In Pandas Data Cleaning and Modeling with Python LiveLessons, Daniel Y. Chen builds upon the foundation he built in Pandas Data Analysis with Python Fundamentals LiveLessons. In this LiveLesson Dan teaches you the techniques and skills you need to know to be able to clean and process your data. Dan shows you how to do data munging using some of the built-in Python libraries that can be used to clean data loaded into Pandas. Once your data is clean you are going to want to analyze it, so next Dan introduces you to other libraries that are used for model fitting. Daniel Y. Chen is a graduate student in the interdisciplinary Ph.D. program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech.

Data Science with Python and R LiveLessons is tailored to beginner data scientists seeking to use Python or R for data science. This course includes fundamentals of data preparation, data analysis, data visualization, machine learning, and interactive data science applications. Students will learn how to build predictive models and how to create interactive visual applications for their line of business using the Anaconda platform. This course will introduce data scientists to using Python and R for building on an ecosystem of hundreds of high performance open source tools. The companion Jupyter notebooks for these LiveLessons are available at https://anaconda.org/datasciencepythonr.

Advanced R LiveLessons, Part I teaches R programmers techniques for dealing with large data, both in memory and in databases. It also covers advanced machine learning, network analysis, web graphics, and documents and presentations. In this video training Jared starts with reading XML data and some common data manipulation operations using various base R functions and packages like plyr, comparing the speed of in memory calculations. He then demonstrates more advanced techniques for accomplishing the same task such as data.table, He then turns to some other useful advanced concepts and techniques.

Advanced R LiveLessons, Part I teaches R programmers techniques for dealing with large data, both in memory and in databases. It also covers advanced machine learning, network analysis, web graphics, and documents and presentations. In this video training Jared starts with reading XML data and some common data manipulation operations using various base R functions and packages like plyr, comparing the speed of in memory calculations. He then demonstrates more advanced techniques for accomplishing the same task such as data.table, He then turns to some other useful advanced concepts and techniques.