Get yourself trained on Python: Data Visualization with this Online Training Python: Data Visualization with Python: 2-in-1.
Online Training Python: Data Visualization with Python: 2-in-1
Effective visualization helps you get better insights from your data, make better and more informed business decisions. Python is a favorite tool for programmers and data scientists because its easy to learn, and the extensive list of built-in features and importable libraries contribute to increased productivity. To do this, we will focus on the following very popular libraries in Python: matplotlib, ggplot, seaborn, and plotly. This comprehensive 2-in-1 course is a practical tutorial to help you determine different approaches to data visualization, and how to choose the most appropriate one for your needs. It will help you use data visualization as your preferred business reporting tool. Adds impact to your data by representing information in the form of a chart, diagram, pictures, and so on. This will also help you deploy plots and charts using various data visualization tools in Python. Contents and Overview This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Data Visualization in Python by Examples, covers Data visualization with matplotlib, ggplot, and seaborn in Python. In this course, you will walk through some of the fundamentals of data visualization, sharing many examples of how to handle different types of data and how best to present your insights. Finally, youll use Plotly to plot comparative graphs of Apple iPhone version releases and compare the performance of gaming consoles such as Xbox and PlayStation. The second course, Python Data Visualization Solutions, covers creation of attractive visualizations using Pythons most popular libraries. This video starts by showing you how to set up matplotlib and other Python libraries that are required for most parts of the course, before moving on to discuss various widely used diagrams and charts such as Gantt Charts. As youll go through the course, youll get to know about various 3D diagrams and animations. As maps are irreplaceable to display geo-spatial data, this course will show you how to build them. In the last section, youll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python. By the end of this training program youll be able to create effective visualizations for your data sets using tools: matplotlib, ggplot, seaborn and plotly in Python. About the AuthorsHarish Garg is a data scientist and a lead software developer with 17 years’ software Industry experience. He worked for McAfeeIntel for 11+ years before starting his own software consultancy. He is an expert in creating Data visualizations using R, Python, and Web based visualization libraries. Dimitry is a data scientist with a background in applied mathematics and theoretical physics. After completing his physics undergraduate studies in ENS Lyon (France), he studied fluid mechanics at cole Polytechnique in Paris where he obtained first Class class Masters degree. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data-scientist for a smart-energy start-up in Cambridge, in close collaboration with the university. Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualisation in Python on his blog.Igor Milovanovi is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
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Investing in yourself through Learning
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.