Get yourself trained on IPython and Jupyter with this Online Training IPython and Jupyter Notebook In Practice: 3-in-1.
Online Training IPython and Jupyter Notebook In Practice: 3-in-1
Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and constitute an ideal gateway to the platform. This comprehensive 3-in-1 course is a practical, hands-on, example-driven tutorial to considerably improve your productivity during interactive Python sessions, and shows you how to effectively use IPython for interactive computing, data analysis, and data visualization. You will learn all aspects of of IPython, from the highly powerful interactive Python console to the numerical and visualization features that are commonly associated with IPython. You will also learn high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to write better and faster code. This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learning IPython for Interactive Computing and Data Visualization, begins with an introduction to Python language, IPython, and Jupyter Notebook. You will then learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel.The second 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.The third course, Statistical Methods and Applied Mathematics in Data Science, tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. You will be well versed with the standard methods in data science and mathematical modeling.By the end of this course, you will be able to apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College, London. He is a graduate of cole Normale Suprieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collge de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization. He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.