Python Coding Challange - Question with Answer (01010725)

 

Step-by-Step Explanation:

1. x = [1, 2, 3]

   • A list x is created.

   • Lists are mutable in Python.

2. modify(x)

   • You pass the list x to the function modify.

   • The parameter a now refers to the same list as x (initially).

3. Inside the function:

   • a = a + [4, 5]

     • This creates a new list by combining a and [4, 5].

     • The result [1, 2, 3, 4, 5] is assigned to a, but this does not affect the original list x.

     • a now refers to a new list, but x still points to the original [1, 2, 3].

4. After the function call:

   • x is unchanged.

   • So print(x) outputs:

   
   [1, 2, 3]

---

 Why didn't the original list change?

Because this line:

a = a + [4, 5]

does not modify the list in-place. It creates a new list and reassigns it to a, breaking the reference to the original list x.

---

✅ To modify the original list, you'd use:

def modify(a):
    a += [4, 5]

Or:

def modify(a):
    a.append(4) 
    a.append(5)

These modify the list in-place, so x would then change to [1, 2, 3, 4, 5].

Python for Software Testing: Tools, Techniques, and Automation

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Python Coding challenge - Day 583| What is the output of the following Python Code?

 

Code Explanation:

1. Class Definition

class A:

Defines a new class A.

2. Class Variable

    x = 5

x is a class variable, meaning it's shared across all instances.

So A.x = 5.

3. Constructor Method

    def __init__(self):

        self.x = A.x + 1

__init__ is automatically called when an instance of the class is created.

Inside it:

A.x refers to the class variable, which is 5.

self.x = A.x + 1 evaluates to 6.

So, a new instance variable self.x is created and set to 6.

4. Creating an Instance

a = A()

This creates an object a of class A.

The constructor runs, setting a.x = 6.

5. Printing the Instance Variable

print(a.x)

Outputs the instance variable x of a.

Output: 6

6. Printing the Class Variable

print(A.x)

Outputs the class variable x of class A.

Nothing has changed it, so it remains 5.

Output: 5

Final Output:

6

5

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Python Coding challenge - Day 582| What is the output of the following Python Code?

 

Code Explanation:

1. Class Definition

class A:

This defines a new class named A.

Classes are blueprints for creating objects in Python.

2. Class Variable

    val = 1

This is a class variable, meaning it is shared across all instances of the class.

A.val is now 1.

3. Constructor Method

    def __init__(self):

__init__ is the constructor in Python, called automatically when an object is created.

self refers to the specific instance being created.

4. Modifying self.val

        self.val += 1

At this point, self.val does not exist yet on the instance.

So Python looks up the class variable val (which is 1) and uses that.

Then it creates an instance variable self.val, and sets it to 1 + 1 = 2.

 This line shadows the class variable by creating an instance variable of the same name.

5. Creating an Instance

a = A()

This creates an object a of class A.

It automatically calls __init__, which creates a.val and sets it to 2.

6. Printing the Instance Variable

print(a.val)

This prints the instance variable a.val, which was set to 2 in the constructor.

Output: 2

Final Output:

2

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Master Data Analytics with AWS: A Complete Learning Plan with Labs (Free Course)

 In today’s data-driven world, the ability to extract insights from raw data is a game-changer. Whether you’re a data enthusiast, analyst, or cloud developer, Amazon Web Services (AWS) offers a comprehensive learning path designed to equip you with the most in-demand data analytics skills.

Introducing the AWS Data Analytics Learning Plan (includes labs) — your roadmap to mastering modern data analytics using the AWS Cloud. This learning plan is free, hands-on, and perfect for learners at all levels.

---

What’s Included in the Learning Plan?

The AWS Data Analytics Learning Plan is a curated series of courses and hands-on labs covering all major aspects of analytics on AWS. The content is designed and delivered by AWS experts.

Key Modules:

1. Introduction to Data Analytics on AWS
   Learn the basics of data analytics and the role AWS plays in modern data pipelines.

2. Data Collection and Ingestion
   Explore services like Amazon Kinesis, AWS Glue, and Amazon MSK to ingest and prepare data in real-time or batches.

3. Data Storage and Management
   Learn how to manage structured and unstructured data using Amazon S3, Amazon Redshift, and AWS Lake Formation.

4. Data Processing
   Gain practical knowledge of data processing with AWS Glue, Amazon EMR, and AWS Lambda.

5. Data Visualization
   Use Amazon QuickSight to create compelling dashboards and visual insights from your datasets.

6. Machine Learning Integration
   Understand how to integrate data analytics with Amazon SageMaker for predictive modeling.

7. Security and Governance
   Dive into the security and compliance best practices using AWS IAM, AWS KMS, and AWS Config.

---

Why the Labs Are a Game-Changer

Theory is essential, but hands-on practice is what truly builds skills.

The labs included in this plan allow you to:

• Work in real AWS environments

• Build end-to-end pipelines

• Analyze large-scale datasets

• Apply machine learning models on real use cases

These labs simulate real-world tasks, making it ideal for beginners and professionals alike.

---

Who Should Enroll?

This learning plan is perfect for:

• Aspiring data analysts and scientists

• Cloud engineers looking to specialize in analytics

• IT professionals upgrading their skills in cloud data platforms

• Students exploring career options in data and cloud computing

Outcomes You Can Expect

By completing the AWS Data Analytics Learning Plan, you’ll:

• Gain a solid foundation in data analytics

• Learn how to build scalable data pipelines on AWS

• Be prepared for AWS Data Analytics certification

• Stand out in the job market with cloud-native analytics skills

---

Start Learning Now — It’s Free!

Don’t miss out on this opportunity to skill up in one of the fastest-growing fields. Whether you’re just getting started or sharpening existing skills, the AWS Data Analytics Learning Plan is your gateway to becoming a cloud analytics expert.

👉 Get started today for free:
🔗 Enroll now on AWS Skill Builder

For Certification: Getting Started with Data Analytics on AWS

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Introduction to Back-End Development

 

Introduction to Back-End Development

Back-end development is a critical part of web and software development that focuses on how a website or application functions behind the scenes. While front-end development handles what users see and interact with, back-end development deals with data storage, server logic, application performance, and security. In this post, we’ll explore what back-end development is, why it matters, and how you can get started with it.

What Is Back-End Development?

Back-end development refers to the server-side of a web application. It includes everything that users don’t see but that powers the application: servers, databases, application logic, and APIs. When a user interacts with a website—like logging in or submitting a form—the front end sends a request to the back end, which processes it, accesses the database if needed, and sends back a response.

Why Is Back-End Development Important?

Back-end development ensures that applications are functional, reliable, and secure. It handles user authentication, data storage, business logic, and communication between different parts of a system. Without back-end systems, even the most beautifully designed websites would be static and incapable of doing anything meaningful like saving user data or retrieving personalized content.

Responsibilities of a Back-End Developer

A back-end developer is responsible for building and maintaining the technology that powers the server, database, and application. This includes writing server-side code, managing APIs, securing the system, integrating databases, and ensuring the performance and scalability of the application. They work closely with front-end developers to ensure that the data flow between the client and server is smooth and secure.

Key Components of Back-End Development

Back-end development involves several core technologies and components. Each one plays an important role in creating a fully functional application.

Server-Side Programming Languages

Back-end logic is written in server-side programming languages. These languages allow developers to build the functionality that runs on the server.

Popular back-end languages include JavaScript (Node.js), Python, Java, PHP, Ruby, and Go. The choice of language often depends on the type of application, team preferences, and performance needs.

Databases

Databases are where an application’s data is stored and managed. Back-end developers use databases to save and retrieve information like user profiles, orders, posts, and more.

There are two main types:

Relational databases (like MySQL, PostgreSQL) use structured tables and SQL.

Non-relational databases (like MongoDB, Redis) store data more flexibly, often in JSON-like documents or key-value pairs.

APIs (Application Programming Interfaces)

APIs allow the front end and back end to communicate. They define how data is sent, received, and structured. Back-end developers design APIs that let other parts of the system (or external systems) interact with the server and database.

Most commonly, developers build RESTful APIs or use GraphQL for more dynamic data querying.

Authentication and Authorization

Authentication is the process of verifying a user’s identity (e.g., login), and authorization determines what the user is allowed to do (e.g., access admin features).

Back-end developers implement these using techniques like session tokens, JSON Web Tokens (JWT), OAuth2, and multi-factor authentication to keep applications secure.

Hosting and Deployment

Once the back-end code is ready, it needs to be hosted on a server. Hosting providers and platforms make it possible to run applications online.

Popular choices include AWS, Heroku, Render, DigitalOcean, and Google Cloud. Developers must configure servers, set up environment variables, and ensure that applications remain available and performant.

Tools and Frameworks in Back-End Development

Back-end frameworks simplify development by providing ready-made structures and libraries for common tasks.

Popular frameworks include:

• Express.js for Node.js
• Django and Flask for Python
• Spring Boot for Java
• Laravel for PHP
• Ruby on Rails for Ruby

These frameworks help speed up development and promote best practices in routing, middleware, and security.

How the Front End Connects to the Back End

The front end and back end communicate through APIs. For example, when a user submits a form, the front end sends a POST request to the back end. The back-end server processes the request, interacts with the database, and sends back a response—usually in JSON format—that the front end displays.

Best Practices in Back-End Development

To write efficient, secure, and scalable back-end code, developers follow these best practices:

• Use modular code and design patterns (like MVC)
• Validate all user inputs to prevent injection attacks
• Protect sensitive data using encryption and environment variables
• Write unit and integration tests
• Implement logging and monitoring
• Document APIs using tools like Swagger or Postman

These practices help in maintaining code quality and ensuring a smooth development experience.

How to Get Started with Back-End Development

Here’s a step-by-step approach for beginners:

• Learn the basics of a server-side language like JavaScript (Node.js) or Python.
• Understand HTTP methods (GET, POST, PUT, DELETE) and status codes.
• Get familiar with building RESTful APIs.
• Practice using a database like PostgreSQL or MongoDB.
• Create simple projects like a to-do list or blog backend.
• Learn about authentication (JWT or sessions).
• Deploy your project on a cloud platform.

With consistent practice, you’ll gain the confidence to build and manage the back ends of real-world applications.

Join Now : Introduction to Back-End Development

Final Thoughts

Back-end development is an essential part of creating modern web and mobile applications. It powers the logic, data, and operations that users rely on every day. While it may seem complex at first, learning back-end development step by step—with real projects and hands-on experience—can make it both accessible and rewarding.

Whether you aim to be a full-stack developer or specialize in back-end systems, this skill set will open doors to countless opportunities in tech.

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Introduction to Databases for Back-End Development

 

Introduction to Databases for Back-End Development

In the world of web and software development, databases play a critical role in storing and managing data. For back-end developers, understanding how databases work is essential for building efficient, secure, and scalable applications. This blog introduces the basics of databases and how they integrate into back-end systems.

What Is a Database?

A database is a structured collection of data that allows easy access, management, and updating. It acts as a digital filing system where applications can store information such as user details, transactions, or product inventories. Unlike temporary data stored in memory, databases provide long-term storage for application data.

Why Are Databases Important in Back-End Development?

In back-end development, databases are the backbone of functionality. They enable data persistence across user sessions, allow for efficient data retrieval and manipulation, and provide mechanisms for access control and data consistency. Without databases, most applications would lose all data after a session ends.

Types of Databases

There are two main categories of databases that back-end developers commonly work with: relational (SQL) and non-relational (NoSQL).

Relational Databases (SQL)

Relational databases store data in tables composed of rows and columns. They follow a fixed schema and support complex queries using SQL (Structured Query Language). Examples include MySQL, PostgreSQL, and SQLite. These are ideal for applications requiring transactional consistency and relationships between data, such as banking systems or inventory platforms.

Non-Relational Databases (NoSQL)

NoSQL databases offer more flexible data storage. They can be document-based (e.g., MongoDB), key-value pairs (e.g., Redis), wide-column stores (e.g., Cassandra), or graph-based (e.g., Neo4j). These databases excel in handling unstructured or semi-structured data and are often chosen for real-time, large-scale applications like social media platforms or recommendation engines.

Core Concepts to Understand

Tables and Schemas

In relational databases, data is stored in tables, and the schema defines the structure of these tables—including column names, data types, and constraints. Schemas ensure data follows specific rules and formats.

Primary and Foreign Keys

A primary key uniquely identifies each row in a table, while a foreign key links data between tables, maintaining referential integrity. These concepts are vital for establishing relationships in relational databases.

CRUD Operations

CRUD stands for Create, Read, Update, and Delete—the basic operations developers perform on data. Every back-end system must implement these operations to manage application data effectively.

Indexes

Indexes improve query performance by allowing the database to find rows faster. However, excessive indexing can slow down write operations, so it's important to use them judiciously.

Transactions

Transactions allow a group of operations to be executed together, ensuring that either all operations succeed or none do. This is crucial for maintaining data integrity, especially in applications involving money or inventory.

Tools and Technologies

Back-end developers interact with databases through various tools:

Database Management Systems (DBMS): Examples include MySQL, PostgreSQL, and MongoDB.

Object-Relational Mappers (ORMs): Tools like Sequelize (Node.js), SQLAlchemy (Python), and Hibernate (Java) abstract complex SQL queries.

Admin Interfaces: Tools such as phpMyAdmin, pgAdmin, and MongoDB Compass offer visual ways to manage and query databases.

How Back-End Developers Use Databases

In a typical back-end project, developers begin by designing the database schema based on application requirements. They then establish a connection to the database using drivers or ORMs and write queries to manipulate data. Throughout development, they manage migrations to version-control the database structure and implement optimizations to ensure performance and scalability.

Best Practices in Database Development

To build reliable and scalable systems, developers should:

• Normalize data to avoid duplication.
• Use parameterized queries to prevent SQL injection.
• Backup databases regularly for disaster recovery.
• Monitor performance using profiling tools.
• Secure connections and access through roles and encryption.

Following these practices ensures the database remains efficient, secure, and manageable over time.

Learning Path for Beginners

If you're just starting out, follow this roadmap:

• Learn basic SQL syntax and commands.
• Set up a relational database like PostgreSQL or MySQL.
• Explore a NoSQL option like MongoDB to understand schema-less design.
• Practice building CRUD applications.
• Study ORMs and experiment with integrating them in real projects.
• Dive deeper into advanced topics like indexing, transactions, and migrations.

Join Now : Introduction to Databases for Back-End Development

Final Thoughts

Databases are an indispensable part of back-end development. They empower applications to handle real-world data efficiently, reliably, and securely. Whether you're building a portfolio project or a large-scale system, mastering databases is key to being a competent back-end developer.

Start small, build often, and always be mindful of performance, security, and scalability.

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Python Coding challenge - Day 580| What is the output of the following Python Code?

 

Code Explanation:

1. Function Definition with Memoization

def foo(n, cache={0: 1}):

Defines a function foo that calculates factorial of a number n.

cache is a default dictionary used to store already computed values (memoization).

Initially, it contains {0: 1} because 0! = 1.

2. Check if Result Already Cached

    if n not in cache:

Checks if n's factorial is already computed and saved in the cache.

If not, we need to compute it.

3. Recursive Calculation and Caching

        cache[n] = n * foo(n - 1)

If n is not in the cache:

Recursively call foo(n - 1) to get (n - 1)!

Multiply it by n to compute n!

Save it to cache[n] so it's not recomputed in the future.

4. Return Cached Result

    return cache[n]

Whether it was just computed or already existed, return cache[n].

5. Call and Print: foo(3)

print(foo(3))

What Happens:

3 not in cache

→ compute 3 * foo(2)

2 not in cache

→ compute 2 * foo(1)

1 not in cache

→ compute 1 * foo(0)

0 is in cache → 1

foo(1) = 1 * 1 = 1 → store in cache

foo(2) = 2 * 1 = 2 → store in cache

foo(3) = 3 * 2 = 6 → store in cache

Printed Output:

6

6. Call and Print: foo(2)

print(foo(2))

What Happens:

2 is already in cache from previous call.

Just return cache[2] = 2.

Printed Output:

2

Final Output

6

2

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Python Coding challenge - Day 581| What is the output of the following Python Code?

 

Code Explanation:

1. Function Definition: mystery(n, acc=0)

def mystery(n, acc=0):

This function is recursive and calculates something (we'll reveal what it is shortly).

n: the main number we're working with.

acc: short for "accumulator", used to keep track of a running total. Default is 0.

2. Base Case: When n == 0

    if n == 0:

        return acc

If n becomes 0, the function returns the accumulated value acc.

This stops the recursion — the base case.

3. Recursive Case: Add n to acc and Recurse

    return mystery(n - 1, acc + n)

If n is not 0:

Subtract 1 from n

Add current n to acc

Call mystery() again with these new values.

4. Call the Function: print(mystery(4))

Let's trace the recursive calls:

Call n acc Computation

mystery(4) 4 0 → mystery(3, 4)

mystery(3, 4) 3 4 → mystery(2, 7)

mystery(2, 7) 2 7 → mystery(1, 9)

mystery(1, 9) 1 9 → mystery(0, 10)

mystery(0, 10) 0 10 → return 10

Final Output

print(mystery(4))  # Output: 10

Output:

10

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Python Coding Challange - Question with Answer (01300625)

 

Explanation:

1. x = 5
   → We assign the value 5 to the variable x.

2. if x > 2:
   → Since x = 5, this condition is True, so we enter the first if block.

3. Inside that block:

   
   if x < 4:
      print("Low")

   → This is False because 5 < 4 is not true. So this block is skipped.

4. Next:

   
   elif x == 5:
        print("Exact")

   → This is True because x = 5.
   → So "Exact" gets printed.

---

✅ Final Output:

Exact

Digital Image Processing using Python
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Statistics 101 – Free Beginner Course by Cognitive Class (IBM)

 

Want to build a strong foundation in statistics—the backbone of data science, machine learning, and analytics?

The Statistics 101 course from Cognitive Class, developed by IBM, is the perfect starting point. Whether you're a student, professional, or data enthusiast, this course will help you understand data at a deeper level — and best of all, it’s completely free!

---

📘 Course Overview

This self-paced, beginner-friendly course is designed to teach you the fundamentals of statistics from the ground up. No prior knowledge required!

🔹 Platform: Cognitive Class (IBM)
🔹 Level: Beginner
🔹 Duration: ~12–15 hours
🔹 Cost: 100% Free
🔹 Certificate: Yes (IBM Verified)

---

🧠 What You’ll Learn

The course offers a clear and concise introduction to key statistical concepts, using simple examples and easy-to-follow lessons.

📌 1. Introduction to Statistics

• What is statistics?

• Importance of statistics in the real world

• Populations vs. samples

📌 2. Types of Data

• Categorical vs. numerical data

• Discrete vs. continuous variables

• Levels of measurement (nominal, ordinal, interval, ratio)

📌 3. Data Summarization

• Measures of central tendency: mean, median, mode

• Measures of dispersion: range, variance, standard deviation

• Frequency tables and distributions

📌 4. Data Visualization

• Histograms, pie charts, box plots

• Interpreting visual data

• Identifying outliers

📌 5. Probability Basics

• Probability theory in simple terms

• Events, outcomes, and sample spaces

• Basic probability rules

📌 6. Introduction to Inferential Statistics

• What is inference?

• Confidence intervals

• Hypothesis testing (conceptual level)

---

📈 Why Take This Course?

✅ No prior knowledge required
✅ Perfect for data science & analytics beginners
✅ Taught by IBM experts
✅ Includes quizzes & hands-on examples
✅ Earn a free, shareable IBM certificate

---

🏅 Certificate of Completion

At the end of the course, you’ll receive a digital certificate issued by IBM — great for your resume, LinkedIn profile, or student portfolio.

---

💬 Student Feedback

“I finally understand what standard deviation really means. This course made stats simple!”

“A great crash course on the concepts every data professional should know.”

---

📌 Who Should Enroll?

• Students new to data science, business, or research

• Professionals looking to refresh core statistical concepts

• Anyone interested in understanding data better

---

🚀 How to Enroll

1. Go to 👉 https://cognitiveclass.ai/courses/statistics-101

2. Sign up for a free account

3. Enroll and start learning at your own pace!

---

✍ Final Thoughts

Whether you're planning to become a data scientist, exploring machine learning, or just want to become more data-literate, statistics is your essential first step.

The Statistics 101 course by Cognitive Class (IBM) is free, accessible, and certified — making it the ideal way to get started.

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Data Visualization with Python – Free Course by Cognitive Class (IBM)

 

Are you ready to turn raw data into compelling visual stories?

The Data Visualization with Python course offered by Cognitive Class (an initiative by IBM) is a beginner-friendly, hands-on course that teaches you how to create stunning and insightful visualizations using Python — and it’s completely FREE.

---

🧾 Course Overview

Data visualization is one of the most important skills in data science, analytics, and business intelligence. This course walks you through the fundamentals and advanced techniques using popular Python libraries like Matplotlib, Seaborn, and Folium.

🔹 Platform: Cognitive Class (by IBM)
🔹 Level: Beginner to Intermediate
🔹 Duration: ~15 hours
🔹 Cost: Free
🔹 Certificate: Yes, from IBM

---

📚 What You’ll Learn

This course is packed with interactive lessons, real datasets, and practical labs to help you visualize data like a pro.

📌 1. Introduction to Data Visualization

• What is data visualization?

• Why visualization matters in data science

• Types of charts and when to use them

📌 2. Basic Graphs with Matplotlib

• Line plots, bar charts, pie charts, histograms

• Plot customization: labels, legends, colors, styles

📌 3. Advanced Graphs with Seaborn

• Creating beautiful statistical plots

• Box plots, violin plots, swarm plots

• Heatmaps and pair plots

📌 4. Interactive Maps with Folium

• Visualizing geographic data

• Plotting location data on maps

• Adding markers, choropleths, and popups

📌 5. Creating Dashboards

• Combining multiple plots

• Creating storytelling visuals

• Best practices for layout and design

---

🛠️ Tools & Libraries Used

• Matplotlib – Core plotting library

• Seaborn – High-level statistical graphics

• Folium – For interactive leaflet maps

• Pandas – For data manipulation

• Jupyter Notebooks – For hands-on practice

---

🧠 Why Take This Course?

✅ Real-world Datasets – Analyze global economic trends, population, crime stats, and more
✅ Hands-on Labs – Learn by doing inside your browser
✅ No Prior Data Viz Knowledge Needed
✅ Earn a Verified Certificate by IBM
✅ Completely Free

---

🏆 Certificate of Completion

At the end of the course, you can earn an IBM-recognized certificate to showcase your skills on LinkedIn, GitHub, or your portfolio.

---

💬 Student Testimonials

"I never thought visualizing data could be this exciting. This course made it simple and fun!"

"Now I can create compelling charts and dashboards for my reports at work. Thank you, IBM!"

---

📍 Who Should Enroll?

• Beginners in data science or analytics

• Business analysts looking to improve presentations

• Students and professionals curious about data storytelling

---

🔗 How to Enroll

🎯 Visit the course page:
👉 https://cognitiveclass.ai/courses/data-visualization-python

🆓 Sign up with a free account and start learning instantly!

---

✍ Final Thoughts

In the era of data overload, the ability to tell clear, concise, and compelling visual stories is a superpower.

The Data Visualization with Python course by IBM via Cognitive Class is the perfect first step toward mastering this skill — whether you're in business, data science, or just curious.

It’s interactive, hands-on, project-based, and 100% free.

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Data Analysis with Python – Free Course by Cognitive Class (IBM)

 

Want to master data analysis using Python? Whether you're an aspiring data analyst, data scientist, or simply curious about data-driven decision making, the Data Analysis with Python course by Cognitive Class (by IBM) is a must-try — and it’s 100% FREE!

---

🧾 Course Overview

This self-paced course focuses on teaching you how to analyze data using the most popular Python libraries — like Pandas, Numpy, and Scipy — and visualize data using Matplotlib, Seaborn, and Folium.

🔹 Platform: Cognitive Class (by IBM)
🔹 Difficulty: Intermediate
🔹 Duration: ~20 hours
🔹 Cost: Free
🔹 Certificate: Yes, from IBM

---

📚 What You’ll Learn

The course is well-structured into several modules that walk you through everything from importing data to building regression models.

🔹 1. Importing Datasets

• Reading data from different file types (CSV, Excel, SQL)

• Exploring and cleaning datasets using Pandas

🔹 2. Data Wrangling

• Identifying and handling missing values

• Data formatting and normalization

• Binning and indicator variables

🔹 3. Exploratory Data Analysis (EDA)

• Grouping, pivoting, and summarizing data

• Detecting outliers and understanding distributions

• Using boxplots, histograms, and scatter plots

🔹 4. Model Development

• Introduction to machine learning models

• Linear regression and multiple regression

• Model evaluation metrics (MAE, MSE, R²)

🔹 5. Model Evaluation and Refinement

• Splitting data into training and testing sets

• Cross-validation and ridge regression

• Refining models for better accuracy

---

📈 Tools & Libraries Covered

• Pandas – For data manipulation

• NumPy – For numerical operations

• Matplotlib & Seaborn – For visualization

• Scikit-learn – For model building

• Statsmodels, Folium, and more

---

🎓 Certificate of Completion

Complete the course and receive a verified certificate by IBM – a great way to validate your skills and showcase them on LinkedIn or your resume.

---

✅ Why Take This Course?

✅ Practical & Project-Based
✅ Hands-on Labs with Jupyter Notebooks
✅ Real-world datasets used in analysis
✅ Recognized IBM certificate
✅ Absolutely Free! No hidden costs

---

💬 Learner Reviews

“I finally understand how to clean, analyze, and visualize data. The concepts were easy to grasp with examples that felt real.”

“This course helped me get my first freelance data analysis project!”

---

🚀 Who Should Take This?

• Data science and AI beginners

• Business analysts & engineers

• Anyone who has basic Python knowledge and wants to apply it to real-world datasets

---

🔗 How to Enroll

👉 Visit: https://cognitiveclass.ai/courses/data-analysis-python
👉 Sign up for a free account
👉 Enroll and start learning today!

---

✍ Final Thoughts

Data is the new oil, and this course teaches you how to refine it!

Whether you're aiming for a career in data science, improving your current job skills, or just exploring the data world out of curiosity, the Data Analysis with Python course is a perfect launchpad.

Offered by IBM, backed by practical labs, and 100% free — there’s no reason to wait.

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Python for Data Science – Free Course by Cognitive Class (IBM)

 

Are you looking to kickstart your Data Science journey with Python?

Look no further! The Python for Data Science course by Cognitive Class, an IBM initiative, is one of the best free learning resources available for beginners and aspiring data scientists.

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📘 Course Overview

This self-paced course introduces you to the fundamentals of Python programming with a strong emphasis on its application in data science.

🔹 Platform: Cognitive Class (by IBM)
🔹 Level: Beginner
🔹 Duration: ~15 hours
🔹 Cost: FREE
🔹 Certificate: Yes, after completion

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🔍 What You’ll Learn

The course is thoughtfully divided into five modules, each building a strong foundation for data science applications using Python:

1. Introduction to Python

• Why Python is popular in Data Science

• Installation and setup (Anaconda, Jupyter Notebooks)

• Writing your first Python program

2. Python Basics

• Variables and data types

• Expressions and operators

• String operations

• Working with lists, tuples, and dictionaries

3. Python Data Structures

• Creating and modifying lists and dictionaries

• Nesting and indexing

• Practical use cases in Data Science

4. Working with Data in Python

• Reading and writing files

• Introduction to Pandas for data manipulation

• Loading datasets, filtering, and summarizing data

5. Data Visualization

• Using Matplotlib and Seaborn

• Creating line plots, bar charts, scatter plots

• Visualizing real-world datasets

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🧠 Why You Should Take This Course

✅ Beginner-Friendly: No prior programming experience required
✅ Hands-On Labs: Learn by doing with Jupyter Notebooks
✅ Real-World Examples: Practical applications of Python in data analysis
✅ Free Certification: Great for your resume and LinkedIn
✅ Offered by IBM: Recognized and trusted globally

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🏆 Certificate of Completion

Upon passing the quizzes and final exam, you’ll receive a verified certificate from IBM through Cognitive Class — a valuable addition to your data science portfolio.

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💬 Student Feedback

"This is the perfect course to start your Python and Data Science journey. Everything is clearly explained, and the labs make learning fun!"

"Thanks to this course, I cracked my first Data Analyst internship."

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📌 How to Enroll

1. Visit: https://cognitiveclass.ai/courses/python-for-data-science

2. Sign up for a free account

3. Enroll and start learning at your own pace

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✍️ Final Thoughts

The Python for Data Science course by IBM’s Cognitive Class is more than just an introduction — it’s your first real step into the world of data analysis, machine learning, and artificial intelligence.

Whether you’re a student, professional, or curious learner, this course will give you the confidence to code with Python and explore the fascinating field of Data Science.

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🔗 Start learning now:
👉 https://cognitiveclass.ai/courses/python-for-data-science

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Python Coding Challange - Question with Answer (01290625)

 

Loop Range:

for i in range(1, 6)

This means i will go through the numbers:
1, 2, 3, 4, 5

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✅ Now, check each value of i:

➤ i = 1

• 1 % 2 == 0 → ❌ False → does not continue

• i == 5 → ❌ False → does not break
  ✅ print(1, end=" ") → prints 1

➤ i = 2

• 2 % 2 == 0 → ✅ True → continue (skips the rest of loop)
  ❌ Nothing is printed.

➤ i = 3

• 3 % 2 == 0 → ❌ False

• i == 5 → ❌ False
  ✅ print(3, end=" ") → prints 3

➤ i = 4

• 4 % 2 == 0 → ✅ True → continue
  ❌ Nothing is printed.

➤ i = 5

• 5 % 2 == 0 → ❌ False

• i == 5 → ✅ True → break
   Loop stops before printing 5

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✅ Final Output:

1 3

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Summary:

• Skips even numbers using continue

• Stops the loop when i is 5 using break

• Only odd numbers less than 5 get printed

Digital Image Processing using Python

https://pythonclcoding.gumroad.com/l/oxdsvy

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Python Coding challenge - Day 578| What is the output of the following Python Code?

 Code Explanation:

1. Function Definition

def extend_list(val, list=None):

def is used to define a function named extend_list.

The function takes two parameters:

val: the value to be added to the list.

list: an optional parameter. If not provided, it defaults to None.

Using None as a default is a common Python practice to avoid mutable default arguments (like lists).

2. Handling the Default Argument

    if list is None:

        list = []

This block checks if list was provided.

If not (i.e., list is None), it creates a new empty list.

This ensures that a new list is created every time the function is called without an explicit list, avoiding shared state across calls.

3. Appending the Value

    list.append(val)

Adds the val to the list using the append() method.

This modifies the list in place.

4. Returning the Result

    return list

Returns the updated list containing the newly added value.

5. First Function Call

print(extend_list(10))

Calls extend_list with val = 10 and no list argument.

Since no list is provided, a new list is created: []

10 is appended: [10]

Output: "[10]"

6. Second Function Call

print(extend_list(20))

Calls extend_list again, this time with val = 20, still with no list argument.

Again, since no list is provided, a new empty list is created.

20 is appended: [20]

Output: "[20]"

Final Output:

[10]

[20]

Download Book - 500 Days Python Coding Challenges with Explanation

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Python Coding challenge - Day 579| What is the output of the following Python Code?

 

Code Explanation:

1. Function Definition

def func(a, L=[]):

A function named func is defined.

It takes two parameters:

a: a number (likely an integer).

L: a list with a default value of an empty list [].

2. Loop to Append Values

    for i in range(a):

        L.append(i)

A for loop runs from i = 0 to i = a - 1.

Each i is appended to the list L.

3. Return the List

    return L

After the loop, the modified list L is returned.

4. First Function Call

print(func(2))

a = 2, default L = [] (the empty list defined once at function creation).

Loop: i = 0, 1 → L becomes [0, 1]

Output: [0, 1]

5. Second Function Call

print(func(3))

a = 3, but now the default list L is not empty—it's [0, 1] from the previous call.

Loop: i = 0, 1, 2 → Append to existing list → L becomes [0, 1, 0, 1, 2]

Output: [0, 1, 0, 1, 2]

Final Output

[0, 1]

[0, 1, 0, 1, 2]

Download Book - 500 Days Python Coding Challenges with Explanation

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Cloud Computing Foundations

 

There are 5 modules in this course

Welcome to the first course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines. You will also learn how to apply Agile software development techniques to projects which will be useful in building portfolio projects and global-scale Cloud infrastructures. 

This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a statically hosted website using the Hugo framework, AWS Code Pipelines, AWS S3 and GitHub.

Join Now : Cloud Computing Foundations

Free Courses : Cloud Computing Foundations

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Applying AI Principles with Google Cloud

 

What you'll learn

Explain the business case for responsible AI.

Identify ethical considerations with AI using issue spotting best practices.

Describe how Google developed and put their AI Principles into practice and leverage their lessons learned.

Adopt a framework for how to operationalize responsible AI in your organization.

Join Now : Applying AI Principles with Google Cloud

Free Courses : Applying AI Principles with Google Cloud

There are 7 modules in this course

This course, Responsible AI: Applying AI Principles with Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Responsible AI: Applying AI Principles with Google Cloud.

As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. 

In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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