Get yourself trained on Data visualization and with this Online Training Data visualization and Descriptive Statistics with Python 3.
Online Training Data visualization and Descriptive Statistics with Python 3
This course is designed to teach analysts, students interested in data science, statisticians, data scientists how to analyze real world data by creating professional looking charts and using numerical descriptive statistics techniques in Python 3. You will learn how to use charting libraries in Python 3 to analyze real-world data about corruption perception, infant mortality rate, life expectancy, the Ebola virus, alcohol and liver disease data, World literacy rate, violent crime in the USA, soccer World Cup,migrants deaths, etc.You will also learn how to effectively use the various statistical libraries in Python 3 such as numpy, scipy.stats, pandas and statistics to create all descriptive statistics summaries that are necessary for analyzing real world data.In this course, you will understand how each library handles missing values and you will learn how to compute the various statistics properly when missing values are present in the data.The course will teach you all that you need to know in order to analyze hands on real world data using Python 3. You will be able to appropriately create the visualizations using seaborn, matplotlib or pandas libraries in Python 3.Using a wide variety of world datasets, we will analyze each one of the data using these tools within pandas, matplotlib and seaborn:Correlation plotsBox-plots for comparing groups distributionsTime series and lines plotsSide by side comparative pie chartsAreas chartsStacked bar chartsHistograms of continuous dataBar chartsRegression plotsStatistical measures of the center of the dataStatistical measures of spread in the dataStatistical measures of relative standing in the dataCalculating Correlation coefficientsRanking and relative standing in dataDetermining outliers in datasetsBinning data in terciles, quartiles, quintiles, deciles, etc.The course is taught using Anaconda Jupyter notebook, in order to achieve a reproducible research goal, where we use markdowns to clearly document the codes in order to make them easily understandable and shareable.This is what some students are saying:”I really like the tips that you share in every unit in the course sections. This was a well delivered course.””I am a Data Scientist with many years using Python /Big Data. The content of this course provides a rich resource to students interested in learning hands on data visualization in Python and the analysis of descriptive statistics. I will recommend this course anyone trying to come into this domain.”
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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.