Supriya Darisa’s Post

Python Learning Plan |-- Week 1: Introduction to Python | |-- Python Basics | | |-- What is Python? | | |-- Installing Python | | |-- Introduction to IDEs (Jupyter, VS Code) | |-- Setting up Python Environment | | |-- Anaconda Setup | | |-- Virtual Environments | | |-- Basic Syntax and Data Types | |-- First Python Program | | |-- Writing and Running Python Scripts | | |-- Basic Input/Output | | |-- Simple Calculations | |-- Week 2: Core Python Concepts | |-- Control Structures | | |-- Conditional Statements (if, elif, else) | | |-- Loops (for, while) | | |-- Comprehensions | |-- Functions | | |-- Defining Functions | | |-- Function Arguments and Return Values | | |-- Lambda Functions | |-- Modules and Packages | | |-- Importing Modules | | |-- Standard Library Overview | | |-- Creating and Using Packages | |-- Week 3: Advanced Python Concepts | |-- Data Structures | | |-- Lists, Tuples, and Sets | | |-- Dictionaries | | |-- Collections Module | |-- File Handling | | |-- Reading and Writing Files | | |-- Working with CSV and JSON | | |-- Context Managers | |-- Error Handling | | |-- Exceptions | | |-- Try, Except, Finally | | |-- Custom Exceptions | |-- Week 4: Object-Oriented Programming | |-- OOP Basics | | |-- Classes and Objects | | |-- Attributes and Methods | | |-- Inheritance | |-- Advanced OOP | | |-- Polymorphism | | |-- Encapsulation | | |-- Magic Methods and Operator Overloading | |-- Design Patterns | | |-- Singleton | | |-- Factory | | |-- Observer | |-- Week 5: Python for Data Analysis | |-- NumPy | | |-- Arrays and Vectorization | | |-- Indexing and Slicing | | |-- Mathematical Operations | |-- Pandas | | |-- DataFrames and Series | | |-- Data Cleaning and Manipulation | | |-- Merging and Joining Data | |-- Matplotlib and Seaborn | | |-- Basic Plotting | | |-- Advanced Visualizations | | |-- Customizing Plots | |-- Week 6-8: Specialized Python Libraries | |-- Web Development | | |-- Flask Basics | | |-- Django Basics | |-- Data Science and Machine Learning | | |-- Scikit-Learn | | |-- TensorFlow and Keras | |-- Automation and Scripting | | |-- Automating Tasks with Python | | |-- Web Scraping with BeautifulSoup and Scrapy | |-- APIs and RESTful Services | | |-- Working with REST APIs | | |-- Building APIs with Flask/Django | |-- Week 9-11: Real-world Applications and Projects | |-- Capstone Project | | |-- Project Planning | | |-- Data Collection and Preparation | | |-- Building and Optimizing Models | | |-- Creating and Publishing Reports

Python's Pro Tips : 🚀 What I learned building my first real Python dashboard (no one talks about this) Everyone talks about: 👉 Pandas 👉 Plotly 👉 AI tools But the real breakthrough for me was NOT libraries… It was understanding how Python actually thinks: 💡 1. Indentation = Logic Not formatting. Not style. 👉 One wrong space = broken program. GOLDEN RULE 👉 Every block must follow: if something:    code (4 spaces)        deeper code (8 spaces)à  if file: → 0 spaces inside it → 4 spaces inside if len(...) → 8 spaces   🔒 BRACKETS Every: ( → must close ) [ → must close ] { → must close } WHY 0 / 4 / 8 SPACES? Python doesn’t use { } like JS… 👉 it uses INDENTATION = STRUCTURE 🔥 YOUR 3 if STATEMENTS (EXPLAINED CLEAN) ✅ LEVEL 1 (0 spaces) if file: 👉 This is TOP LEVEL 👉 Means: “only run everything below IF file is uploaded” ✅ LEVEL 2 (4 spaces)    if categorical_cols: 👉 This is INSIDE if file 👉 Means: “only run this IF file exists AND categorical columns exist” ✅ LEVEL 3 (8 spaces)        if selected_click != "None": 👉 This is INSIDE both above 👉 Means: “only run if: file exists categorical exists user selected something” 🎯 SIMPLE WAY TO SEE IT Think like this: IF file exists:    DO A    IF categorical exists:        DO B        IF user clicked:            DO C 📊 VISUAL TREE (VERY IMPORTANT) if file:                   ← level 0    df = ...    if categorical_cols:   ← level 4        selected_click = ...        if selected_click != "None":  ← level 8            filtered_df = ... 💡 2. Execution Order matters 👉 You can’t use a variable before it exists. 👉 “Create → then use” (simple, but critical) 💡 3. Scope is everything If something is inside: if file: 👉 it ONLY exists there. 💡 4. Structure > Syntax You can know all libraries… But without structure, nothing works. 💡 5. Build → Break → Fix → Repeat That’s the real learning loop. Not tutorials. 🔥 After struggling with these, I built: ✔ Dynamic dashboard (multi-KPI) ✔ Power BI–style filtering ✔ Multi-chart engine ✔ RCA (insight layer coming next) 📌 Biggest takeaway: Stop chasing tools. Learn how things flow. Everyone learns in different way for me is outside à So first understand the whole picture (Structure) then see what section goes where and each section have what scripts inside it and how they structured (Spacing…..) That’s when everything clicks. Have difficult undertesting it I can explain in Mental Model: “Python Rooms & Floors” (only the first section if the image _Having the right Spacing_ kept me confused for while) :-) Next step: 📡 Turning this into a 💎 A commercial-grade telecom intelligence platform. Let’s see where this goes 🚀 Thanks for your impressions, feedbacks & comments

Stop drowning in Python tutorials. 🛑 Most people fail Data Science not because they lack content, but because they lack order. Here is the 7-step roadmap to mastery (Start learning withe the DS roadmap https://lnkd.in/gKDjNVkg): 1️⃣ Python Fundamentals (The "Practical" Only) Don’t learn everything. Just the essentials: Variables & Data Types Loops & Logic Functions File Handling 2️⃣ NumPy (Performance Layer) The backbone of ML. Master: Vectorized operations Array manipulation Slicing & Indexing 3️⃣ Pandas (The Workhorse) 🐎 90% of your job is here. Focus on: DataFrames & Series Handling missing values Groupby, Merge, & Pivot tables 4️⃣ Visualization (The Storytelling) Insights are useless if you can't show them: Matplotlib (The basics) Seaborn (Statistical plots) 5️⃣ EDA (The Data Scientist Mindset) Start asking "Why": Summary statistics Correlations & Outliers Distribution patterns 6️⃣ Real-World Data (Beyond Notebooks) Connect to the real world: SQL + Python (Crucial!) APIs & Web Scraping Small-scale Data Pipelines 7️⃣ Build & Ship (The Portfolio) Stop "learning," start "building": Sales trends dashboard Customer churn analysis Automated data cleaning scripts The Shortcut? There isn't one. Just the right sequence. [https://prachub.com/] Why most people fail? They jump to Step 7 before mastering Step 3. Or they get stuck in "Tutorial Hell" at Step 1. My Advice: Learn 20% of the syntax. Build 80% of the time. Which step are you currently on? Let’s discuss in the comments! 👇

Day 12 of My Data Science Journey — Python Lists: Methods, Comprehension & Shallow vs Deep Copy Today’s focus was on one of the most essential data structures in Python — Lists. From data storage to manipulation, lists are used everywhere in real-world applications and data science workflows. 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: List Properties – Ordered, mutable, allows duplicates, and supports mixed data types Accessing Elements – Used indexing, negative indexing, slicing, and stride for flexible data access List Methods – append(), extend(), insert() for adding elements – remove(), pop() for deletion – sort(), reverse() for ordering – count(), index() for searching and analysis Shallow vs Deep Copy – Understood that direct assignment does not create a new copy – Used copy(), list(), slicing for safe duplication – Learned the importance of copying, especially with nested data List Comprehension – Wrote concise and efficient code using list comprehension – Combined loops and conditions in a single readable line Built-in Functions – Used sum(), len(), min(), max() for quick data insights Additional Useful Methods – clear(), sorted(), zip(), filter(), map(), any(), all() 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Understanding how lists work — especially copying and comprehension — is critical for writing efficient and bug-free Python code. Lists are not just a data structure; they are a core tool for solving real-world problems. Read the full breakdown with examples on Medium 👇 https://lnkd.in/gFp-nHzd #DataScienceJourney #Python #Lists #Programming

✅ *🐍 Python Roadmap: Quick Recap for Beginners to Advanced* 📘👨💻 | |── *1️⃣ Basics of Python* | ├── Installing Python, Using IDLE/Jupyter/VS Code | ├── `print()`, Comments, Indentation | └── Data Types: int, float, string, bool | |── *2️⃣ Variables & Operators* | ├── Variable assignment & naming rules | ├── Arithmetic, Comparison, Logical operators | └── Type casting (int → str etc.) | |── *3️⃣ Control Flow* | ├── `if`, `elif`, `else` | ├── `for` and `while` loops | └── `break`, `continue`, `pass` | |── *4️⃣ Functions* | ├── `def`, Parameters, Return | ├── `*args`, `**kwargs` | └── Lambda functions | |── *5️⃣ Data Structures* | ├── List, Tuple, Set, Dictionary | ├── CRUD operations & Methods | └── List comprehension | |── *6️⃣ File Handling* | ├── `open()`, read, write, append | ├── Working with text & CSV files | └── Using `with` context manager | |── *7️⃣ Exception Handling* | ├── `try`, `except`, `finally`, `raise` | └── Custom exceptions | |── *8️⃣ Object-Oriented Programming (OOP)* | ├── Class, Object, `_init_()` | ├── Inheritance, Polymorphism, Encapsulation | └── Class vs Instance methods | |── *9️⃣ Modules & Libraries* | ├── `import`, built-in modules (`math`, `random`, `datetime`) | ├── External: `numpy`, `pandas`, `matplotlib`, `requests` | └── Creating your own module | |── *🔟 Advanced Concepts* | ├── Decorators, Generators | ├── Iterators, `zip()`, `enumerate()` | ├── Virtual environments & pip | └── Regular expressions (`re` module) | |── *🔁 Bonus: Practice Areas* | ├── Web scraping with `BeautifulSoup` or `Selenium` | ├── Flask or Django for Web Dev | ├── Data Analysis with Pandas | └── Automation scripts with `os`, `shutil`, `schedule`

✅ *🐍 Python Roadmap: Quick Recap for Beginners to Advanced* 📘👨💻 | |── *1️⃣ Basics of Python* | ├── Installing Python, Using IDLE/Jupyter/VS Code | ├── `print()`, Comments, Indentation | └── Data Types: int, float, string, bool | |── *2️⃣ Variables & Operators* | ├── Variable assignment & naming rules | ├── Arithmetic, Comparison, Logical operators | └── Type casting (int → str etc.) | |── *3️⃣ Control Flow* | ├── `if`, `elif`, `else` | ├── `for` and `while` loops | └── `break`, `continue`, `pass` | |── *4️⃣ Functions* | ├── `def`, Parameters, Return | ├── `*args`, `**kwargs` | └── Lambda functions | |── *5️⃣ Data Structures* | ├── List, Tuple, Set, Dictionary | ├── CRUD operations & Methods | └── List comprehension | |── *6️⃣ File Handling* | ├── `open()`, read, write, append | ├── Working with text & CSV files | └── Using `with` context manager | |── *7️⃣ Exception Handling* | ├── `try`, `except`, `finally`, `raise` | └── Custom exceptions | |── *8️⃣ Object-Oriented Programming (OOP)* | ├── Class, Object, `_init_()` | ├── Inheritance, Polymorphism, Encapsulation | └── Class vs Instance methods | |── *9️⃣ Modules & Libraries* | ├── `import`, built-in modules (`math`, `random`, `datetime`) | ├── External: `numpy`, `pandas`, `matplotlib`, `requests` | └── Creating your own module | |── *🔟 Advanced Concepts* | ├── Decorators, Generators | ├── Iterators, `zip()`, `enumerate()` | ├── Virtual environments & pip | └── Regular expressions (`re` module) | |── *🔁 Bonus: Practice Areas* | ├── Web scraping with `BeautifulSoup` or `Selenium` | ├── Flask or Django for Web Dev | ├── Data Analysis with Pandas | └── Automation scripts with `os`, `shutil`, `schedule`

✅ *🐍 Python Roadmap: Quick Recap for Beginners to Advanced* 📘👨💻 | |── *1️⃣ Basics of Python* | ├── Installing Python, Using IDLE/Jupyter/VS Code | ├── `print()`, Comments, Indentation | └── Data Types: int, float, string, bool | |── *2️⃣ Variables & Operators* | ├── Variable assignment & naming rules | ├── Arithmetic, Comparison, Logical operators | └── Type casting (int → str etc.) | |── *3️⃣ Control Flow* | ├── `if`, `elif`, `else` | ├── `for` and `while` loops | └── `break`, `continue`, `pass` | |── *4️⃣ Functions* | ├── `def`, Parameters, Return | ├── `*args`, `**kwargs` | └── Lambda functions | |── *5️⃣ Data Structures* | ├── List, Tuple, Set, Dictionary | ├── CRUD operations & Methods | └── List comprehension | |── *6️⃣ File Handling* | ├── `open()`, read, write, append | ├── Working with text & CSV files | └── Using `with` context manager | |── *7️⃣ Exception Handling* | ├── `try`, `except`, `finally`, `raise` | └── Custom exceptions | |── *8️⃣ Object-Oriented Programming (OOP)* | ├── Class, Object, `_init_()` | ├── Inheritance, Polymorphism, Encapsulation | └── Class vs Instance methods | |── *9️⃣ Modules & Libraries* | ├── `import`, built-in modules (`math`, `random`, `datetime`) | ├── External: `numpy`, `pandas`, `matplotlib`, `requests` | └── Creating your own module | |── *🔟 Advanced Concepts* | ├── Decorators, Generators | ├── Iterators, `zip()`, `enumerate()` | ├── Virtual environments & pip | └── Regular expressions (`re` module) | |── *🔁 Bonus: Practice Areas* | ├── Web scraping with `BeautifulSoup` or `Selenium` | ├── Flask or Django for Web Dev | ├── Data Analysis with Pandas | └── Automation scripts with `os`, `shutil`, `schedule` 💬 *Double Tap ❤️ for more!*

Python Libraries One library can save you 5 hours. The wrong one can cost you 5 days. That is the real Python skill no one teaches. You do not need to master every Python library. You need to know exactly which one solves the problem in front of you. Here are the top Python libraries every data professional should know in 2026 👇 ✅ NumPy ↳ Fast numerical computations, array and matrix operations, base for scientific computing. ✅ Pandas ↳ Data cleaning, transformation, handling CSV/Excel/SQL, analysis with DataFrames. ✅ Matplotlib ↳ Basic data visualisation, static charts (line, bar), quick exploratory plots. ✅ SciPy ↳ Scientific computations, statistical functions, optimisation tasks. ✅ Scikit-learn ↳ Machine learning models, classification and regression, clustering and preprocessing. ✅ TensorFlow ↳ Deep learning models, production-scale deployment, neural network training. ✅ PyTorch ↳ Flexible deep learning, research and experimentation, dynamic model building. ✅ PySpark ↳ Big data processing, distributed computing, handling large datasets. ✅ Jupyter Notebook ↳ Interactive coding, data exploration, visualisation + notes in one place. ✅ SQLAlchemy ↳ Database ORM, query using Python, multi-database support. ✅ FastAPI ↳ High-performance APIs, ML model deployment, async support. ✅ Flask ↳ Lightweight web apps, simple API creation, quick model serving. ✅ Plotly ↳ Interactive charts, dashboards, real-time visualisation. ✅ Selenium ↳ Browser automation, scraping dynamic sites, UI testing. ✅ BeautifulSoup ↳ Web scraping basics, HTML parsing, extracting structured data. Here is the truth, you do not become a better data professional by learning more libraries. You become better by knowing when to reach for each one. Save this. Revisit it the next time you are stuck picking the right tool. Which library do you use most? 👇 ♻️ Repost to help another data pro sharpen their Python toolkit. 🔔 Follow for more ♻️ I share cloud , data analysis/data engineering tips, real world project breakdowns, and interview insights through my free newsletter. #python #developer #softwaredevelopment