Get yourself trained on Applied Statistics and with this Online Training Applied Statistics and Data Preparation with Python.
Online Training Applied Statistics and Data Preparation with Python
This is the bite size course to learn Python Programming for Applied Statistics. In CRISP DM data mining process, Applied Statistics is at the Data Understanding stage.This course also covers Data processing, which is at the Data Preparation Stage. You will need to know some Pythonprogramming, and you can learn Python programming from my “Create Your Calculator: Learn Python Programming Basics Fast” course. You will learn Python Programming for applied statistics. You can take the course as follows, and you can take a exam at EMHAcademy to get SVBook Certified Data Miner using Python certificate :- Create Your Calculator: Learn Python Programming Basics Fast (R Basics)- Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)- Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation, in future)- Machine Learning with Python (Modeling and Evaluation)ContentGetting StartedGetting Started 2Getting Started 3Data Mining ProcessDownload Data setRead Data setModeMedianMeanRangeRange One ColumnQunatileVarianceStandard DeviationHistogramQQPLotShapiro TestSkewness and KurtosisDescribe()CorrelationCovarianceOne Sample T TestTwo Sample TTestChi Square TestOne Way ANOVASimple Linear RegressionMultiple LInear RegressionData Processing:DF.head()Data Processing:DF.tail()Data Processing:DF.describe()Data Processing:SelectVariablesData Processing:Select RowsData Processing:Select Variables and RowsData Processing:Remove VariablesData Processing:AppendRowsData Processing:Sort VariablesData Processing:Rename VariablesData Processing:GroupBYData Processing:Remove Missing ValuesData Processing:Is THere Missing ValuesData Processing:Replace Missing ValuesData Processing:Remove Duplicates
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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.