Abdul Muqsit’s Post

🐍 Python dominates data science in 2026, but success isn't just about knowing the language—it's about mastering the RIGHT libraries. After working with countless datasets and models, I've identified the 5 essential Python libraries every data scientist needs in their toolkit: 📊 Pandas - Data manipulation powerhouse 🔢 NumPy - Numerical computing foundation   📈 Matplotlib/Seaborn - Visualization storytelling 🤖 Scikit-learn - Machine learning workhorse 🚀 Polars - The speed game-changer 💡 Pro tip: Don't just learn syntax—understand WHEN to use each tool. What's YOUR essential Python library? 👇 #DataScience #Python #MachineLearning #DataAnalytics #AI #DataScientist #PythonProgramming #Analytics

🚴♂️ End-to-End Machine Learning Project Predicted bike-sharing demand using historical data and weather data. 🔧 Performed Data Preprocessing Feature Engineering Model Training Cross-Validation Hyperparameter Tuning → to find the best performing model 🌐 Deployment Final model deployed as a web application for real-time predictions. 💻 Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Flask/Streamlit 📌 #MachineLearning #DataScience #Python #AI #MajorProject

Turning financial statements into visual insights 📊 Used Python, Pandas, Seaborn, and Matplotlib to reshape the data and visualize Equity Capital, Reserves, Deposits, and Total Assets over the years. Converting wide data into long format and plotting it makes trends much clearer than raw numbers. When you can see the growth, you understand the story better. #Python #DataVisualization #Pandas #Seaborn #Matplotlib #FinancialAnalysis #LearningByDoing

In this project, I performed data cleaning, visualization, and statistical exploration to better understand feature relationships such as sepal length, sepal width, petal length, and petal width across different species. Using Python libraries like Pandas, Matplotlib, and Seaborn in Google Colab, I generated insights through summary statistics and visual plots. This exercise strengthened my understanding of data preprocessing, visualization techniques, and pattern identification — key steps before building any machine learning model. #DataScience #EDA #Python #MachineLearning #GoogleColab #IrisDataset

📊 Day 13/90 — Creating Powerful Visuals with Seaborn Yesterday we learned basic visualization. Today we level up using Seaborn, a Python library that helps create more professional and insightful charts. ✅ Today’s Focus: • What is Seaborn & why analysts use it • Creating attractive statistical charts • Visualizing relationships between variables • Understanding distributions & trends 🎯 Why this matters: Seaborn makes it easier to discover patterns and present insights in a professional, presentation-ready format. 📌 Practice Tip: Try this in Python: import seaborn as sns import matplotlib.pyplot as plt data = [12, 15, 14, 10, 18, 20, 17] sns.histplot(data) plt.show() Better visuals → clearer insights → stronger impact. 💬 Comment “DAY 13” if you’re learning with me. #DataAnalytics #Seaborn #DataVisualization #Python #LearningInPublic #90DaysChallenge

Gradient Descent explained — with live, runnable Python code. 🐍 I built this interactive notebook that walks through all 3 variants: 📌 Batch Gradient Descent 📌 Stochastic Gradient Descent (SGD) 📌 Mini-Batch Gradient Descent Each one is implemented from scratch using NumPy, with cost function plots so you can literally see the model learning. 🔗 Open the notebook here (no sign-up needed): https://lnkd.in/dKwuP6FU --- This notebook was built on sciFI — an AI-powered Python notebook workspace. The AI copilot wrote the code, fixed the errors, and helped structure the whole thing. I just described what I wanted. If you work with data and Python, it's worth a look 👇 🌐 https://scifi.ink — free beta, no credit card. #DataScience #MachineLearning #Python #GradientDescent #AI #sciFI

 Python is still the king of data + AI — but it’s not just about pandas anymore. These tools changed how I build in 2026: 🔸 Polars – 10× faster than pandas, lower RAM. Built on Rust. A no-brainer for big data. 🔸 FastAPI + Pydantic – Blazing fast APIs, auto-validating schemas, and async support. 🔸 Rich + Typer – Want beautiful CLIs? You need these. 🔸 Ruff + Black + Pyrefly – Lint, format, and type-check at warp speed. ⚡ Bonus: Tools like uv and RightTyper make env and typing management effortless. 👉 Python’s ecosystem isn’t just powerful — it’s lightning fast now. 💬 What’s one Python tool you discovered recently that you now can’t live without? #Python #DataScience #DevTools #FastAPI #OpenSource #Productivity

Numbers alone don’t explain much. Charts make data easy to understand 📊 Data visualization helps us spot trends, compare values, and explain insights clearly to others. Today I learned how different charts are used: • Bar charts for comparison • Line charts for trends • Pie charts for proportions • Scatter plots for relationships This is Day 6 of my Python + Data Analytics learning series. One step closer to real-world analytics 🚀 #DataVisualization #Python #Matplotlib #Seaborn #DataAnalytics #LearningInPublic

🚀 Unlock the Power of Data Analysis with Python Ready to turn raw data into real insights? Python is the tool that makes it happen. Python is one of the most popular languages for data analysis because it’s simple, powerful, and incredibly flexible. With libraries like Pandas, NumPy, and Matplotlib, you can clean data, uncover trends, and visualize results that actually support smarter decisions. From finance and healthcare to marketing and AI, Python helps professionals transform data into impact faster and more efficiently. 💬 Your turn: What’s your favorite Python library for data analysis, and how are you using it in your work? #Python #DataAnalysis #DataScience #Analytics #LearningPython #TechCareers

📊 From raw data to real insights — powered by Python 🐍 As a Data Analyst, Python isn’t just a tool for me — it’s a thinking partner. From: ✔️ Cleaning messy datasets ✔️ Exploring patterns with Pandas & NumPy ✔️ Visualizing insights using Matplotlib / Seaborn ✔️ Writing efficient logic that turns data into decisions Python helps me move beyond what happened to why it happened and what’s next. What I love most? Data + Python = clarity, automation, and impact 🚀 Every dataset has a story. Python helps me tell it—clearly and confidently. #DataAnalytics #Python #DataAnalyst #SQL #AnalyticsJourney #LearningEveryday #WomenInTech #CareerGrowth #DataDriven