Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations as it is also a powerful tool for interactive data exploration, visualization and has become the standard tool among data scientists. This course is a step-by-step guide to exploring the possibilities in the field of Jupyter. You will first get started with data science to perform various task such as data exploration to visualization, using the popular Jupyter Notebook, along with this you will also learn how Python 3, R, and Julia can be integrated with Jupyter for various data science. Then you will learn data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, plotly & work with some real datasets. Along with this, you will also learn to create insightful visualizations, showing time-stamped and spatial data. Finally, you will master relatively advanced methods in interactive numerical computing, high-performance computing, and data visualization. By the end of this course, you will comfortably leverage the power of Jupyter to perform various data science tasks efficiently. Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Jupyter for Data Science gets you started with data science using the popular Jupyter Notebook. If you are familiar with Jupyter Notebook and want to learn how to use its capabilities to perform various data science tasks, this video course is for you! From data exploration to visualization, this course will take you every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter’s features to share your documents and codes with your colleagues. The course also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this course, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully. The second course, Jupyter Notebook for Data Science will help you get familiar with Jupyter Notebook and all of its features to perform various data science tasks in Python. Jupyter Notebook is a powerful tool for interactive data exploration and visualization and has become the standard tool among data scientists. In the course, we will start with basic data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, and plotly. We will work with real datasets, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create insightful visualizations, showing time-stamped and spatial data. By the end of the course, you will feel confident about approaching a new dataset, cleaning it up, exploring it, and analyzing it in Jupyter Notebook to extract useful information in the form of interactive reports and information-dense data visualizations. The third course, Interactive Computing with Jupyter Notebook covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. In short, you will master relatively advanced methods in interactive numerical computing, high-performance computing, and data visualization. About the Authors: Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies of all sizes, in roles from sole contributor to VP/CTO level. For the last 10 years or so, he has been contracting companies in the eastern Massachusetts area under Dan Toomey Software Corp. Dan has also written the R for Data Science and Learning Jupyter books for Packt Publishing. Draen Luanin is a developer, data analyst, and the founder of Punk Rock Dev, an indie web development studio. He’s been building web applications and doing data analysis in Python, JavaScript, and other technologies professionally since 2009. In the past, Draen worked as a research assistant and did a Ph.D. in computer science at the Vienna University of Technology. There he studied the energy efficiency of geographically distributed data centers and worked on optimizing VM scheduling based on real-time electricity prices and weather conditions. He also worked as an external associate at the Ruer Bokovi Institute, researching machine learning methods for forecasting financial crises. During Draen’s scientific work Python, Jupyter Notebook (back then still IPython Notebook), Matplotlib, and Pandas were his best friends over many nights of interactive manipulation of all sorts of time series and spatial data. Draen also did a Master’s degree in computer science at the University of Zagreb. Cyrille Rossant, Ph.D, is a neuroscience researcher and software engin
