Data Science

📖 SQL ROADMAP

🔅 PREMIUM CHANNELS -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 Web Development -◦-◦--◦--◦-◦--◦--◦-◦-- 217k| 🔰 Linkedin Learning 143k| 🔰 Udemy Premium 132k| 🔰 Web Development -◦-◦--◦- 121k| 🔰 Python 3 097k| 🔰 JavaScript Training 091k| 🔰 Machine Learning -◦-◦--◦- 070k| 🔰 Data Analysis and Databases 068k| 🔰 Artificial Intelligence 064k| 🔰 Linux and DevOps -◦-◦--◦- 063k| 🔰 React and NextJs 049k| 🔰 100 Days of Python 049k| 🔰 OpenAI Mastery -◦-◦--◦- 049k| 🔰 Business and Finance 043k| 🔰 Best Telegram Channels 042k| 🔰 Udemy Learning -◦-◦--◦- 040k| 🔰 Zero to Mastery 040k| 🔰 Mobile Apps 036k| 🔰 Linkedin Learning Courses -◦-◦--◦- 035k| 🔰 Codedamn Courses 034k| 🔰 React 101 031k| 🔰 Coding Interview -◦-◦--◦- 030k| 🔰 Crypto Tutorials 025k| 🔰 Telegram's Shorts 024k| 🔰 The Coding Space -◦-◦--◦- 023k| 🔰 Linux Training -◦-◦--◦--◦-◦--◦--◦-◦-- 🔰 Add Your Channel -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 2hrs on top & 8hrs in channel!

To choose the right graph for data visualization, you should first understand your data and the message you want to convey Consider what you want to show (trends, comparisons, distributions, relationships, etc.) and then select a graph type that effectively communicates that information. 📝 Here's a breakdown of common chart types and their uses: 1. Showing Change Over Time: ⏳ • Line charts: Ideal for showing trends and patterns in continuous data over time. • Area charts: Useful for visualizing trends and showing the magnitude of change, especially when comparing multiple series. • Column/Bar charts: Can also be used to show trends, especially for discrete data or when comparing values across categories at specific points in time. 2. Comparing Values: ⚖️ • Bar charts: Excellent for comparing values across different categories, highlighting differences and outliers. • Column charts: Similar to bar charts but better for showing change over time or comparing categories, particularly when there are many categories or a large number of data points. • Pie charts: Best for showing the composition of a whole, especially when you have a small number of categories (ideally less than 5). • Scatter plots: Useful for examining relationships between two variables and identifying clusters or patterns. • Bubble charts: Expand on scatter plots by adding a third dimension (size of the bubble), allowing you to visualize relationships between three variables. 3. Showing Distribution: 📊 • Histograms: Show the distribution of a single variable, revealing how frequently different values occur. • Scatter plots: Can also be used to show the distribution of two variables simultaneously. • Box plots: Provide a visual summary of the distribution, showing the median, quartiles, and potential outliers. 4. Showing Relationships: 🔗 • Scatter plots: Best for exploring relationships between two variables. • Bubble charts: Can visualize relationships between three variables.

💡 How to grab a data analyst internship

🔅 Hands-On PostgreSQL Project: Spatial Data Science 📝 Learn how to perform advanced Spatial SQL operations, from setting up a local database to importing public data sets and running queries to perform spatial joins. 🌐 Author: Maggie Ma 🔰 Level: Intermediate ⏰ Duration: 1h 45m 📋 Topics: Data Manipulation, DBeaver, PostgreSQL 🔗 Join Data Science for more courses

📖 Must-Know Concepts in Data Science

📖 Must-Know Concepts in Data Science

Whether you’re building models, leading teams, or breaking into the field — there are a few core concepts you need to understand deeply (not just mention in interviews).

In this carousel, we break down: ✅ Supervised vs Unsupervised learning ✅ Overfitting & underfitting ✅ Cross-validation strategies ✅ Precision vs recall trade-offs ✅ Feature engineering techniques ✅ Dimensionality reduction methods

If you have knowledge — you can turn it into structured content in minutes No tools No setup No complexity Just describe your idea LUMILY uses AI to turn it into structured lessons and delivers it directly in Telegram Feels almost too easy 👉 Try live demo

🔰 Top 5 Clustering Techniques in Data Science

🔅 Introduction to PostgreSQL 📝 Get an introduction to PostgreSQL—what it is, what it can do, and how to start using it. 🌐 Author: Sarah Conway Schnurr 🔰 Level: Beginner ⏰ Duration: 48m 📋 Topics: PostgreSQL 🔗 Join Data Analysis for more courses

🔰 Math Topics every Data Scientist should know

🔗 YouTube Channels to learn Data Analysis

🔅 PREMIUM CHANNELS -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 Web Development -◦-◦--◦--◦-◦--◦--◦-◦-- 217k| 🔰 Linkedin Learning 143k| 🔰 Udemy Premium 132k| 🔰 Web Development -◦-◦--◦- 121k| 🔰 Python 3 098k| 🔰 JavaScript Training 091k| 🔰 Machine Learning -◦-◦--◦- 070k| 🔰 Data Analysis and Databases 068k| 🔰 Artificial Intelligence 064k| 🔰 Linux and DevOps -◦-◦--◦- 063k| 🔰 React and NextJs 050k| 🔰 100 Days of Python 049k| 🔰 OpenAI Mastery -◦-◦--◦- 049k| 🔰 Business and Finance 044k| 🔰 Best Telegram Channels 041k| 🔰 Udemy Learning -◦-◦--◦- 040k| 🔰 Zero to Mastery 040k| 🔰 Mobile Apps 036k| 🔰 Linkedin Learning Courses -◦-◦--◦- 035k| 🔰 Codedamn Courses 034k| 🔰 React 101 031k| 🔰 Coding Interview -◦-◦--◦- 031k| 🔰 Crypto Tutorials 025k| 🔰 Telegram's Shorts 024k| 🔰 The Coding Space -◦-◦--◦- 023k| 🔰 Linux Training -◦-◦--◦--◦-◦--◦--◦-◦-- 🔰 Add Your Channel -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 2hrs on top & 8hrs in channel!

Unlocking the power of data analysis starts with understanding its foundation. Dive deep with me into the most pivotal distributions every data scientist should have in their toolkit. From Gaussian to Binomial, knowing these distributions is a game-changer in the realm of Data Science.

🔅 Top Five Things to Know in Advanced SQL 📝 Learn advanced SQL concepts and practice them with hands-on exercises. 🌐 Author: Kendall Ruber 🔰 Level: Advanced ⏰ Duration: 1h 35m 📋 Topics: SQL 🔗 Join Data Analysis for more courses