Structured Python Notes for Beginners

If you already know Python basics and you’re still writing slow, repetitive, and inefficient code… Then you’re not stuck You’ve just not learned Advanced Python Operations yet I just released a session on “Advanced Python Operations for Machine Learning Beginners” and this is where things start to separate beginners from serious builders. Because this is the stage where Python stops being “just code” And becomes a tool for speed, efficiency, and real-world problem solving ⸻ 1. You stop writing long code and start writing smart code At this level, it is not about doing more It is about doing things better You begin to use: List comprehensions Lambda functions Ternary operations Instead of writing 10 lines, you write 2 clean lines that do the same thing That is how professionals think ⸻ 2. You understand how Python actually works under the hood Most beginners use Python Few understand it Here you start learning: Iterators and generators Memory efficiency How loops really execute This is critical when working with large datasets especially in health research where data can be massive ⸻ 3. You master vectorization and stop using slow loops This is a big shift Instead of looping through data manually, you use: NumPy operations Pandas vectorization This alone can make your code 10x faster And in data science, speed is not luxury It is necessity ⸻ 4. You write reusable and scalable code Now you are thinking like a system builder You start using: Functions properly Modular code structure Reusable pipelines Because in research and machine learning, you will run the same process multiple times Efficiency matters ⸻ 5. You handle data like a pro At this stage, your data manipulation becomes sharper You are now comfortable with: Advanced filtering Groupby operations Merging and joining datasets Handling complex missing data This is the real backbone of machine learning work ⸻ 6. You begin to think in workflows, not just scripts Beginners write scripts Advanced users design workflows You now understand: Data pipeline thinking Step by step transformation Reproducibility in research And this is exactly what separates academic work from impactful research ⸻ 7. You move closer to real machine learning Advanced Python operations are the bridge Between: “I can code” And “I can build models that solve problems” Without this level, machine learning becomes guesswork With it, everything becomes structured and intentional Link https://lnkd.in/eK3CDZFT

Learning AI starts with Python. Period. Why? Super simple. It’s the language behind most AI tools and libraries, and it’s actually designed to be easy to read and write. That means you can focus on understanding how AI works instead of getting stuck on code. Want a high-quality, easy intro to Python? Choose smth from this list: 1. MongoDB University: MongoDB Python Developer Path https://lnkd.in/g7pPn-bw This learning path shows you how to use MongoDB with Python in real applications. You’ll learn the basics of the document model, how to perform CRUD operations, work with indexes and run aggregation queries, all while connecting your Python code through the PyMongo driver. Along the way, you’ll also get hands-on with tools like MongoDB Atlas and MongoDB Compass. The entire path is free and built for developers who want to start working with real databases. 2. Harvard: Introduction to Programming with Python A popular Python course that focuses entirely on programming fundamentals. It covers functions, conditionals, loops, OOP, file handling and testing. David Malan’s teaching style is clear and engaging, making it one of the best free introductions to Python. 3. Google for Developers: Google’s Python Class Originally created for Google’s internal training, this course is short, practical and easy to follow. It combines written materials, videos and exercises covering strings, lists, files and HTTP connections. Great for quickly strengthening your Python basics. 4. MIT OpenCourseWare: Introduction to Computer Science and Programming Using Python A more rigorous introduction to computer science using Python. The course explores algorithms, recursion and problem-solving while teaching programming fundamentals. Full MIT lectures, assignments and exams are available for free. 5. IBM Training: Python for Data Science This course focuses on using Python to work with data. It covers Python basics, data structures, and tools like Pandas and NumPy inside IBM’s interactive lab environment. Completing the course earns you a free IBM digital badge. 6. Microsoft Learn: Python for Beginners + Build Real World Applications with Python A structured set of learning paths that starts with Python fundamentals and gradually moves to real applications. You’ll learn concepts like unit testing, package management and working with APIs. All content is free on Microsoft Learn. Do you think it’s possible to have a solid understanding of AI without coding knowledge?

I need this and I am planning a trade with Jade Lizard on the company MongoDB that provides the free classes in Python. You can see my prior post in learning how to build an Agentic AI Trading system with cyber protection. MDB is the ticker for MongoDB.

Ranked: The World’s Most Popular Programming Languages (2014-2024): https://lnkd.in/eTuvYxpV. Our CIS faculty sometimes use the TOIBE index: https://lnkd.in/ebBmPFnP. It shows a different perspective but also shows that Python is popular. Is Python a must-have skill in the supply chain? https://lnkd.in/dcEJuZJX. How much Python do I need? https://lnkd.in/e6Rmzpw. Our CIS 2650 is not just a Python class, but instead a Python class for "analytics". I had a SCM professional ask: “I don’t even know basic python yet . I want to get to the more advanced items but I am not ready yet . I’ve done research into different courses but there are so many basic courses, I’m not sure which one is actually useful for someone looking to do supply chain analysis.” Instead of spending time fighting w/ language syntax/semantics & debugging programs, students in "Business" Analytics learn the best tools for the business problems at hand. Even our Python course is designed to be accessible to all business majors to solve problems that Excel falls short. I am not downplaying stats & computer languages. However, not all problems are programming problems. At the end of the day, solving business problems w/ the right tools for efficient decisions is usually valued more. Several years ago, we made this a req'd class for all SCM majors - CIS 2640 Predictive Data Analytics (Excel on steroids): I get this kind of feedback often…https://lnkd.in/dQABdsXc - You will also be glad to hear that (Anaconda + Jupyter Notebook + Python + visualization libraries) is what we have been teaching in CIS 2650 since the course was created. Our Business Analytics minor serves our Supply Chain majors very well. Our SCM students minor in Analytics by learning: 1. Advanced Excel (power query & pivot) & macros; 2. Data visualization (Tableau, Power BI & python w/ seaborn & matplotlib); 3. Data mining/RapidMiner, machine learning & data science; 4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy); 5. Relational data models (Excel data model); 6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly). https://lnkd.in/guvjPb_V You could make the very strong case that all of the above is the foundation for high AI literacy.

Why Python for bio/structure Learn Python once with free resources. Reuse it everywhere: sequence analysis, structural biology, data science, and AI – without leaving the open‑source ecosystem. If you work in bioinformatics or structural biology, Python isn’t a nice‑to‑have. It’s your toolbox – and you can get very far with free, open‑source tools only. Python quietly runs most “AI in biology”: parsing FASTA/GenBank, querying NCBI, automating BLAST, cleaning multi‑omics with NumPy/pandas, training ML models, and exploring 3D structures with Biopython’s Bio.PDB. You’re not “just learning to code” – you’re learning the language your data already speaks, deep diving beyond the bench. Here’s where to start, at every level, using only free and open resources: 🟢 Brand new to coding ·      Harvard CS50’s Introduction to Programming with PythonFull, university‑level course with clear explanations of variables, functions, loops, OOP, and testing. Entire lecture series is freely available to audit. ·      Microsoft Learn – “Python for Beginners”Browser‑based, step‑by‑step modules with short videos and interactive exercises. No setup headaches, ideal if you’ve never programmed before. 🟡 Already scripting (R/Matlab/bash) and want Python ·      Google’s Python ClassOriginally built for internal training, now fully open: written notes, videos, and exercises that move fast through core Python (strings, lists, dicts, files, HTTP). Great if you already think in scripts. ·      MIT OpenCourseWare – “Introduction to Computer Science and Programming in Python”Full MIT lectures, problem sets, and exams available free, with a focus on problem‑solving and algorithms using Python. 🔵 Want real CS depth and strong foundations ·      Stick with the MIT OCW course below and actually do the assignments. It forces you to think like a computer scientist, not just copy‑paste solutions. Then plug directly into biology and structure with free, open‑source tools: ·      Python for Biologists - Python taught through biological problems: sequences, file parsing, motif finding, simple pipelines - all explained for life scientists.  For more information, visit https://lnkd.in/eFpqttyW Set in PT Serif and Source Code Pro ·      Biopython (especially Bio.PDB) - Open‑source library for the whole stack: sequences, BLAST, NCBI queries, alignments, population genetics, plus structural biology via Bio.PDB (load PDBs, traverse chains/residues/atoms, annotate secondary structure, integrate DSSP).

Hello all... 🖐 Today's learning - Python for Mathematical Thinking - 1 Today we begin with the basics. This assumes that the basic knowledge of Python is already known. These examples may not be very useful for seniors or advanced learners, but they will definitely help those who want to revise their Python knowledge, revisit mathematical concepts, or start learning from scratch. 1. Finding the Square of a Number num = int(input("Enter a number: ")) square = num ** 2 print(f"The square of {num} is {square}")   This simple program takes a number as input and calculates its square using the exponent operator (**). 2. Finding the Cube of a Number Using a Function The code below is slightly different from the previous one. Here, we define a function first and then use it to calculate the cube of the given number. def cube(num):  """Calculates the cube of a number."""  return num ** 3 number = float(input("Please Enter any numeric Value: ")) cubed_number = cube(number) print(f"The Cube of the Given Number {number} = {cubed_number}")   Using functions makes the code more reusable and organized. 3. Finding the Factorial of a Number Using the Math Library In this example, we import the built-in math module in Python. This module contains many useful mathematical functions that allow us to perform calculations easily. import math num = int(input("Enter a number:")) if num < 0:   print("Sorry, factorial does not exist for negative numbers") else:   result = math.factorial(num)   print(f"The factorial of {num} is {result}")   These small exercises help build the foundation for solving larger mathematical and computational problems using Python. Thank you for reading... Have a nice day. 😊 Want to try these programs in Visual Studio Code? Follow the steps below: 1. Install Visual Studio Code. 2. Install the Python extension by pressing Ctrl + Shift + X and searching for Python. 3. Copy and paste the code from above. 4. Save the file with any name you like, but make sure it ends with .py (for example: program.py). 5. Open the Terminal in VS Code. 6. Type the command python your_filename.py and press Enter. 7. Now you can run the program and enjoy trying it with different numbers! #mathusingpython

Start learning Python by writing code. Not by watching tutorials. Not by saving playlists. Actually writing code. Because Python looks simple on the surface… but the real value comes when you start using it. Most people stop at basics like: print statements   loops   if-else  And then say “I know Python.” But real understanding starts when you go deeper. When you learn things like: • how data structures actually behave   • how functions organize logic   • how OOP helps structure real systems   • how APIs, files, and databases connect to code   • how automation and scripting solve real problems  That’s when Python starts becoming useful. This PDF is helpful because it doesn’t just show syntax. It walks through Python step-by-step — from fundamentals to real-world concepts like APIs, file handling, multithreading, and more. :contentReference[oaicite:0]{index=0} So instead of jumping between random tutorials, you can build understanding in one structured flow. A simple way to use it: 1. Pick one concept   2. Write code for it   3. Modify it and break it   4. Try to apply it in a small use case  That’s how skills actually stick. Because Python is not about knowing everything. It’s about being able to use it when needed. And that only happens through practice. Not passive learning. Save this sheet so you can revisit it while practicing. Comment "Python" and I’ll send the full PDF. Follow Sahil Hans for daily tech job openings and practical interview prep resources that make switching easier.

I know many of you, and even more if you're starting within one of many several branches of IT will surely appreciate this material as Python has been one of the main skills in IT. #StartingInIT #Python #ITForBeginners

Learning Python becomes much easier when you write small programs instead of only reading theory. Mini-projects help you understand logic, syntax, debugging, and problem-solving. Here are a few beginner programs you should practice 👇 *1️⃣ Simple Calculator* This program performs basic arithmetic operations. # Simple Calculator num1 = float(input("Enter first number: ")) num2 = float(input("Enter second number: ")) operation = input("Enter operation (+, -, *, /): ") if operation == "+": print("Result:", num1 + num2) elif operation == "-": print("Result:", num1 - num2) elif operation == "*": print("Result:", num1 * num2) elif operation == "/": if num2 != 0: print("Result:", num1 / num2) else: print("Cannot divide by zero") else: print("Invalid operation") 📌 Concepts Used - Input/output - Conditionals (if-elif-else) - Arithmetic operators *2️⃣ To-Do List App (Basic Version)* A simple program to add and view tasks. tasks = [] while True: print("\n1. Add Task") print("2. View Tasks") print("3. Exit") choice = input("Enter choice: ") if choice == "1": task = input("Enter a task: ") tasks.append(task) print("Task added!") elif choice == "2": print("\nYour Tasks:") for i, task in enumerate(tasks, start=1): print(i, task) elif choice == "3": print("Goodbye!") break else: print("Invalid choice") 📌 Concepts Used - Lists - Loops (while) - List operations (append) *3️⃣ Dice Roller* Simulates rolling a dice. import random while True: input("Press Enter to roll the dice...") number = random.randint(1, 6) print("You rolled:", number) again = input("Roll again? (yes/no): ") if again.lower() != "yes": break 📌 Concepts Used - random module - Loops - User input *4️⃣ Guess the Number Game* The computer generates a random number and the user guesses it. import random secret = random.randint(1, 10) while True: guess = int(input("Guess the number (1-10): ")) if guess == secret: print("🎉 Correct! You guessed the number.") break elif guess < secret: print("Too low!") else: print("Too high!") 📌 Concepts Used - Random numbers - Comparison operators - Loop logic *🎯 Why These Projects Matter* These mini projects help you practice: - Logic building - Debugging errors - Writing structured programs - Understanding real coding workflows Most Python beginners skip practice, but real learning happens when you write code daily. *💡 Pro Tip:* Try improving these programs by adding features like: - GUI using tkinter - Saving tasks to a file - Limiting guess attempts - Adding score tracking *Double Tap ♥️ For More*

Learning Python becomes much easier when you write small programs instead of only reading theory. Mini-projects help you understand logic, syntax, debugging, and problem-solving. Here are a few beginner programs you should practice 👇 *1️⃣ Simple Calculator* This program performs basic arithmetic operations. # Simple Calculator num1 = float(input("Enter first number: ")) num2 = float(input("Enter second number: ")) operation = input("Enter operation (+, -, *, /): ") if operation == "+": print("Result:", num1 + num2) elif operation == "-": print("Result:", num1 - num2) elif operation == "*": print("Result:", num1 * num2) elif operation == "/": if num2 != 0: print("Result:", num1 / num2) else: print("Cannot divide by zero") else: print("Invalid operation") 📌 Concepts Used - Input/output - Conditionals (if-elif-else) - Arithmetic operators *2️⃣ To-Do List App (Basic Version)* A simple program to add and view tasks. tasks = [] while True: print("\n1. Add Task") print("2. View Tasks") print("3. Exit") choice = input("Enter choice: ") if choice == "1": task = input("Enter a task: ") tasks.append(task) print("Task added!") elif choice == "2": print("\nYour Tasks:") for i, task in enumerate(tasks, start=1): print(i, task) elif choice == "3": print("Goodbye!") break else: print("Invalid choice") 📌 Concepts Used - Lists - Loops (while) - List operations (append) *3️⃣ Dice Roller* Simulates rolling a dice. import random while True: input("Press Enter to roll the dice...") number = random.randint(1, 6) print("You rolled:", number) again = input("Roll again? (yes/no): ") if again.lower() != "yes": break 📌 Concepts Used - random module - Loops - User input *4️⃣ Guess the Number Game* The computer generates a random number and the user guesses it. import random secret = random.randint(1, 10) while True: guess = int(input("Guess the number (1-10): ")) if guess == secret: print("🎉 Correct! You guessed the number.") break elif guess < secret: print("Too low!") else: print("Too high!") 📌 Concepts Used - Random numbers - Comparison operators - Loop logic *🎯 Why These Projects Matter* These mini projects help you practice: - Logic building - Debugging errors - Writing structured programs - Understanding real coding workflows Most Python beginners skip practice, but real learning happens when you write code daily. *💡 Pro Tip:* Try improving these programs by adding features like: - GUI using tkinter - Saving tasks to a file - Limiting guess attempts - Adding score tracking *Double Tap ♥️ For More*