Tools and Techniques in Data Science | 9th Computer - Chapter 9 - Lec 18

In this video, we will learn about Tools and Techniques in Data Science, an important topic from 9th Class Computer Science – Chapter 9: Data Science and Data Gathering. Data science depends on powerful tools and methods that help us collect, manage, analyse, and visualize data to make better decisions. 👉 Key points covered in this lecture: 🔹 Data Science Tools: Excel: Used to organise data and create graphs, e.g., showing progress of test scores over time. Python: A popular programming language with libraries like Pandas (data analysis) and Matplotlib (charts/graphs). R: A language mainly used for statistics and creating visualizations from experiments and surveys. SQL: A database language that helps retrieve specific information, e.g., finding all students who scored above 90 in a test. 🔹 Data Science Techniques: Predictive Modelling: Forecasting future outcomes using past data, e.g., predicting students who may need extra help in studies. Graph Analytics: Analysing connections between data points, e.g., finding influential users on social media through their network of friends. 🔹 Applications of Data Science Techniques: Retail: Understanding shopping patterns, like people buying coats and gloves together. Healthcare: Predicting diseases by analysing patient history for better treatment. Finance: Detecting fraud by noticing unusual spending activities. Sports: Improving team performance by analysing player statistics and strategies. 📚 Class: 9th Class Computer Science 📖 Chapter: Data Science and Data Gathering 🎯 Topic: Tools and Techniques in Data Science By the end of this video, you will clearly understand the major tools (Excel, Python, R, SQL), techniques (Predictive Modelling, Graph Analytics), and their real-world applications in retail, healthcare, finance, and sports. ✨ Don’t forget to Like, Share, and Subscribe for more Computer Science lectures! #ToolsInDataScience #TechniquesInDataScience #DataScienceApplications #9thClassComputer #DataScienceAndDataGathering