Get yourself trained on Python Data Visualization: with this Online Training Python Data Visualization: 2-in-1.
Online Training Python Data Visualization: 2-in-1
Python is an easy to learn, powerful programming language. Pythons elegant syntax and dynamic typing, together with its interpreted nature, makes it an ideal language for data visualization which is a wise investment for your future big-data needs. If you’re a Python user who wishes to enter the field of data visualization or enhance your data visualization skills to become more effective visual communicator, then this learning path is for you.This comprehensive 2-in-1 course is designed to teach you the advanced techniques in data visualization to create an interesting and impactful analysis of your data sets using bqplot, NetworkX, Bokeh, and Dash. This course aims to excite you with awesome visualizations focused on the analysis of some very interesting data sets. You will get well-versed with Python data visualization concepts that gets you up and running in no time. 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, will walk you through some of the fundamentals of data visualization, sharing many examples of how to handle different types of data and explaining you the best way to present your insights. You will then be glanced through 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 also see how ggplot can be used to analyze trends in BRICS economies and crude oil price trends. Next, you will level up your data visualization skills using Python’s advanced plotting libraries such as matplotlib and Seaborn. 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. The second course, Data Visualization Projects in Python, starts off with programming stunning interactive data visualizations using bqplot, an open source Python library developed by Bloomberg. You will then learn how to programmatically create interactive network graphs and visualizations. You will also learn to programmatically visualize data with the interactive Python visualization library, Bokeh. Next, you will learn how to build interactive web visualizations of data using Python wherein you will choose a number of inputs your users can control and then use dash library to create plots based on those inputs. By the end of this Learning Path, you will be able to demonstrate visualizations with interesting, real-world data sets, and a useful blend of ideas to sharpen your skills in data visualization. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Harish Garg is a data scientist and a lead software developer with 17 years of experience in software Industry. 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.