Get yourself trained on Learn By Example: with this Online Training Learn By Example: Statistics and Data Science in R.
Online Training Learn By Example: Statistics and Data Science in R
Taught bya Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.This course is a gentle yet thorough introduction toData Science, Statistics and R using real life examples.Lets parse that.Gentle, yet thorough:This course does not require a prior quantitative or mathematics background. It starts by introducingbasic conceptssuch asthe mean, median etc and eventually covers all aspects of an analytics (or)data science career from analysing and preparing raw data to visualising your findings.Data Science, Statistics and R:This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.Real life examples:Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics andrange from A/Btesting in an Internet company context to the Capital Asset Pricing Model in a quant finance context.What’s Covered:Data Analysis with R:Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting &Merging Data FramesLinear Regression:Regression, SimpleLinear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plotsData Visualization in R:Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map,Packages for Data Visualisation :Rcolorbrewer, ggplot2Descriptive Statistics:Mean, Median, Mode, IQR,Standard Deviation, Frequency Distributions, Histograms, BoxplotsInferential Statistics:Random Variables, Probability Distributions, Uniform Distribution,Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance
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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.