Python Tutorial: Why deal with missing data?

Want to learn more? Take the full course at https://learn.datacamp.com/courses/dealing-with-missing-data-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to the course on dealing with missing data in python. I'm Suraj Donthi, a Deep Learning and Computer Vision Consultant. While I specialize in solving computer vision problems like vision for self-driving cars, video analytics of traffic on roads, people analytics in retail and public spaces, or biomedical image analysis, I've also extensively worked on analyzing and back testing trading strategies using time-series data. In data science, the first and foremost task while working with any data for analysis is to clean the messy data. Almost all real world data is messy data and a large portion of it includes missing values. For instance, did you know that 72% of the organizations believe that data quality issues hinders their analysis, customer trust and perception! Values might go missing during the data acquisition process, whether it is due to faulty sensors or due to unfilled information by humans. Another prominent reason can be due to accidental data loss or deletion of records by ill-informed users. There can be several other reasons for missingness. In this course you will dig deep into analyzing the causes of missingness and appropriately treat them. This course will cover the significance of missing values, detecting missing values, analyzing the type of missingness and treating the missing values for all the frequently encountered data types namely, numerical, time-series and categorical values. Lastly, you'll learn the most important step in dealing with missing data which is imputing them. You'll learn both the simple techniques as well as advanced techniques to deal with missing data. Finally, you'll also learn to compare between various imputation techniques both statistically and visually. To be concise,