Python Coding challenge - Day 61 | What is the output of the following Python code?

 

In the above code a tuple named cl that contains a single element, the string 'a'. Tuples are defined by placing a comma-separated sequence of values within parentheses. In this case, you have a single value 'a' enclosed in parentheses.

When you print cl, you will get the following output:

('a',)

The trailing comma after 'a' is optional but commonly included in tuples with a single element to distinguish them from regular parentheses. It doesn't affect the tuple's behavior but is a convention used to define one-element tuples.

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Learn to Program: The Fundamentals (Free Course)

 

There are 7 modules in this course

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

This module gives an overview of the course, the editor we will use to write programs, and an introduction to fundamental concepts in Python including variables, mathematical expressions, and functions.

Join Free- Learn to Program: The Fundamentals

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Python Coding challenge - Day 60 | What is the output of the following Python code?

 

The above code creates two lists a and b, each containing a single element with the value 10. When you use the is operator to compare a and b, it checks if they are the same object in memory. In this case, the two lists are not the same object, even though their contents are the same, so a is b will return False`. Here's the code and the result:

a = [10]

b = [10]

print(a is b)  # This will print False

Even though the values in a and b are the same, they are different objects in memory, so the is comparison returns False. If you want to check if the contents of the lists are equal, you should use the == operator:

a = [10]

b = [10]

print(a == b)  # This will print True

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Difference between == and = in python

 

x=[1,2,3] ; y=[1,2,3]

x==y      

x is y

In Python, when you use the == operator, it checks if the values of the two variables are equal. So, when you do x == y, it will return True because the values of x and y are the same: both lists contain the same elements in the same order.

However, when you use the is operator, it checks if two variables refer to the same object in memory. In your example, x is y will return False because even though the values of x and y are the same, they are two different list objects in memory. This is because lists are mutable objects in Python, and the x and y variables refer to two separate list objects that happen to have the same values.

x=[1,2,3] ; y=[1,2,3]

x=y     

x is y

In this case, when you do x = y, you are assigning the variable x to refer to the same list object that y is referring to. As a result, x and y now both point to the same list object in memory. So, when you check x is y, it will return True because they are indeed the same object in memory.

Here's what happens step by step:

x and y are initially two separate list objects with the same values: [1, 2, 3].

When you do x = y, you're essentially making x reference the same list object as y. Now, both x and y point to the same list object.

Therefore, when you check x is y, it returns True because they are the same object in memory.

Keep in mind that this behavior is specific to mutable objects like lists in Python. For immutable objects like integers or strings, x is y would return True if x and y have the same value, because Python caches some immutable objects, and they can be shared among variables.

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Binary search in Python

 

def binary_search(arr, target):

    left, right = 0, len(arr) - 1

    while left <= right:

        mid = (left + right) // 2

        if arr[mid] == target:

            return mid  # Found the target, return its index

        elif arr[mid] < target:

            left = mid + 1  # Target is in the right half

        else:

            right = mid - 1  # Target is in the left half

    return -1  # Target is not in the list

# Example usage:

arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

target = 6

result = binary_search(arr, target)

if result != -1:

    print(f"Element {target} found at index {result}")

else:

    print(f"Element {target} not found in the list")

#clcoding.com

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LehighX: Python Fundamentals for Business Analytics (Free Course)

 This course covers the basics of the Python programming language, and is targeted to students who have no programming experience. The course begins by establishing a strong foundation in basic programming concepts. Through engaging lessons and practical exercises, students will master essential topics such as variables, relational and boolean operators, control statements, and input/output operations. Building on this solid groundwork, students will program with functions, lists, and tuples. In the final phase of the course, students will unlock the full potential of Python by harnessing the capabilities of the popular NumPy library.

About this course

The course will illustrate how Python is utilized in the exciting field of business analytics through real-world examples and hands-on exercises. With an emphasis on interactivity, students will code alongside the course materials. By the end of this course, students will have developed a strong understanding of programming principles, gained proficiency in Python syntax, and developed the skill to apply Python functions to basic analytic problems.

What you'll learn

Upon completion of this course, students should be able to:

1. Install and use the Anaconda distribution of Python through the creation of Jupyter notebooks.

2. Understand and use variables in Python

3. Work with common Python data types including float, integer, string, boolean, lists, and tuples

4. Create expression in Python with variables, relational operators, and boolean operators

5. Implement basic program flow control with if statements and loops

6. Read data from and write data to text files.

7. Utliize important analytic libraries like Numpy

8. Understand how to implement and adequately test algorithms in Python

Join free - Python Fundamentals for Business Analytics

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Coding for Everyone: C and C++ Specialization

 Beginner to Programmer — Learn to Code in C & C++. Gain a deep understanding of computer programming by learning to code, debug, and solve complex problems with C and C++.

What you'll learn

Write and debug code in C and C++ programming languages

Understand algorithms, and how to properly express them

Specialization - 4 course series

This Specialization is intended for all programming enthusiasts, as well as beginners, computer and other scientists, and artificial intelligence enthusiasts seeking to develop their programming skills in the foundational languages of C and C++. Through the four courses — two in C, and two in C++ — you will cover the basics of programming in C and move on to the more advanced C++ semantics and syntax, which will prepare you to apply these skills to a number of higher-level problems using AI algorithms and Monte Carlo evaluation in complex games.

Applied Learning Project

Learners in this specialization will start coding right from the start. Every module presents ample opportunities for writing programs and finding errors in the learner's own and others' code. Building on their knowledge, learners will demonstrate their understanding of coding in a practice-intensive final assessment.

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Python Coding challenge - Day 59 | What is the output of the following Python code?

 

Code - 

s = {1, 2, 3, 4, 1}

s.discard(0)

print(s)

Solution - 

The code does step by step:

s is defined as a set containing the elements {1, 2, 3, 4, 1}. Note that sets in Python are unordered collections of unique elements, so any duplicate elements are automatically removed.

The s.discard(0) line attempts to remove the element 0 from the set s using the discard method.

However, since 0 is not in the set s, calling discard(0) has no effect.

Finally, the print(s) statement prints the contents of the set s after attempting to discard 0. The output will be: {1, 2, 3, 4}.

The discard method is used to safely remove an element from a set if it exists, but it won't raise an error if the element is not present in the set. In contrast, the remove method would raise a KeyError if you try to remove an element that is not in the set.

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What is a best practice to get consistent results when using pandas?

 Pandas is a powerful library for data manipulation and analysis in Python. To ensure consistent and reliable results when using Pandas, consider the following best practices:

Import Pandas Properly: Import Pandas at the beginning of your script or notebook, and use a common alias like import pandas as pd. This makes your code more readable and consistent across projects.

Use Explicit Data Types: When reading data with read_csv, read_excel, or other methods, specify the data types for columns using the dtype parameter. This ensures that Pandas doesn't infer data types incorrectly.

Handle Missing Data: Use appropriate methods like isna(), fillna(), or dropna() to deal with missing data. Be consistent in your approach to handling missing values throughout your analysis.

Consistent Naming Conventions: Use consistent naming conventions for your variables and DataFrame columns. This makes your code more readable and reduces the chance of errors.

Indexing and Selection: Use loc and iloc for explicit and consistent DataFrame indexing. Avoid using chained indexing, as it can lead to unpredictable results.

Method Chaining: Consider using method chaining to perform a sequence of Pandas operations on a DataFrame. This makes your code more concise and readable. Libraries like pandas-flavor and pipe can help with this.

Avoid SettingWithCopyWarning: When creating new DataFrames or manipulating existing ones, avoid using chained assignment. Instead, use copy() to ensure that you work on a copy of the data and not a view, which can lead to unexpected behavior.

Documentation and Comments: Provide documentation and comments in your code to explain the purpose and steps of your data analysis. This helps others understand your code and ensures consistency in your own work.

Testing and Validation: Write unit tests to validate the correctness of your data processing steps. Consistent testing can help catch errors early and maintain reliable results.

Version Control: Use version control tools like Git to keep track of changes in your code and data. This helps maintain consistency when working on projects with a team or over time.

Data Type Awareness: Be aware of data types and their impact on operations. For example, dividing integers in Pandas will result in integer division, which may not be what you expect. Use appropriate casting or conversion to handle data types correctly.

Use Vectorized Operations: Take advantage of Pandas' built-in vectorized operations whenever possible. They are more efficient and lead to consistent results.

Avoid Global Variables: Minimize the use of global variables in your code. Instead, encapsulate your operations in functions to ensure consistent behavior.

Upgrade Pandas: Keep your Pandas library up to date to benefit from bug fixes and improvements. However, be aware that upgrading may require adjustments in your code to maintain consistency.

By following these best practices, you can ensure that your data analysis with Pandas is more consistent, maintainable, and less prone to errors. Consistency in coding practices also helps in collaboration with others and simplifies debugging and troubleshooting.

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Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

 

What's inside this 301 page book?

201 real Data Science interview questions asked by Facebook, Google, Amazon, Netflix, Two Sigma, Citadel and more — with detailed step-by-step solutions!

Learn how to break into Data Science, with tips on crafting your resume, creating kick-ass portfolio projects, sending networking cold emails, and better telling your story during behavioral interviews

Questions cover the most frequently-tested topics in data interviews: Probability, Statistics, Machine Learning, SQL & Database Design, Coding (Python), Product Analytics, and A/B Testing

Each chapter has a brief crash-course on the most important concepts and formulas to review

Learn how to solve open-ended case study questions that combine product-sense, business intuition, and statistical modeling skills, and practice with case interviews from Airbnb, Instagram, & Accenture

Buy - Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

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Automating Real-World Tasks with Python

 

What you'll learn

Use Python external libraries to create and modify documents, images, and messages

Understand and use Application Programming Interfaces (APIs) to interact with web services

Understand and use data serialization to send messages between running programs

Build a solution using the skills you have learned

There are 5 modules in this course

In the final course, we'll tie together the concepts that you’ve learned up until now. You'll tackle real-world scenarios in Qwiklabs that will challenge you to use multiple skills at once.

First, we'll take a closer look at how to use external Python modules to extend your code's capabilities, and spend some time learning how to use documentation to learn a new module. For example, we'll use the Python Image Library (PIL) to create and modify images. We'll show you some simple examples of how to perform common tasks in the course material, but it will be up to you to explore the module documentation to figure out how to solve specific problems.

Next, we'll show you how to communicate with the world outside of your code! You'll use data serialization to turn in-memory objects into messages that can be sent to other programs. Your program will send messages across the network to Application Programming Interfaces (APIs) offered by other programs. For those times when your code needs to talk to a person instead of a program, you'll also learn to send email messages.

At the end of this course, you’ll be able to take a description of a problem and use your skills to create a solution -- just like you would on the job. In your final capstone project, you'll be given a description of what your customer needs, and it will be up to you to create a program to do it!

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Getting Started With Game Development Using PyGame

 

About this Guided Project

In this 1-hour long project-based course, you will learn how to create a basic single-player Pong replica using the PyGame library for Python, creating a welcome screen, a game that responds to user input to move the paddle, scoring, and a game over screen with user options. By the end of the course, learners will have a basic understanding of the PyGame library and will be able to create simple games built on shapes. No previous experience with PyGame is required, as this is a basic introduction to the library, but familiarity with Python is recommended.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Create the display and the ball

Add motion to the ball and a paddle

Detect collisions and create a Game Over screen

Expand game play options

Reset the game to continue play

Scorekeeping, randomizing, and expansion options

Join  - Getting Started With Game Development Using PyGame

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Applied Text Mining in Python

 

What you'll learn

Understand how text is handled in Python

Apply basic natural language processing methods

Write code that groups documents by topic

Describe the nltk framework for manipulating text

There are 4 modules in this course

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). 

Join - Applied Text Mining in Python

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Python Project for Data Science

 

What you'll learn

Play the role of a Data Scientist / Data Analyst working on a real project.

Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis. 

Apply Python fundamentals, Python data structures, and working with data in Python.

Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

There is 1 module in this course

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends. 

You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard. This project will showcase your proficiency with Python and using libraries such as Pandas and Beautiful Soup within a Jupyter Notebook. Upon completion you will have an impressive project to add to your job portfolio.   

PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.  

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

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Python Coding challenge - Day 58 | What is the output of the following Python code?

 

In the above code a function add(a, b) that takes two arguments a and b and returns their sum using the + operator. However, there's a potential issue because you are trying to add an integer (3) and a string ('2') together, which can lead to a type error.

When you call add(3, '2'), the function is trying to add an integer and a string, which is not a valid operation in Python. Python typically doesn't allow you to perform arithmetic operations between different data types without explicit type conversion.

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Python Coding challenge - Day 57 | What is the output of the following Python code?

 

The code and the output for print([] * 3):

print([] * 3)

Output: []

In Python, when you use the * operator with a list and a number, it is used for repetition. When you do [] * 3, it repeats the empty list [] three times. This is a feature of Python that allows you to create a new list by repeating the elements of an existing list.

So, in the case of print([] * 3), it repeats the empty list [] three times, resulting in a new empty list with no elements. This is how the * operator works when used with lists and numbers in Python.

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Python Project for Data Engineering

 

What you'll learn

Demonstrate your skills in Python for working with and manipulating data

Implement webscraping and use APIs to extract data with Python

Play the role of a Data Engineer working on a real project to extract, transform, and load data

Use Jupyter notebooks and IDEs to complete your project

There are 3 modules in this course

Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data.  

You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. 

By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. 

Upon completion of this course, you will also have a great new addition to your portfolio! 

Join - Python Project for Data Engineering

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Python for Data Science, AI & Development

 

What you'll learn

Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.

Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.

Access web data using APIs and web scraping from Python in Jupyter Notebooks.  

There are 5 modules in this course

Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher.  

This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching. You will use Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with APIs.  

You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python.  

This course is suitable for anyone who wants to learn Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps as well as a number of other job roles. 

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Python Coding challenge - Day 56 | What is the output of the following Python code?

 

Code - 

x = 1

while x <= 10:

    if x % 3 == 0:

        print(x)

    x += 1

Solution - 

The above code is a simple Python while loop that iterates through the numbers from 1 to 10 and prints the values that are divisible by 3. Here's a step-by-step explanation of how the code works:

Initialize the variable x with the value 1.

Enter a while loop with the condition x <= 10, which means the loop will continue as long as x is less than or equal to 10.

Inside the loop, there is an if statement that checks if the current value of x is divisible by 3 (i.e., x % 3 == 0). If the condition is true, the code inside the if block is executed.

If x is divisible by 3, the current value of x is printed using the print function.

After printing the value, x is incremented by 1 using x += 1, which means the loop will proceed to the next value.

The loop continues to the next iteration until x is no longer less than or equal to 10.

This code will print the numbers 3, 6, and 9 because those are the values in the range from 1 to 10 that are divisible by 3.

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Python Coding challenge - Day 55 | What is the output of the following Python code?

 

Code - 

x = 10

while x > 0:

    print(x)

    x -= 1

Solution - 

The above code is a simple Python while loop that counts down from 10 to 1 and prints each value of x in each iteration. Here's what the code does step by step:

Initialize the variable x with the value 10.

Enter a while loop that continues as long as the value of x is greater than 0.

Print the current value of x.

Decrement the value of x by 1 (using x -= 1).

Repeat steps 3 and 4 until the value of x becomes 0 or negative.

The output of this code will be:

10

9

8

7

6

5

4

3

2

1

After the loop, the value of x will be 0, and the loop will terminate since x > 0 is no longer true.

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Machine Learning Basics (Free Course)

 

There are 4 modules in this course

In this course, you will:   

a) understand the basic concepts of machine learning.

b) understand a typical memory-based method, the K nearest neighbor method.

c) understand linear regression.

d) understand model analysis.

Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

JOIN Free - Machine Learning Basics

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Build a Website using an API with HTML, JavaScript, and JSON (Free Course)

 

Objectives

Provide ability for website users to look up 7 day weather forecasts for major European cities
Keep website visitors on the website longer
Increase online bookings

About this Project

In this project, you’ll support a European travel agency’s effort to increase booking by building a webpage that provides visitors with a 7-day weather forecast for major European cities. 

Accomplishing this task will require you to retrieve real-time weather data from an external service. In creating the webpage, you’ll request, process, and present the weather data using HTML, JavaScript, and JSON. 

There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers.

ROLE: Software Developer

SKILLS: Web Design, Web Development, Cloud API

PREREQUISITES: 

Function closures, asynchronous processing, REST API, and JSON handling with JavaScript

Present content with HTML tags 

Present content using classes with CSS

Format and syntax of JSON

JOIN Free - Build a Website using an API with HTML, JavaScript, and JSON

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Problem Solving, Python Programming, and Video Games (Free Course)

 

There are 12 modules in this course

This course is an introduction to computer science and programming in Python.  Upon successful completion of this course, you will be able to:

1.  Take a new computational problem and solve it, using several problem solving techniques including abstraction and problem decomposition.

2.  Follow a design creation process that includes: descriptions, test plans, and algorithms.

3.  Code, test, and debug a program in Python, based on your design.

Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout.

This course uses problem-based learning. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. The instructional videos present Python using a conceptual framework that can be used to understand any programming language. This framework is based on several general programming language concepts that you will learn during the course including: lexics, syntax, and semantics.

Other approaches to programming may be quicker, but are more focused on a single programming language, or on a few of the simplest aspects of programming languages. The approach used in this course may take more time, but you will gain a deeper understanding of programming languages. After completing the course,  in addition to learning Python programming, you will be able to apply the knowledge and skills you acquired to: non-game problems, other programming languages, and other computer science courses.

You do not need any previous programming, Python, or video game experience.  However, several basic skills are needed: computer use (e.g., mouse, keyboard, document editing), elementary mathematics, attention to detail (as with many technical subjects), and a “just give it a try” spirit will be keys to your success.  Despite the use of video games for the main programming project, PVG is not about computer games.  For each new programming concept, PVG uses non-game examples to provide a basic understanding of computational principles, before applying these programming concepts to video games.

The interactive learning objects (ILO) of the course provide automatic, context-specific guidance and feedback, like a virtual teaching assistant, as you develop problem descriptions, functional test plans, and algorithms.  The course forums are supported by knowledgeable University of Alberta personnel, to help you succeed.

All videos, assessments, and ILOs are available free of charge.  There is an optional Coursera certificate available for a fee. 

JOIN Free  - Problem Solving, Python Programming, and Video Games

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Python Coding challenge - Day 54 | What is the output of the following Python code?

 

Code - 

roman = {1:'i',2:'ii'}

d,r=roman

print(d,r)

Solution - 

In above code, Code is trying to unpack the dictionary roman into two variables d and r. When you unpack a dictionary like this, it iterates over the keys of the dictionary and assigns them to the variables. In this case, d will be assigned the first key (1), and r will be assigned the second key (2).

Here's what the code does step by step:

d, r = roman tries to unpack the dictionary roman.

The keys of the dictionary roman are 1 and 2.

d is assigned the first key, which is 1.

r is assigned the second key, which is 2.

So, after this code executes, d will be 1 and r will be 2.

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Data Structures and Algorithms Specialization

 

Master Algorithmic Programming Techniques. Advance your Software Engineering or Data Science Career by Learning Algorithms through Programming and Puzzle Solving. Ace coding interviews by implementing each algorithmic challenge in this Specialization. Apply the newly-learned algorithmic techniques to real-life problems, such as analyzing a huge social network or sequencing a genome of a deadly pathogen.

What you'll learn

Play with 50 algorithmic puzzles on your smartphone to develop your algorithmic intuition!  Apply algorithmic techniques (greedy algorithms, binary search, dynamic programming, etc.) and data structures (stacks, queues, trees, graphs, etc.) to solve 100 programming challenges that often appear at interviews at high-tech companies. Get an instant feedback on whether your solution is correct.

Apply the newly learned algorithms to solve real-world challenges: navigating in a Big Network  or assembling a genome of a deadly pathogen from millions of short substrings of its DNA.

Learn exactly the same material as undergraduate students in “Algorithms 101” at top universities and more! We are excited that students from various parts of the world are now studying our online materials in the Algorithms 101 classes at their universities. Here is a quote from the website of Professor 

If you decide to venture beyond Algorithms 101, try to solve more complex programming challenges (flows in networks, linear programming, streaming algorithms, etc.) and complete an equivalent of a graduate course in algorithms!

Specialization - 6 course series

Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.  

Applied Learning Project

The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco 1000 times faster than the shortest path algorithms you learn in the standard Algorithms 101 course! Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

JOIN free - Data Structures and Algorithms Specialization

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Python Coding challenge - Day 53 | What is the output of the following Python code?

 

Code -  

c = 'hello'

print(c.center(10, '1'))

Solution - 

c = 'hello': In this line, you create a variable c and assign the string 'hello' to it.

c.center(10, '1'): This is the main part of the code where you use the center method on the string c.

c is the string 'hello'.

.center(10, '1') is calling the center method on the string c with two arguments:

10 is the total width you want for the resulting string.

'1' is the character you want to use to fill the remaining space on both sides of the centered string.

The center method then centers the string 'hello' within a total width of 10 characters, using the fill character '1' for the remaining space.

print(c.center(10, '1')): This line prints the result of the center method to the console.

Now, let's break down the output:

The specified total width is 10 characters.

The string 'hello' is 5 characters long.

So, there are 10 - 5 = 5 spaces to fill on either side of 'hello'.

The output is: 11hello111

Here's how it's constructed:

First, you have 5 '1' characters on the left side.

Then, you have the string 'hello'.

Finally, you have 3 '1' characters on the right side to reach the total width of 10 characters.

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Introduction to Embedded Machine Learning (Free Course)

 

What you'll learn

The basics of a machine learning system

How to deploy a machine learning model to a microcontroller

How to use machine learning to make decisions and predictions in an embedded system

There are 3 modules in this course

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers.

This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course. Familiarity with Arduino and microcontrollers is advised to understand some topics as well as to tackle the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects.

We will cover the concepts and vocabulary necessary to understand the fundamentals of machine learning as well as provide demonstrations and projects to give you hands-on experience.

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Algorithms, Part I (Free Course)

 

There are 13 modules in this course

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free.  It does not offer a certificate upon completion.

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Foundations of Cybersecurity from Google (Free Course)

 

What you'll learn

Recognize core skills and knowledge needed to become a cybersecurity analyst

Identify how security attacks impact business operations

Explain security ethics

Identify common tools used by cybersecurity analysts

Build your Computer Security and Networks expertise

This course is part of the Google Cybersecurity 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 Google
There are 4 modules in this course

This is the first course in the Google Cybersecurity Certificate. These courses will equip you with the skills you need to prepare for an entry-level cybersecurity job. 

In this course, you will be introduced to the world of cybersecurity through an interactive curriculum developed by Google. You will identify significant events that led to the development of the cybersecurity field, explain the importance of cybersecurity in today's business operations, and explore the job responsibilities and skills of an entry-level cybersecurity analyst. 

Google employees who currently work in cybersecurity will guide you through videos, provide hands-on activities and examples that simulate common cybersecurity tasks, and help you build your skills to prepare for jobs. 

Learners who complete the eight courses in the Google Cybersecurity Certificate will be equipped to apply for entry-level cybersecurity roles. No previous experience is necessary.

By the end of this course, you will: 

- Identify how security attacks impact business operations.

- Explore the job responsibilities and core skills of an entry-level cybersecurity analyst.

- Recognize how past and present attacks on organizations led to the development of the cybersecurity field.

- Learn the CISSP eight security domains.

- Identify security domains, frameworks, and controls.

- Explain security ethics.

- Recognize common tools used by cybersecurity analysts.

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3 Uses of Walrus Operators in Python

 The walrus operator (:=) in Python, introduced in Python 3.8, allows you to both assign a value to a variable and use that value in an expression in a single line. This can lead to more concise and readable code. Here are three common uses of the walrus operator in Python:

Simplify While Loops:

The walrus operator is often used to simplify while loops by allowing you to combine the assignment and condition check in a single line. This is particularly useful when you want to read lines from a file until a certain condition is met.

with open('data.txt') as file:

    while (line := file.readline().strip()) != 'END':

        # Process the line

In this example, the line variable is assigned the value of file.readline().strip() and then immediately checked to see if it's equal to 'END'. The loop continues until the condition is False.

Simplify List Comprehensions:

The walrus operator can simplify list comprehensions by allowing you to use the assigned variable within the list comprehension. This is especially useful when you want to filter or transform elements in a list based on a condition.

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

doubled_even_numbers = [x * 2 for x in numbers if (x % 2 == 0)]

In this example, the x * 2 operation is only performed when x is an even number.

Optimize Expressions for Performance:

In some cases, the walrus operator can be used to optimize code for performance by avoiding redundant calculations. This is particularly useful when working with expensive operations or complex conditions.

result = (expensive_function(x) if (x > 0) else None)

In this example, expensive_function(x) is only called when x is greater than 0. This can save computational resources by avoiding unnecessary function calls.

The walrus operator simplifies code in cases where you need to assign and use a variable within an expression, improving readability and, in some cases, performance. However, it should be used judiciously to maintain code clarity.

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Generative AI with Large Language Models (Free Course)

 

What you'll learn

Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works

Dive into the latest research on Gen AI to understand how companies are creating value with cutting-edge technology

Instruction from expert AWS AI practitioners who actively build and deploy AI in business use-cases today

Skills you'll gain

Generative AI

LLMs

large language models

Machine Learning

Python Programming

There are 3 modules in this course

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

By taking this course, you'll learn to:

- Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment

- Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases

- Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements

- Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project 

- Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners

Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.

This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.

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Python Coding challenge - Day 52 | What is the output of the following Python code?

Code - 

numbers = [1, 2, 3]

for num in numbers:

    print(num)

Solution - 

Step-by-step explanation of the code:

List Definition: You start by defining a list named numbers containing three integers: 1, 2, and 3.

numbers = [1, 2, 3]

This line creates a list with the values [1, 2, 3] and assigns it to the variable numbers.

For Loop: You then use a for loop to iterate over the elements in the numbers list.

for num in numbers:

This loop will go through each element of the numbers list, and for each iteration, the current element is assigned to the variable num.

Print Statement: Inside the loop, you have a print statement.

print(num)

This line prints the value of num to the console.

Iteration: The loop executes three times, once for each element in the numbers list.

1. In the first iteration, num is 1, and it is printed to the console.
2. In the second iteration, num is 2, and it is printed to the console.
3. In the third iteration, num is 3, and it is printed to the console.

Output: As a result, the code will print each number in the numbers list on a separate line.

The output will be:

1

2

3

Each number is printed on a new line, so the output displays:

1

2

3

That's the step-by-step explanation of the provided Python code. It simply prints the elements of the numbers list, one at a time, on separate lines.

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Python Coding challenge - Day 51 | What is the output of the following Python code?

 

Code - 

r = [20, 40, 60, 80]

r[1:4] = []

print(r)

Detailed Solution - 

initialize a list called r with four elements:

r = [20, 40, 60, 80]

Attempt to modify the list by removing elements using a slice. The slice notation used is [1:4], which means it will remove elements starting from index 1 (inclusive) up to index 4 (exclusive).

The list r is modified as follows:

1. Remove the element at index 1 (which is 40).
2. Remove the element at index 2 (which is 60).
3. Remove the element at index 3 (which is 80).

After the modifications, the list r will now look like this:

r = [20]

Finally, the code prints the modified list:

[20]

So, the step-by-step solution demonstrates that the code removes elements 40, 60, and 80 from the list r, leaving only the element 20 in the list.

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IBM: SQL for Data Science (Free Course)

 

Learn how to use and apply the powerful language of SQL to better communicate and extract data from databases - a must for anyone working in the data science field.

About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.

The emphasis in this course is on hands-on, practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.

No prior knowledge of databases, SQL, Python, or programming is required.

What you'll learn

Learn and apply foundational knowledge of the SQL language

How to create a database in the cloud

How to use string patterns and ranges to query data

How to sort and group data in result sets and by data type

How to analyze data using Python

JOIN Free - IBM: SQL for Data Science

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Python Coding challenge - Day 50 | What is the output of the following Python code?

 

Code - 

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

print(a[:4].pop())

Detailed Solution - 

This code will print 4. Here's what happens:

a[:4] creates a new list slice containing the elements [1, 2, 3, 4].

Then, pop() is called on this new list slice, which removes and returns the last element of the slice, which is 4.

The print() function then displays the value returned by pop(), which is 4.

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IBM Data Science Professional Certificate

 

There are 10 courses in this program

• Course 1: What is Data Science?

• Course 2: Tools for Data Science

• Course 3: Data Science Methodology

• Course 4: Python for Data Science, AI & Development

• Course 5: Python Project for Data Science

• Course 6: Databases and SQL for Data Science with Python

• Course 7: Data Analysis with Python

• Course 8: Data Visualization with Python

• Course 9: Machine Learning with Python

• Course 10: Applied Data Science Capstone

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Python Coding challenge - Day 49 | What is the output of the following Python code?

 

Question

k = [2, 1, 0, 3, 0, 2, 1]

print(k.count(k.index(0)))

Solution

The given list is k = [2, 1, 0, 3, 0, 2, 1].

k.index(0) finds the index of the first occurrence of the value 0 in the list k. In this case, the first occurrence of 0 is at index 2.

The result of k.index(0) is 2.

k.count(2) counts how many times the value 2 appears in the list k.

In the list k, the value 2 appears twice, at index 0 and index 5.

So, the final result is 2 because the index of the first occurrence of 0 (which is 2) appears twice in the list k.

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Python For Everybody: Python Programming Made Easy (Free eBook)

 

Yes, Python developers are in high-demand.

Python software engineers are also among the highest-paid software developers today, earning an average income of $150,000 a year.

The Python language is easy to learn, yet POWERFUL.

YouTube, Dropbox, Google, Instagram, Spotify, Reddit, Netflix, Pinterest - they are all developed using Python.

And most recently, ChatGPT is also written in Python.

Learning Python opens up the possibilities of a whole new career in Data Science.

This book contains only the first 10 chapters (Chapters 1 to 10) of my online Python course as a Udemy instructor.

eBook - Python For Everybody: Python Programming Made Easy

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Introduction to Data Science with Python - October 2023

 

What you'll learn

Gain hands-on experience and practice using Python to solve real data science challenges

Practice Python coding for modeling, statistics, and storytelling

Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn

Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems

Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

Course description

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI). 

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

JOIN Free  - Introduction to Data Science with Python - October 2023

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10 Advanced Python CLI Tricks To Save You From Writing Code

Here are 10 advanced Python Command Line Interface (CLI) tricks and techniques that can help you save time and effort when working with CLI applications:

Click Library:

Click is a powerful Python library for creating command-line interfaces. It simplifies the process of defining and parsing command-line arguments and options. By using Click, you can create well-structured and user-friendly CLI applications with minimal code.

import click

@click.command()

@click.option('--name', prompt='Your name', help='Your name')

def hello(name):

    click.echo(f'Hello, {name}!')

if __name__ == '__main__':

    hello()

Argparse Subcommands:

If your CLI application has multiple subcommands, use the argparse library to define and manage them. Subcommands allow you to organize your CLI tools logically.

Colorama for Colored Output:

The Colorama library makes it easy to add colored text to your CLI application's output. This can help highlight important information and make your tool more user-friendly.

from colorama import Fore, Style

print(f'{Fore.GREEN}Success!{Style.RESET_ALL} Operation completed.')

Progress Bars with TQDM:

Use the tqdm library to add progress bars to your CLI applications, especially for time-consuming tasks. It provides a visual indicator of progress.

from tqdm import tqdm

import time

for i in tqdm(range(10)):

    time.sleep(1)

Configparser for Configuration Files:

The configparser library allows you to read and write configuration files for your CLI application. This is useful for storing settings and user preferences.

Logging:

Implement robust logging using Python's built-in logging module. It helps you track errors, debug your application, and provide better user feedback.

import logging

logging.basicConfig(filename='myapp.log', level=logging.INFO)

Interactive Menus:

If your CLI application has a menu-driven interface, use libraries like inquirer or prompt_toolkit to create interactive menus for user input.

Shell Command Execution:

You can execute shell commands from within your Python CLI application using the subprocess module. This is useful for running external commands and integrating them into your tool.

import subprocess

result = subprocess.run(['ls', '-l'], capture_output=True, text=True)

print(result.stdout)

Table Formatting:

Use libraries like tabulate or PrettyTable to format data into tables for better presentation, especially when dealing with tabular data in your CLI application.

Unit Testing:

Make use of Python's unittest or pytest to create unit tests for your CLI application. Proper testing ensures that your code is robust and reliable.

These advanced Python CLI tricks should help you streamline the development and improve the user experience of your command-line applications. Remember to choose the right tools and techniques based on your specific requirements.

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Python Coding challenge - Day 48 | What is the output of the following Python code?

 

Code - 

q = [47, 28, 33, 54, 15]

q.reverse()

print(q[:3])

Detailed Solution - 

In this code, you have a list q containing five elements [47, 28, 33, 54, 15]. You then use the reverse() method to reverse the order of the elements in the list. Finally, you print the first three elements of the modified list. Here's what happens step by step:

q = [47, 28, 33, 54, 15]: q is a list with five elements.

q.reverse(): This reverses the order of the elements in the list q. After this operation, q becomes [15, 54, 33, 28, 47].

print(q[:3]): This prints the first three elements of the modified list q, which are [15, 54, 33].

So, the output will be: [15, 54, 33]

step-by-step solutions to the code - 

q = [47, 28, 33, 54, 15]

Step 1: Initialize a list q with five elements: [47, 28, 33, 54, 15].

q.reverse()

Step 2: Use the reverse() method to reverse the order of elements in the list q.

After this operation, q becomes [15, 54, 33, 28, 47].

print(q[:3])

Step 3: Print the first three elements of the modified list q.

The output will be:

[15, 54, 33]

These are the first three elements of the list q after it has been reversed.

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Crash Course on Python (From google)

 

What you'll learn

What Python is and why Python is relevant to automation

How to write short Python scripts to perform automated actions

How to use the basic Python structures: strings, lists, and dictionaries

How to create your own Python objects

There are 6 modules in this course

This course is designed to teach you the foundations in order to write simple programs in Python using the most common structures. No previous exposure to programming is needed. By the end of this course, you'll understand the benefits of programming in IT roles; be able to write simple programs using Python; figure out how the building blocks of programming fit together; and combine all of this knowledge to solve a complex programming problem. 

We'll start off by diving into the basics of writing a computer program. Along the way, you’ll get hands-on experience with programming concepts through interactive exercises and real-world examples. You’ll quickly start to see how computers can perform a multitude of tasks — you just have to write code that tells them what to do.

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Information Extraction from Free Text Data in Health (Free Project)

 

What you'll learn

Identify text mining approaches needed to identify and extract different kinds of information from health-related text data.

Differentiate how training deep learning models differ from training traditional machine learning models.

There are 4 modules in this course

In this MOOC, you will be introduced to advanced machine learning and natural language

processing techniques to parse and extract information from unstructured text documents in

healthcare, such as clinical notes, radiology reports, and discharge summaries. Whether you are an aspiring data scientist or an early or mid-career professional in data science or information technology in healthcare, it is critical that you keep up-to-date your skills in information extraction and analysis. 

To be successful in this course, you should build on the concepts learned through other intermediate-level MOOC courses and specializations in Data Science offered by the University of Michigan, so you  will be able to delve deeper into challenges in recognizing medical entities in health-related documents, extracting clinical information, addressing ambiguity and polysemy to tag them with correct concept types, and develop tools and techniques to analyze new genres of health information.

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

Identify text mining approaches needed to identify and extract different kinds of information from health-related text data

Create an end-to-end NLP pipeline to extract medical concepts from clinical free text using one terminology resource

Differentiate how training deep learning models differ from training traditional machine learning models

Configure a deep neural network model to detect adverse events from drug reviews

List the pros and cons of Deep Learning approaches."

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Python Coding challenge - Day 47 | What is the output of the following Python code?

 

CODE - 

n = [76, 24]

p = n.copy()

n.pop()

print(p, n)

Solution - 

Step 1: Initialize the list n with two elements [76, 24].

n = [76, 24]

Step 2: Create a copy of the list n and assign it to the variable p using the copy() method. Both p and n will initially contain the same elements.

p = n.copy()

At this point, p and n are both [76, 24].

Step 3: Use the pop() method on the list n without specifying an index, which by default removes and returns the last element of the list. In this case, it removes the last element, 24, from the list n. So, after this line of code, n contains only [76].

n.pop()

Now, n is [76].

Step 4: Print the contents of the variables p and n. This will display the values of both lists at this point.

print(p, n)

The output will be:

[76, 24] [76]

p still contains the original values [76, 24], while n has had one element removed and now contains [76].

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Perform exploratory data analysis on retail data with Python (Free Project)

 

Project

Demonstrate your skills to employers, and leverage industry tools to solve real-world challenges

Objectives

Load, clean, analyze, process, and visualize data using Python and Jupyter Notebooks

Produce an end-to-end exploratory data analysis using Python and Jupyter Notebooks

About this Project

In this project, you'll serve as a data analyst at an online retail company helping interpret real-world data to help make key business decisions. Your task is to explore and analyze this dataset to gain insights into the store's sales trends, customer behavior, and popular products. 

Upon completion, you’ll be able to demonstrate your ability to perform a comprehensive data analysis project that involves critical thinking, extensive data analysis and visualization, and making data-driven business decisions.

There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers.

ROLE: Data Analyst

SKILLS: Python

PREREQUISITES: 

Python, Numpy, Matplotlib or Seaborn, Git, Jupyter Notebook

Project plan

This project requires you to independently complete the following steps:

Import required libraries

Load and explore the data

Clean the data

Visualize and analyze the data

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