Get yourself trained on R Programming: From with this Online Training R Programming: From Statistics and Analysis to Data Science.
Online Training R Programming: From Statistics and Analysis to Data Science
Data is everywhere and growing faster than ever before! It has now become a challenge to deal with such huge amount of data as it is highly time-consuming. This has created a rising demand for people who can mine and interpret data. There is enormous value in data processing and analysis – and that is where a data scientist steps into the spotlight. R can help you work with the data you already have. You can do this by learning R data commands, exploring your data, aggregating the data into summary information, and visualizing the results to share with others.This comprehensive 2-in-1 course follows a step-by-step practical approach to getting grips with the basics of Reinforcement Learning with R and build your own intelligent systems. Initially, youll learn how to implement Reinforcement Learning techniques using the R programming language. Youll also learn concepts and key algorithms in Reinforcement Learning. Moving further, youll dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming. Finally, youll implement typical applications for model-based and model-free RL.Towards the end of this course, you’ll get insights into your data with R: Break down data, summarize information, and produce visually appealing plots to demonstrate powerful insights.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Programming for Data Science with R, covers statistics and analysis to full deep data science. This course will help candidates having basic knowledge of R Programming elevate to the next level. R can be used to tease actionable insights out of gigabytes of data, and this course will show you exactly how to do it. Here, we will be building on the advanced and efficient ways of doing different parts of analytics- right from data cleaning, visualizing to building high performing models Youll start your journey by loading data, visualizing it and interpreting it while providing intuitive solutions. Further, you will learn to apply machine learning algorithms to real-world problems in R. By the end of the course, get geared up to tackle real-life data challenges by analyzing complex datasets. This, in turn, will bring out insights that companies can convert into actions.The second course, Hands-On Data Exploration with R, covers exploring how R can provide insights to your data. This course will teach you how to put R to practical use in a world where decisions are data-driven. We start off by understanding how to prepare your data for analysis. You will learn how to organize data in a way that is easily workable. We will then explore data and understand how easy it is to gain insights from it by summarizing, aggregating, and visualizing data in R. By the end of this course, you will be equipped with the skills you need to explore a Retail, Telecom, or any other dataset handed to you, break down its key feature into easily digestible information, summarize this information, and produce visually appealing plots to demonstrate these insights.Towards the end of this course, you’ll get insights into your data with R: Break down data, summarize information, and produce visually appealing plots to demonstrate powerful insights.About the AuthorsRajat Jatana is a Data Scientist who is extremely passionate about Data Science. His area of specialization is Machine Learning, Predictive Analytics, R, Python, and Tableau. He has also equipped himself with Deep Learning specializations. He is a voracious reader and keeps himself up-to-date with the latest developments in Data Science. He is also passionate about teaching Data Science and believes that the best way to learn is by sharing it with others. Rajat likes to play chess in his free time and is a national level chess player too.Rahul Tiwari trains and consults organizations and individuals on Business Analytics, Data Science, and Machine Learning (Using R and Python). For 12 years, he has been helping students and organizations in various domains (such as retail, telecom, life sciences, finance, and more) solve their business problems using Data Science, Business Analytics, and Machine Learning. He has implemented machine learning algorithms in R extensively. He worked on various classification and regression models for his clients using R and Python. He has a sound knowledge of statistics as well, which is very much necessary for Data Science projects. After starting his career 12 years back in data warehousing, he moved on to the Data Science domain and held various roles. Mostly working with CTOs, key IT decision makers, and students, he has always focused on building capacity, knowledge, and solutions in Data Science, Business Analytics, and Machine Learning. He is a certified Tableau and Teradata associate. His core expertise is in R, Python, Tableau, Power BI, and SQL.
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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.