Look at the top-left menu or bottom bar. Look for an app called “Terminal” (black icon with > or _). You can also: Right-click on the desktop → click Open Terminal Or press Ctrl + Alt + T to open it with a shortcut ❓Still not seeing the terminal? Please let me know: Are you in a Jupyter notebook, Experimental Desktop, or the main Huawei Cloud dashboard? What is the name of the lab you are doing? Once you tell me that, I’ll give you the exact clicks you need. You said: after creating the obs bucket it is saying that open terminal from right side destop i am not understanding this option ChatGPT said: Thanks for the clarification — you're doing a Huawei Cloud lab, and it's asking you to "open Terminal from the right-side desktop." Let me explain exactly what that means: ✅ What “Open Terminal from Right Side Desktop” Means In Huawei Cloud ModelArts labs, especially in KooLabs or AI training labs, after you launch the lab, it opens a special virtual desktop (Experimental Desktop) in your browser. That desktop has a split view: Left side: Instructions or the web browser (for logging in to the Huawei Cloud) Right side: A Linux desktop environment (like a virtual machine) 🖥️ How to Open the Terminal on the Right-Side Desktop Look at the right side of your lab screen. It looks like a Linux virtual desktop. You'll see a taskbar at the top or bottom (like in Ubuntu or CentOS). On that right-side virtual desktop, find and click the “Terminal” icon. The icon looks like this: 🖥️ or like a black window with a > or _. It may be on the desktop itself or in the top-left “Applications” menu. If you can’t find it: Right-click anywhere on the desktop → Click “Open Terminal” Or try double-clicking a Terminal icon on the desktop Or click the "Applications" or "System Tools" menu → select "Terminal"

1. MUHAMMAD SARFRAZ

2. PYTHON
And Its Applications

3. • Introduction
• Features
• Hello world :)
• More Features
• Applications for Python
• Applications of Python
• Python Frameworks
• Python JOBs
Agenda

4. What is Python?
Python is a versatile, high-level programming language known for its simplicity and
readability, making it ideal for beginners and professionals alike. It supports multiple
paradigms, including procedural, object-oriented, and functional programming, and is
widely used in web development, data science, artificial intelligence, and more.
Features
• Uses an elegant syntax, making programs easier to read.
• Easy-to-use language.
• Comes with a large standard library that supports many common programming tasks,
such as:
• Connecting to the web,
• Searching text with regular expressions,
• Reading and modifying files.
• Runs on multiple platforms, including macOS, Windows, Linux, and Unix.
Introduction

5. Why We Say Python syntax is so
simple Hello World
C++
#include <iostream>
using namespace std;
int main() {
cout << "Hello, World!" << endl;
return 0;
}
Java
public class HelloWorld {
public static void
main(String[] args) {
System.out.println("Hello,
World!");
}
}
Python
print("Hello, World!")

6. Key Features of Python
2.
Easy to Learn and Use
• Simple English-like syntax makes it beginner-friendly.
• Faster to grasp and implement than many other languages.
Free and Open-Source
• Free to use, modify, and redistribute under an OSI-approved license.
• Ideal for companies needing customized versions.
Rapid Development
• Highly versatile, enabling experimentation and quick prototyping.
Interpreted Language
• Executes code line by line, halts on errors, and simplifies debugging.
Extensive Libraries and Frameworks
• Comes with a vast standard library, reducing reliance on external tools.
Dynamically Typed
• Automatically assigns data types during execution—no need for manual
declaration.
Portable
• Write once, run anywhere (avoiding system-dependent features).
Strong Community Support
• Large, active community with guides, videos, and documentation for all skill
levels.
1.
4.
3.
5.
6.
7.
8.

7. Real-World
Applications of
Python

8. Extract knowledge and insights from structured and unstructured data.
Core Components:
•Data Collection – Gathering raw data from various sources.
•Data Cleaning – Removing inconsistencies and preparing
data.
•Data Analysis – Using statistics and tools to find patterns.
•Data Visualization – Representing data graphically for
insights.
•Machine Learning – Building predictive models.
•Decision Making – Using insights to guide business or
research.
Tools & Languages:
•Python, R, SQL
•Pandas, NumPy, Scikit-
learn
•Tableau, Power BI,
Matplotlib
Applications of Data Science:
•Healthcare (predictive
diagnosis)
•Finance (fraud detection)
•Marketing (customer
segmentation)
•Transportation (route
optimization)
•Sports (performance analytics)
DATA SCIENCE:

9. AI is the simulation of human intelligence processes by machines,
especially computer systems.
Core Components:
• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Robotics
• Expert Systems
Tools & Languages:
• Python, TensorFlow
• Keras, PyTorch
• OpenCV, spaCy
• Scikit-learn
Applications of Data Science:
• Virtual Assistants
(e.g., Siri, Alexa)
• Autonomous Vehicles
• Medical Diagnosis
• Fraud Detection
• Recommendation Systems
Artificial Intelligence:

11. Instagram - Backend infrastructure and data processing in the popular
social media platform.
YouTube - Video transcoding, content recommendations, and analytics.
NASA - Scientific computing, data analysis, and simulation tasks.
Spotify - Data analysis, recommendation systems, and backend services
in the music streaming platform.
Dropbox - Building and maintaining the file hosting service's server
infrastructure.
Google - Web crawling, data analysis, and automation.

12. Applications
• Data Visualization
• Artificial Intelligence (AI)
• Machine Learning (ML)
• Deep Learning
• Data Analytics
• Computer Vision(CV)
Additional Applications
• Game Development
• Internet of Things (IoT)
• Network Programming
• Robotics
• Natural Language
Processing (NLP)
• Desktop GUI Development
• Tools like PyQT and Kivy
make GUI creation
efficient and secure.
• Web Development
• Frameworks like Django
and Flask power major
platforms like Spotify and
Reddit.
• Audio and Visual
Applications
• Libraries like OpenCV
and Pyo support apps
like Netflix and
YouTube.
• CAD Applications
• Tools like Blender and
FreeCAD enable
precise 2D/3D modeling
for designers.

14. 4. Network Programming:
Twisted, Tornado
5. API Development:
FastAPI, Hug, Falcon
6. Robotics and IoT: ROSPy
(Robot Operating System for
Python)
7. Game Development:
Pygame, Panda3D, Cocos2d
8. Data Visualization and
Dashboards: Dash, Bokeh,
Plotly
9. Machine Learning and AI:
TensorFlow, PyTorch, Scikit-
learn
10. Web Scraping: Scrapy,
BeautifulSoup
Python Frameworks
1. Web Development: Django,
Flask, Pyramid, FastAPI, CherryPy,
Bottle, Falcon
2. Desktop Applications: PyQt,
Kivy, wxPython
3. Mobile Applications: Kivy,
BeeWare

16. Thank you!
Q&A

17. Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to
extract knowledge and insights from structured and unstructured data.
•Data Collection – Gathering raw data from various sources.
•Data Cleaning – Removing inconsistencies and preparing data.
•Data Analysis – Using statistics and tools to find patterns.
•Data Visualization – Representing data graphically for insights.
•Machine Learning – Building predictive models.
•Decision Making – Using insights to guide business or research.