Learn how you can capture trends and make sense out of time series data with the help of a moving or rolling average. In this tutorial, you'll learn how to implement power applications like Prisma using TensorFlow 2.0. Explore basic math concepts for data science and deep learning such as scalar and vector, determinant, singular value decomposition, and more. Learn about histograms and how you can use them to gain insights from data with the help of matplotlib. In this basic Python tutorial, you'll learn about why and when you should use inner classes. In this tutorial, you will get an overview of how to use the INDEX-MATCH function in spreadsheets. Many Python users are familiar with using asterisks for multiplication and power operators, but in this tutorial, you'll find out additional ways on how to apply the asterisk. Learn about PCA and how it can be leveraged to extract information from the data without any supervision using two popular datasets: Breast Cancer and CIFAR-10. To be an adept data scientist, one must know how to deal with many different kinds of data. Learn to read various formats of data like JSON and HTML using pandas. Go through the ten most important updates introduced in the newly released TensorFlow 2.0, and learn how to implement some of them. Learn how to use spreadsheets to calculate an inventory demand model that helps determine how much inventory a business should hold moving forward. In this tutorial, you will learn why it is important to pre-process data and how to do it in pandas. In this tutorial, learn how to parse one or more arguments from the command-line or terminal using the getopt, sys, and argparse modules. Learn how to apply operations like add, subtract, divide, multiply, and a lot more in Google Spreadsheets with the help of an actual dataset. Learn how to utilize conditional formatting with one or multiple conditions and how to color cells by custom criteria with the help of a dataset.