Data Science

💡 Data Structures, you need to know for Coding interview

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🚦Top 10 Data Science Tools🚦 Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data . Here is the list of some useful Data Science Tools that are normally utilized : 1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text . 2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability. Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization. 3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning. 4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning. 5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively. 6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly. 7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts. 8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets. 9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem. 10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.

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📖 SQL ROADMAP

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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