#Python programming skills

[niceitservices1]

In 2025, Python continues to dominate the tech world. Whether you're a student, a working professional, or someone looking for a career switch, Python is the one language you can rely on. It powers everything from web development and automation to artificial intelligence and data science.

[cleveredlearning]

Teaching Python Programming to High School Students and Offering Online Coding Courses for Teenagers

In today's digitally-driven world, coding and programming have become essential skills for students of all ages. High school students and teenagers, in particular, can greatly benefit from learning a programming language like Python. Python is known for its simplicity, versatility, and a wide range of applications, making it an ideal choice for beginners. In this article, we will explore the significance of Python programming for high school students and online coding courses designed for teenagers.

Python: The Perfect Choice for Young Coders

Python is often recommended as the first programming language for beginners, and for a good reason. Its user-friendly syntax and readability make it accessible to students with little to no coding experience. High school students and teenagers can quickly grasp the fundamental concepts of programming and start building their own projects, from simple games to web applications.

One of the key advantages of Python is its real-world applicability. Students can apply their Python knowledge to various fields, including data science, artificial intelligence, web development, and more. This versatility encourages young learners to explore their interests and create innovative solutions to real-world problems.

Benefits of Python Programming for High School Students

Enhances Problem-Solving Skills: Learning Python involves understanding how to break down complex problems into manageable steps. This process fosters critical thinking and problem-solving skills, which are invaluable both in academics and everyday life.

Prepares for Future Careers: With the growing demand for tech-savvy professionals, Python programming skills provide high school students with a competitive edge in the job market. Moreover, it opens doors to exciting career opportunities in fields like software development, data analysis, and artificial intelligence.

Boosts Creativity: Python allows students to unleash their creativity by developing their own projects. This creative aspect of coding can be particularly appealing to teenagers looking to express themselves and innovate.

Builds Strong Logical Thinking: Programming requires precise thinking and logical reasoning. Python's clean and readable syntax helps students develop logical thinking skills, which are crucial for a wide range of subjects and future careers.

Online Coding Courses for Teens

Online coding courses provide a flexible and accessible way for teenagers to learn Python and other programming languages. These courses are specifically designed to cater to the unique needs and interests of young learners.

Engaging Content: Online coding courses for teens often feature interactive and engaging content, including video tutorials, quizzes, and hands-on coding exercises. This makes the learning process more enjoyable and effective.

Self-Paced Learning: Teenagers have busy schedules with school, extracurricular activities, and social commitments. Online courses allow them to learn at their own pace, ensuring they can balance their education with other responsibilities.

Access to a Global Community: Online coding platforms offer the opportunity to connect with a global community of like-minded individuals. This fosters collaboration, knowledge sharing, and support among teenagers who share a passion for coding.

Certifications and Projects: Many online coding courses provide certifications upon completion, which can be valuable for high school students' resumes. Additionally, students can work on coding projects that they can showcase to potential colleges or employers.

Conclusion

Python programming for high school students and online coding courses for teens offer invaluable opportunities for young learners. Python's accessibility, versatility, and real-world applications make it an excellent choice for beginners. Learning to code at a young age not only enhances problem-solving skills and logical thinking but also prepares students for future careers in technology. Online coding courses provide flexibility, engagement, and the chance to connect with a global community, ensuring that teenagers have the tools they need to succeed in the digital age. Whether they are interested in building websites, creating apps, or diving into the exciting world of artificial intelligence, Python programming and online coding courses can open up a world of possibilities for high school students and teens.

[forever-stuck-on-java-8]

I was forced to use python at work today.

It actually wasn't terrible but damn do I miss types

figuring out what what I thought was a string is actually a dictionary AT RUNTIME is SO FUCKING ANNOYING because I forgot a .values() somewhere

[lesbianfakir]

Y’all I’m so sad. I keep playing video games made by one or two people and now I want to make a video game soooooooo bad so I can design my perfect game and more importantly make the art. But!! Programming is my mortal enemy she and I do NOT get along. Maybe I could make something simple in terms of programming like a visual novel but I want more involved gameplay :,( I really want a fun video game project now

[datasciencewithmohsin]

Simple Linear Regression in Data Science and machine learning

Simple linear regression is one of the most important techniques in data science and machine learning. It is the foundation of many statistical and machine learning models. Even though it is simple, its concepts are widely applicable in predicting outcomes and understanding relationships between variables.

This article will help you learn about:

1. What is simple linear regression and why it matters.

2. The step-by-step intuition behind it.

3. The math of finding slope() and intercept().

4. Simple linear regression coding using Python.

5. A practical real-world implementation.

If you are new to data science or machine learning, don’t worry! We will keep things simple so that you can follow along without any problems.

What is simple linear regression?

Simple linear regression is a method to model the relationship between two variables:

1. Independent variable (X): The input, also called the predictor or feature.

2. Dependent Variable (Y): The output or target value we want to predict.

The main purpose of simple linear regression is to find a straight line (called the regression line) that best fits the data. This line minimizes the error between the actual and predicted values.

The mathematical equation for the line is:

Y = mX + b

: The predicted values.

: The slope of the line (how steep it is).

: The intercept (the value of when).

Why use simple linear regression?

click here to read more https://datacienceatoz.blogspot.com/2025/01/simple-linear-regression-in-data.html

[pansy-placebo]

My friend showed me how to program today! I made a script to pick random items from a list, based on how much energy I have, to make it easier for me to do things every day :) !!!!!!!!!!!!!

(For context I am chronically ill and have massive executive dysfunction on top of that, so being told what to do early in the day depending on my energy makes things so much easier for me)

Ignore the excessive number of ++++ I made them for desktop and this is on mobile , I'll adjust it another time

Anyway I am VERY proud :D and greatful for my friend showing me!!!!

Also I'm already coming up for cool ideas on how to use this for art and fun little projects for my friends :)

I could program a choose-your-own adventure game AAAAAA!!!

Like I want to make one that asks what your name is and when a friend types their name in, it comes up with a little message/poem/ascii art picked just for them!!! Like I have a friend who's really into fanfic and I could have a fanfic or a reference come up, and another name would bring up a huge ascii penis, another name would bring up a poem I wrote for them- I'm losing my mind the possibilities are endless.

Oh also the guy who taught me is my bf's bf, who I want to be more than just boyfriends-in-law with so bonding like this is a boon for me because it shows me in a different light than "trapped by illness and depression"

[demifiendcruithne]

i am not sorry for making my personal helper program pull a demi-fiend if i try and leave its folder

i thought it was funny. now if only terminals supported orange text

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[sorryimananti-romantic]

hello fellow programmer friend i have missed u and im here to tell u im starting programming in c# this year and im terrified also object oriented programming definitely not crying rnn !! (im so so excited tho)

omg hi fellow programmer i missed you too! good luck to you, i did oop in python and it was 💀💀 (i had to watch a few youtube tutorials to wrap my head around it [they all use the same car example which was really not helpful like i didn't get it until they used dogs to explain 😭 LOL you'll know what i'm talking about soon] but once you understand it, it gets fun). i'm here to wipe your tears away hehe good luck!! <33

[vanilla-voyeur]

There are so, so, so many reasons why this incredibly fake story is incredibly fake. But I just keep reading the API technobabble and I can't stop laughing.

Mf out here bragging about how not only is their code unreadable and unmaintainable, but also their documentation is unreadable and undiscoverable. The problem that this creative writer has is that they need to be indispensable in this story, but unfortunately an indispensable programmer is uniquely gifted at making themself superfluous as quickly and efficiently as possible in as many contexts as possible.

The "nobody is familiar with Python" part is probably my favorite. Python is one of the easiest programming languages to learn. It's so easy that it's the language that engineers (real engineers like chemical engineers, biomedical engineers, material engineers, not software engineers developers) use to help them automate things in their work. Anyone who is familiar with any other programming language can pick up Python in under an hour. You who are familiar with zero programming languages could probably pick up a decent amount of Python in a month. Try it! It is probably easier than you think.

More reasons why this fake story is fake:

This is posted on r/antiwork. It's one of the subreddits infamous for fake stories of bad bosses

It's a multipart series. One of the tropes of fake Reddit stories is the escalating updates. Creative writers hear an encore and they keep going back for more

It's a bit ambiguous how long this person has been in industry, but given the context of missing multiple children's school functions, I think 10+ years of experience is a cautious estimate. That is long enough in software for you to be a team lead, if not a people manager yourself. This person should be training junior devs. They explicitly say that they aren't

Software is incredibly collaborative. There's no way a manager would turn down an offer to train new devs on the existing tooling

Moreover there's no way the code got push to production without several eyes on it. Most companies do either code reviews or pair programming or both. It makes no sense that zero other people understand what's going on with this code. Unless it's really buggy

The fact that someone tried to use it and it corrupted a CSV file (??) shows that it's actually really buggy. If the software was so good, anyone would be able to run it

That goes double for the documentation being so bad that nobody knows how to read it. The entire purpose of documentation is to explain how code works. You failed at your one job.

If the only documentation is something that's hard to find, that looks bad on OOP for two reasons: 1) Documentation is normally put inline next to the code precisely for the reason that it would be easy to find. Don't want to see what a nightmare their code with no inline docs looks like. 2) Their programming practices are so bad that their other documentation is hard to find. The program should have a file called README that either has all the documentation or tells you where to find all the documentation.

This violates NDA so bad

"Out of compliance" for what? Which regulation? Why do they have a deadline to regain compliance? They should already be suffering whatever fines or consequences or whatever for already being out of compliance. It would make more sense if they were at risk of being out of compliance if they didn't implement XYZ by January

There's a lot of weird wording here that indicates a lack of familiarity with software: "complex API", "documentation library", "single threaded". That's not how we use those terms

If you're a software developer for a company the size of Disney (ABC's parent) then what OOP asked for is your starting salary straight out of undergrad. Def not a raise for a senior engineer who's been in industry 10+ years. Def not more than their manager is making.

At a company that size, your direct manager has no ability to decide what the terms of your hiring agreement would be. Def not over text. It would need to go through HR and probably legal as well

"Legal checked the contract and there's a clause stating" lmao get outta here!

[skillbabu]

How learning best python skill  can transform your career in 2025

In 2025, tech skills are evolving faster than ever — and Python has become the top programming language powering the future of artificial intelligence and machine learning. Whether you're a beginner or looking to upskill, learning Python for AI and ML could be the career move that sets you apart in this competitive job market.

Key benefits of learning python for AI & ML in 2025

Future-Proof Skill

As automation and AI become integral to every industry, Python fluency gives you a competitive edge in an AI-first world.

Beginner-Friendly Yet Powerful

You don’t need a computer science degree to learn Python. It’s perfect for non-tech professionals transitioning into tech careers.

Freelance and Remote Opportunities

Python developers working in AI and ML are in high demand on platforms like Upwork and Toptal many command salaries above six figures, working remotely.

Community and Resources

With massive open-source support, free tutorials, and active forums, you can learn Python for AI even without formal education.

Create roadmap: python for Ai and Machine learning

Master the Basics Start with variables, data types, loops, functions, and object-oriented programming in Python.

Understand Data Science Foundations Learn to work with Pandas, NumPy, and Matplotlib for data preprocessing and visualization.

Dive into Machine Learning Explore supervised and unsupervised learning using Scikit-learn, then graduate to TensorFlow and PyTorch for deep learning.

Build Real Projects Hands-on experience is key. Start building real-world applications like:

Spam email classifier

Stock price predictor

Chatbot using NLP

Why python is the best language for AI & Machine learning

Python's simplicity, vast libraries, and flexibility make it the best programming language for artificial intelligence. With intuitive syntax and community support, it's a favorite among data scientists, developers, and AI engineers.

✅ High-demand Python libraries in AI:

TensorFlow and Keras – deep learning models

Scikit-learn – machine learning algorithms

Pandas & NumPy – data analysis and manipulation

Matplotlib & Seaborn – data visualization

These tools allow developers to build everything from predictive models to smart recommendation systems  all using Python.

Career Opportunities After Learning Python for AI

If you're wondering how Python for AI and ML can shape your future, consider this: tech companies, startups, and even non-tech industries are hiring for roles like:

Machine Learning Engineer

AI Developer

Data Scientist

Python Automation Engineer

NLP (Natural Language Processing) Specialist

According to LinkedIn and Glassdoor, these roles are not just high-paying but are also projected to grow rapidly through 2030.

Best courses to learn python for Ai & ML in 2025

Google AI with Python (Free course on Coursera)

Python course With SKILL BABU  

IBM Applied AI Certification

Udemy: Python for Machine Learning & Data Science

Fast.ai Deep Learning Courses (Free)

These programs offer certifications that can boost your resume and help you stand out to employers.

Conclusion: Choose  Your Best Career with Python in 2025

If you’re looking to stay ahead in 2025’s job market, learning Python for AI and machine learning is more than a smart move , it’s a career game-changer. With endless growth opportunities, high-paying roles, and the chance to work on cutting-edge technology, Python opens doors to a future-proof tech career.

Start today. The future is written in Python.

[tccicomputercoaching]

Navigate the AI revolution! Discover essential computer skills – from Python to data literacy – you need to thrive in the future tech landscape. Learn how to stay relevant.

[attitudetallyacademy]

Master Website Development: Build Your Dream Site Today! 🌐

Learn From Scratch — Complete Course on How to Make a Website Start from the basics of HTML and CSS and work your way towards creating amazing functional websites utilizing advanced web design methods. 🚀 Learning curve will be with you, from beginner to advanced. Learn to build responsive, mobile-friendly websites and upgrade your career! 💻🌟

Visit Attitude Academy:-

Follow:- https://www.instagram.com/attitudeacademy4u/

for your valuable Training Course insights and content

Call: For Yamuna Vihar +91 9654382235 Or

For Uttam Nagar +91 9205122267 or

Visit Website: https://www.attitudetallyacademy.com

Email: career@attitudetallyacademy.com

[datasciencewithmohsin]

Understanding Outliers in Machine Learning and Data Science

In machine learning and data science, an outlier is like a misfit in a dataset. It's a data point that stands out significantly from the rest of the data. Sometimes, these outliers are errors, while other times, they reveal something truly interesting about the data. Either way, handling outliers is a crucial step in the data preprocessing stage. If left unchecked, they can skew your analysis and even mess up your machine learning models.

In this article, we will dive into:

1. What outliers are and why they matter.

2. How to detect and remove outliers using the Interquartile Range (IQR) method.

3. Using the Z-score method for outlier detection and removal.

4. How the Percentile Method and Winsorization techniques can help handle outliers.

This guide will explain each method in simple terms with Python code examples so that even beginners can follow along.

1. What Are Outliers?

An outlier is a data point that lies far outside the range of most other values in your dataset. For example, in a list of incomes, most people might earn between $30,000 and $70,000, but someone earning $5,000,000 would be an outlier.

Why Are Outliers Important?

Outliers can be problematic or insightful:

Problematic Outliers: Errors in data entry, sensor faults, or sampling issues.

Insightful Outliers: They might indicate fraud, unusual trends, or new patterns.

Types of Outliers

1. Univariate Outliers: These are extreme values in a single variable.

Example: A temperature of 300°F in a dataset about room temperatures.

2. Multivariate Outliers: These involve unusual combinations of values in multiple variables.

Example: A person with an unusually high income but a very low age.

3. Contextual Outliers: These depend on the context.

Example: A high temperature in winter might be an outlier, but not in summer.

2. Outlier Detection and Removal Using the IQR Method

The Interquartile Range (IQR) method is one of the simplest ways to detect outliers. It works by identifying the middle 50% of your data and marking anything that falls far outside this range as an outlier.

Steps:

1. Calculate the 25th percentile (Q1) and 75th percentile (Q3) of your data.

2. Compute the IQR:

{IQR} = Q3 - Q1

Q1 - 1.5 \times \text{IQR}

Q3 + 1.5 \times \text{IQR} ] 4. Anything below the lower bound or above the upper bound is an outlier.

Python Example:

import pandas as pd

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate Q1, Q3, and IQR

Q1 = df['Values'].quantile(0.25)

Q3 = df['Values'].quantile(0.75)

IQR = Q3 - Q1

# Define the bounds

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

# Identify and remove outliers

outliers = df[(df['Values'] < lower_bound) | (df['Values'] > upper_bound)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Values'] >= lower_bound) & (df['Values'] <= upper_bound)]

print("Filtered Data:\n", filtered_data)

Key Points:

The IQR method is great for univariate datasets.

It works well when the data isn’t skewed or heavily distributed.

3. Outlier Detection and Removal Using the Z-Score Method

The Z-score method measures how far a data point is from the mean, in terms of standard deviations. If a Z-score is greater than a certain threshold (commonly 3 or -3), it is considered an outlier.

Formula:

Z = \frac{(X - \mu)}{\sigma}

 is the data point,

 is the mean of the dataset,

 is the standard deviation.

Python Example:

import numpy as np

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate mean and standard deviation

mean = df['Values'].mean()

std_dev = df['Values'].std()

# Compute Z-scores

df['Z-Score'] = (df['Values'] - mean) / std_dev

# Identify and remove outliers

threshold = 3

outliers = df[(df['Z-Score'] > threshold) | (df['Z-Score'] < -threshold)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Z-Score'] <= threshold) & (df['Z-Score'] >= -threshold)]

print("Filtered Data:\n", filtered_data)

Key Points:

The Z-score method assumes the data follows a normal distribution.

It may not work well with skewed datasets.

4. Outlier Detection Using the Percentile Method and Winsorization

Percentile Method:

In the percentile method, we define a lower percentile (e.g., 1st percentile) and an upper percentile (e.g., 99th percentile). Any value outside this range is treated as an outlier.

Winsorization:

Winsorization is a technique where outliers are not removed but replaced with the nearest acceptable value.

Python Example:

from scipy.stats.mstats import winsorize

import numpy as np

Sample data

data = [12, 14, 18, 22, 25, 28, 32, 95, 100]

Calculate percentiles

lower_percentile = np.percentile(data, 1)

upper_percentile = np.percentile(data, 99)

Identify outliers

outliers = [x for x in data if x < lower_percentile or x > upper_percentile]

print("Outliers:", outliers)

# Apply Winsorization

winsorized_data = winsorize(data, limits=[0.01, 0.01])

print("Winsorized Data:", list(winsorized_data))

Key Points:

Percentile and Winsorization methods are useful for skewed data.

Winsorization is preferred when data integrity must be preserved.

Final Thoughts

Outliers can be tricky, but understanding how to detect and handle them is a key skill in machine learning and data science. Whether you use the IQR method, Z-score, or Wins

orization, always tailor your approach to the specific dataset you’re working with.

By mastering these techniques, you’ll be able to clean your data effectively and improve the accuracy of your models.