#Data Analysis Python Notebook Visualization

Relaxing Python & Pygame Creations #coding #programming

Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly resources: 1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course 2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist 3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples. Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together. Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊

Visualizing real YouTube data with Python and it’s easier than you think 📊✨ This @codedex.io tutorial is the perfect starting point if you’re getting into data viz! 💡Tools: Python, Plotly Express, dataset used from Kaggle - #python #dataviz #coding #learntocode #code

Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to move from spreadsheets to web-based visualisations ✨ Covers tools like Google Sheets, Datawrapper, Tableau Public, Chart.js & Leaflet ✨ Perfect if you want to communicate data clearly — even without heavy coding ✨Open-source so freely available online 📌 Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code — Jack Dougherty & Ilya Ilyankou 💭 Summary: This book shows you how to clean, analyse, and visualise data using practical tools — starting with spreadsheets and moving into customisable web-based charts and maps. It’s especially useful if you want to share your work online and make your data interactive, not just informative. If you’re learning data science, bioinformatics, or just want to present your work better, this is a great place to start 🤍 📌 Save this for later — I’ll be sharing more recommendations soon. #womeninstem #datavisualization #datascience #bioinformatics #tech

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These 3 YouTubers taught me Python better than my entire degree. Comment “guide” to get the free interview guide with 40+ data interview questions (and a bonus on how to learn and study for an interview) #data #student #tech

Top 3 data visualization packages 🤔 1️⃣ https://matplotlib.org 2️⃣ https://seaborn.pydata.org 3️⃣ https://ggplot2.tidyverse.org Being able to visualize and tell the story is a key component of being a data scientist. With these 3 packages you can pretty much create any plot you can think of! Not only that, but these packages aren’t actually that bad to learn! There are many other packages out there for data visualization as well, but these are my 3 favorites! Drop a follow for more coding tips 🎯 #code #coding #datascience #tech #python

Dive into the captivating world of data visualization with ‘Seeing Theory.’ 🌐 Explore the art and science of visualizing data, making numbers come alive! 📈✨ Follow @thedataevangelist for more such content #dataanalyst #datascience #datavisualization #visualizations

Comment "Link" to get the links! You Will Never Struggle With Data Structures & Algorithms Again 🔗 Explore these free visualization tools: 1️⃣ visualgo.net 2️⃣ cs.usfca.edu 3️⃣ csvistool.com Stop memorizing code blindly. See every algorithm in action — arrays, linked lists, stacks, queues, trees, graphs, sorting, searching, and more. These interactive platforms show step-by-step exactly how data flows and how operations work. Whether you’re preparing for coding interviews, studying computer science, or just starting with DSA, this is the fastest way to master the fundamentals. Save this, share it, and turn complex algorithms into simple visuals you’ll never forget.

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10 years with Python. I've watched this language quietly become the default across almost every technical field. Not because it's the fastest. Not because of syntax debates. Because it meets people where they are — and the ecosystem is unmatched. Think about what a single AI project touches today: 📊 Data: NumPy, Pandas, Polars 🤖 ML: Scikit-learn, XGBoost, LightGBM 🧠 Deep Learning: PyTorch, TensorFlow, JAX 📈 Tracking: MLflow, Weights & Biases 🎨 Visualization: Matplotlib, Plotly, Altair 🚀 Serving: FastAPI, BentoML, Gradio, Streamlit ⚙️ MLOps: Airflow, Prefect, Kubeflow, Dagster 🔧 Features: Featuretools, tsfresh ✅ Validation: Evidently AI, Deepchecks 🔐 Security: Presidio, PySyft 40+ battle-tested libraries. 10 categories. One language. Python didn't win because of hype. It won because practitioners chose it — day after day, project after project. If you're building in AI today, Python isn't optional. It's infrastructure. What Python tool has had the biggest impact on your workflow? Drop it below 👇

Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!