𝐂𝐥𝐚𝐬𝐬 𝟖 :𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬 𝐏𝐚𝐫𝐭 𝟏/𝟑 | 𝐏𝐲𝐭𝐡𝐨𝐧 𝐄𝐃𝐀 𝐅𝐮𝐥𝐥 𝐂𝐨𝐮𝐫𝐬𝐞 | 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

🛠️ 𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐭𝐨 𝐂𝐥𝐚𝐬𝐬 𝟕 𝐨𝐟 𝐨𝐮𝐫 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞!

In this lesson, we begin our deep dive into Handling Missing Values—an essential aspect of data preprocessing. Understanding how to manage missing data is critical for accurate data analysis, and in this video, we will explore the foundational techniques to do so efficiently using Python.

🔍 What You’ll Learn:

1.Identifying Missing Data: Learn how to detect and understand the scope of missing values in your datasets.
2. Basic Techniques for Handling Missing Values: Discover methods such as deletion, mean/mode imputation, and more.
3. Exploring Pandas Functions: Hands-on with Pandas library functions to handle missing data effectively.
4. Real-World Examples: Apply these techniques to real datasets to see how they work in practice.
5. Setting Up for Advanced Techniques: Get ready for more complex methods in the upcoming classes.

📈 𝐖𝐡𝐲 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬?

Properly handling missing data is crucial for maintaining the integrity of your analysis. Whether you're working with CSV files, SQL databases, or data from APIs, missing values can distort your results if not addressed correctly. Mastering these skills will set the foundation for your success in data science and analytics.

📝 Course Structure: This video is the first part of our series on handling missing values, part of the broader "Data Collection and Preprocessing with Python" course. Be sure to check out our previous classes to build a strong foundation in data handling techniques.

🚀 Next Steps: Subscribe and click the notification bell to stay updated with our latest tutorials! Don't hesitate to leave your questions in the comments or share your experiences with handling missing data.

📚 Resources: #MissingValues #DataPreprocessing #PythonData #PythonCourse #DataScience #Pandas #DataAnalysis #PythonForBeginners #DataCleaning #PythonTutorial