be combined in one single

expression.

Answer

True

c. Though functions can be assigned to variables, they cannot be called

using these variables.

Program

False

d. Functions can be passed as arguments to function and returned from

function.

Program

True

e. Functions can be built at execution time, the way lists, tuples, etc. can

be.

Program

True

f. Lambda functions are always nameless.

Program

True

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DevOps on AWS: Release and Deploy

 

Build your subject-matter expertise

This course is part of the DevOps on AWS 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: DevOps on AWS: Release and Deploy

There are 2 modules in this course

AWS provides a set of flexible services designed to enable companies to more rapidly and reliably build and deliver products using AWS and DevOps practices. These services simplify provisioning and managing infrastructure, deploying application code, automating software release processes, and monitoring your application and infrastructure performance. 

The third course in the series explains how to improve the deployment process with DevOps methodology, and also some tools that might make deployments easier, such as Infrastructure as Code, or IaC, and AWS CodeDeploy.

The course begins with reviewing topics covered in the first course of the DevOps on AWS series. You will learn about the differences between continuous integration, continuous delivery, and continuous deployment. In Exercises 1 and 2, you will set up AWS CodeDeploy and make revisions that will then be deployed. If you use AWS Lambda, you will explore ways to address additional considerations when you deploy updates to your Lambda functions.

Next, you will explore how infrastructure as code (IaC) helps organizations achieve automation, and which AWS solutions provide a DevOps-focused way of creating and maintaining infrastructure. In Exercise 3, you will be provided with an AWS CloudFormation template that will set up backend services, such as AWS CodePipeline, AWS CodeCommit, AWS CodeDeploy, and AWS CodeBuild. You will then upload new revisions to the pipeline.

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DevOps on AWS: Operate and Monitor

 

Build your subject-matter expertise

This course is part of the DevOps on AWS 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: DevOps on AWS: Operate and Monitor

There are 2 modules in this course

The third and the final course in the DevOps series will teach how to use AWS Services to control the architecture in order to reach a better operational state. Monitoring and Operation are key aspects for both the release pipeline and production environments, because they provide instruments that help discover what's happening, as well as do modifications and enhancements on infrastructure that is currently running. 

This course teaches how to use Amazon CloudWatch for monitoring, as well as Amazon EventBridge and AWS Config for continuous compliance. It also covers Amazon CloudTrail and a little bit of Machine Learning for Monitoring operations!

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Exam Prep: AWS Certified Cloud Practitioner Foundations

 

What you'll learn

The four domains - Cloud Concepts, Security and Compliance, Technology and Billing and Pricing - for the AWS Certified Cloud Practitioner exam

Certification exam-level practice questions written by experts from AWS

Simulations designed to solidify understanding of cloud concepts you need to know for the exam

Join Free: Exam Prep: AWS Certified Cloud Practitioner Foundations

There are 4 modules in this course

This new foundational-level course from Amazon Web Services (AWS), is designed to help you to assess your preparedness for the AWS Certified Cloud Practitioner certification exam.  You will learn how to prepare for the exam by exploring the exam’s topic areas and how they map to both AWS Cloud practitioner roles and to specific areas of study. You will review sample certification questions in each domain, practice skills with hands-on exercises, test your knowledge with practice question sets, and learn strategies for identifying incorrect responses by interpreting the concepts that are being tested in the exam. At the end of this course you will have all the knowledge and tools to help you identity your strengths and weaknesses in each certification domain areas that are being tested on the certification exam. 

The AWS Certified Cloud Foundations Certification the AWS Certified Cloud Practitioner (CLF-C01) exam is intended for individuals who can effectively demonstrate an overall knowledge of the AWS Cloud independent of a specific job role. The exam validates a candidate’s ability to complete the following tasks: Explain the value of the AWS Cloud, Understand and explain the AWS shared responsibility model, understand security best practices, Understand AWS Cloud costs, economics, and billing practices, Describe and position the core AWS services, including compute, network, databases, and storage and identify AWS services for common use cases

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AWS Cloud Practitioner Essentials

 

What you'll learn

Understand the working definition of the AWS Cloud

Differentiate between on-premises, hybrid-cloud, and all-in cloud

Describe the basic global infrastructure of the AWS Cloud

Explain the benefits of the AWS Cloud

Join Free: AWS Cloud Practitioner Essentials

There are 7 modules in this course

Welcome to AWS Cloud Practitioner Essentials. If you’re new to the cloud, whether you’re in a technical or non-technical role such as finance, legal, sales, marketing, this course will provide you with an understanding of fundamental AWS Cloud concepts to help you gain confidence to contribute to your organization’s cloud initiatives. This course is also the starting point to prepare for your AWS Certified Cloud Practitioner certification whenever it’s convenient for you.

After you complete the course, you’ll understand the benefits of the AWS Cloud and the basics of its global infrastructure. You’ll be able to describe and provide an example of the core AWS services, including compute, network, databases, and storage. For the finance-minded, you’ll be able to articulate the financial benefits of the AWS Cloud, define core billing and pricing models, and learn how to use pricing tools to make cost-effective choices for AWS services.

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Migrating to the AWS Cloud

 

Build your subject-matter expertise

This course is part of the AWS Fundamentals 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: Migrating to the AWS Cloud

There are 4 modules in this course

This introductory course is for anyone who wants a deeper dive into AWS migration. Whether you want to understand what services are helpful, need to plan a migration for your organization, or are helping other groups with their own migration, you will find valuable information throughout this course. The course sessions structure cloud migration through the three-phase migration process from AWS: assess, mobilize, and migrate and modernize. This process is designed to help your organization approach and implement a migration of tens, hundreds, or thousands of applications. By learning about this three-phase structure—and the various AWS tools, features, and services that can help you during each phase—you will complete this course with a better understanding of how to design and implement migrations to AWS.

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Architecting Solutions on AWS

 

Build your subject-matter expertise

This course is available as part of 

When you enroll in this course, you'll also be asked to select a specific program.

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: Architecting Solutions on AWS

There are 4 modules in this course

Are you looking to get more technical? Are you looking to begin working in the cloud, but don’t know where to go next? Are you looking to up your game by prepping for the AWS Solutions Architect Associate Exam? Do you see yourself as a cloud consultant, but can’t quite envision how your days would be? Are you puzzled how to match a customer’s requirements with the right AWS services/solutions? If so, you are in the right place!! You’ll learn how to plan, think, and act like a Solution Architect in a real-life customer scenario.

In this course, you’ll get prepared to begin your career architecting solutions on AWS. Through a series of use case scenarios and practical learning, you’ll learn to identify services and features to build resilient, secure, and highly available IT solutions in the AWS Cloud. Each week, a fictional customer will present a different need. We will then review the options, choose the best one for the use case and walk you through the architecture design on a whiteboard. You’ll learn about event-driven architectures with a focus on performance efficiency and cost. You’ll then gain knowledge on how to architect a solution using many purpose-built AWS services. With this understanding, you’ll get a sense of hybrid architectures with a refined focus on reliability and operational efficiency. Finally, you’ll wrap up your learning by understanding a multi-account strategy centered on security and cost.

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AWS Cloud Solutions Architect Professional Certificate

 

What you'll learn

Make informed decisions about when and how to apply key AWS Services for compute, storage, database, networking, monitoring, and security.

Design architectural solutions, whether designing for cost, performance, and/or operational excellence, to address common business challenges.

Create and operate a data lake in a secure and scalable way, ingest and organize data into the data lake, and optimize performance and costs.

Prepare for the certification exam, identify your strengths and gaps for each domain area, and build strategies for identifying incorrect responses.

Join Free: AWS Cloud Solutions Architect Professional Certificate

Professional Certificate - 4 course series

This professional certificate provides the knowledge and skills you need to start building your career in cloud architecture and helps you prepare for the AWS Certified Solutions Architect - Associate exam. You will start by learning key AWS Services for compute, storage, database, networking, monitoring, and security, then dive into how to design architectural solutions, how to create and operate a data lake, and how to prepare for the certification exam.

The AWS Certified Solutions Architect – Associate certification showcases knowledge and skills in AWS technology across a wide range of AWS services. The certification focuses on the design of cost and performance optimized solutions and demonstrating a strong understanding of the AWS Well-Architected Framework. This AWS Certification is one of the top-paying IT certifications, per the 

 SkillSoft IT Skills and Salary report

. Per

 Enterprise Strategy Group

, surveyed AWS Certification holders credited their certification for their higher earnings (74%), increased confidence (87%), and increased influence among coworkers (79%).

To prepare for your AWS Certification exam, we recommend that — in addition to attaining this professional certificate — candidates review the free exam guide, sample questions, and AWS technical documentation (e.g. white papers and product FAQs) on the

 AWS Certified Solutions Architect - Associate exam page

 to understand what content and services are covered by the exam.

Applied Learning Project

Through 15 hands-on labs, you’ll use the AWS Management Console to apply skills learned in the videos. 

For example: 

In Architecting Solutions on AWS, you’ll use Amazon API Gateway, AWS Lambda, Amazon SQS, Amazon DynamoDB, and Amazon SNS to build a serverless web backend.

In Introduction to Designing Data Lakes, you’ll use Amazon S3, Amazon OpenSearch Service, AWS Lambda and Amazon API Gateway to create an Amazon OpenSearch Service Cluster. You’ll also use Amazon S3, Amazon EC2, Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, Amazon Elasticsearch Service to create a data ingestion pipeline with the use of high-scale AWS Managed services. 

In Cloud Technical Essentials, you’ll design a 3-tier architecture using services like Amazon VPC, Amazon EC2, Amazon RDS with high availability and Elastic Load Balancing following AWS best practices. You’ll upload an architecture diagram laying out your design including the networking layer.

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AWS Cloud Technical Essentials

 

What you'll learn

Describe terminology and concepts related to AWS services     

Articulate key concepts of AWS security measures and AWS Identity and Access Management (IAM)    

You will learn to distinguish among several AWS compute services, including Amazon EC2, AWS Lambda, and Amazon ECS.  

Understand AWS database and storage offerings, including Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon S3.

Join Free: AWS Cloud Technical Essentials

There are 4 modules in this course

Are you in a technical role and want to learn the fundamentals of AWS? Do you aspire to have a job or career as a cloud developer, architect, or in an operations role? If so, AWS Cloud Technical Essentials is an ideal way to start. This course was designed for those at the beginning of their cloud-learning journey - no prior knowledge of cloud computing or AWS products and services required!

Throughout the course, students will build highly available, scalable, and cost effective application step-by-step. Upon course completion, you will be able to make an informed decision about when and how to apply core AWS services for compute, storage, and database to different use cases. You’ll also learn about cloud security with a review of AWS' shared responsibility model and an introduction to AWS Identity and Access Management (IAM). And, you’ll know how AWS services can be used to monitor and optimize infrastructure in the cloud.

AWS Cloud Technical Essentials is a fundamental-level course and will build your competence, confidence, and credibility with practical cloud skills that help you innovate and advance your professional future. Enroll in AWS Cloud Technical Essentials and start learning the technical fundamentals of AWS today!

Note: This course was designed for learners with a technical background. If you are new to the cloud or come from a business background, we recommend completing AWS Cloud Practitioner Essentials (https://www.coursera.org/learn/aws-cloud-practitioner-essentials) before enrolling in this course.

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Serverless Architectures on AWS

 

Build your subject-matter expertise

This course is part of the Developing Applications on AWS 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: Serverless Architectures on AWS

There are 2 modules in this course

A modern software engineer knows how to use the benefits of managed services from Amazon Web Services to reduce the coding needed to get a project across the line. There’s a lot of code you really don’t need to write when you can use a managed service for your applications. Less code means less tests, less bugs, and quicker delivery. 

In this course, we get hands on with automation tools and serverless managed services. Get your projects completed faster without needing to maintain the underlying servers hosting the managed services. Treat your infrastructure as code using AWS CloudFormation and AWS Serverless Application Model as an automated way to build the resources hosting your applications. We use AWS Amplify to rapidly add front-end hosting and AWS Cognito to add authentication to our application. With Cognito in place, we upgrade the application API to require authentication. Next, we learn to use AWS Step Functions to move a lot of the workflow coordination code out of your applications. Using serverless services, we contrast some options for building event driven architectures with Amazon SNS, Amazon SQS and Amazon EventBridge. Join our expert instructors as we dive deep on real-world use cases for each of the featured services in the course. 

This course will provide a combination of video-based lectures, demonstrations and hands-on lab exercises that will get you working with automation tools, Cognito authentication, Step Function workflows and event-driven architectures.

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AWS Fundamentals 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 Amazon Web Services

Join Free: AWS Fundamentals Specialization

Specialization - 3 course series

This specialization gives current or aspiring IT professionals an overview of the features, benefits, and capabilities of Amazon Web Services (AWS). As you proceed through these four interconnected courses, you will gain a more vivid understanding of core AWS services, key AWS security concepts, strategies for migrating from on-premises to AWS, and basics of building serverless applications with AWS. Additionally, you will have opportunities to practice what you have learned by completing labs and exercises developed by AWS technical instructors.

Applied Learning Project

This specialization gives current or aspiring IT professionals an overview of the features, benefits, and capabilities of Amazon Web Services (AWS). As you proceed through these four interconnected courses, you will gain a more vivid understanding of core AWS services, key AWS security concepts, strategies for migrating from on-premises to AWS, and basics of building serverless applications with AWS. Additionally, you will have opportunities to practice what you have learned by completing labs and exercises developed by AWS technical instructors.

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Introduction to Machine Learning on AWS

 

What you'll learn

Differentiate between artificial intelligence (AI), machine learning, and deep learning. 

Select the appropriate AWS machine learning service for a given use case.

Discover how to build, train, and deploy machine learning models.

Join Free: Introduction to Machine Learning on AWS

There are 2 modules in this course

In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We'll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You'll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.

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Learn SQL Basics for Data Science Specialization

 

What you'll learn

Use SQL commands to filter, sort, & summarize data; manipulate strings, dates, & numerical data from different sources for analysis

Assess and create datasets to solve your business questions and problems using SQL

Use the collaborative Databricks workspace and create an end-to-end pipeline that reads data, transforms it, and saves the result

Develop a project proposal & select your data, perform statistical analysis & develop metrics, and present your findings & make recommendations

Join Free: Learn SQL Basics for Data Science Specialization

Specialization - 4 course series

This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets. 

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