Get yourself trained on Data Visualization in with this Online Training Data Visualization in Python by Examples.
Online Training Data Visualization in Python by Examples
Data visualization is just a wise investment in your future big-data needs. You will learn how to deploy maps and networks to display geographic and network data. To do this, we will focus on the following very popular libraries in Python: matplotlib, ggplot, seaborn, and plotly. 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. We’ll take a look at chart types, such as Matplotlib for visualizing the impact of tornadoes in the US, North Korean nuke tests on global stocks, and analyze forex performances using charts. You will see how ggplot can be used to analyze trends in BRICS economies and crude oil price trends. You will see how to level up your data visualization skills using Python’s advanced plotting libraries: matplotlib and Seaborn, and how you can present the data from the most unstable regions in the world through data visualization. You will then carry out a visual analysis of the performance of various Hollywood releases. Finally, you will use Plotly to plot comparative graphs of Apple iPhone version releases and compare the performance of gaming consoles such as Xbox and PlayStation.About the AuthorHarish 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.
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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.