Data Science

SQL

SQL is way easier when you actually know what matters. These are the core basics every beginner needs to build projects, answer real business questions, and stop feeling overwhelmed 📊 Master these first and everything else becomes 10x easier. Save this to review later ✅

📖 SQL Learning Roadmap — 8 Key Steps: 1. Basic: Start by understanding what SQL is, why it’s used, and the different types of SQL commands (DDL, DML, DCL, TCL). This builds your foundation. 2. Queries: Learn how to fetch data using commands like SELECT, FROM, WHERE, ORDER BY, and LIMIT. Practice filtering data with operators such as =, !=, LIKE, IN, and BETWEEN. 3. Joins: To work with multiple tables, you must understand INNER, LEFT, RIGHT, and FULL OUTER JOIN. Know how primary and foreign keys relate tables. 4. Functions: Use built-in functions like COUNT, SUM, AVG, MIN, and MAX for data analysis. Learn how to group data using GROUP BY and filter groups with HAVING. 5. Subqueries: Write queries within queries! Learn scalar, correlated, and multi-row subqueries. They’re powerful for solving complex data problems. 6. Data Manipulation: Master how to change data using INSERT, UPDATE, and DELETE. Also, understand transactions using BEGIN, COMMIT, and ROLLBACK to maintain data integrity. 7. Advanced: Take it further with window functions (ROW_NUMBER, RANK, LEAD, LAG), CTEs (WITH), views, and indexing to write efficient and optimized queries. 8. Practice: The final step is consistent practice. Work with real-world datasets, focus on query optimization, and solve challenges on platforms like LeetCode, Mode, or HackerRank.

📖 4 Main in Database Types

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🔅 The 80/20 Rule of Data Science 📝 Explore the core concepts of the 80/20 rule for data science and how to get most of the value with minimal effort. 🌐 Author: Howard Friedman 🔰 Level: Intermediate ⏰ Duration: 1h 26m 📋 Topics: Data Science, Project Engineering, Team Management 🔗 Join Data Science for more courses

📖 Data Visualisation CheatSheet

📖 Data Analyst

📖 MOST COMMON SQL INTERVIEW QUESTION Do you knew this? 👉 “What is the order of execution in an SQL query?” Don’t let the SELECT fool you – it’s NOT the first step! 😮 Here’s the correct order that SQL follows behind the scenes: 🔢 SQL Order of Execution: 1️⃣ FROM 2️⃣ JOIN 3️⃣ WHERE 4️⃣ GROUP BY 5️⃣ HAVING 6️⃣ SELECT 7️⃣ DISTINCT 8️⃣ ORDER BY 9️⃣ LIMIT / OFFSET 🔥 Pro tip: Interviewers LOVE this question to test your SQL fundamentals! Memorize it, understand it – and impress in your next interview. 💼

🔅 PostgreSQL Essential Training 📝 Learn how to set up and work with one of the worlds most popular open-source database platforms, PostgreSQL. 🌐 Author: Adam Wilbert 🔰 Level: Intermediate ⏰ Duration: 3h 18m 📋 Topics: PostgreSQL 🔗 Join Data Science for more courses

💡 Data Structures, you need to know for Coding interview

#meme

🚦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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🔅 Complete Guide to Python for Data Engineering: From Beginner to Advanced 📝 Practice fundamental skills using Python for data engineering in this hands-on, interactive course with coding challenges in CoderPad. 🌐 Author: Deepak Goyal 🔰 Level: Advanced ⏰ Duration: 5h 28m 📋 Topics: Data Engineering, Python 🔗 Join Data Science for more courses