Statistical Thinking and Data Science with R.

Statistical Thinking and Data Science with R. - R from A-Z! Statistics, Advanced Regression,Visualizations, Probabilities, Inference, Simulations and Machine learning.

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What you'll learn

How To use statistics to Make Business decisions.
Learn R from Scratch and Become Excellent in it!
Fundamentals of Probability.
Continuous and Discrete Distributions Properties.
How to fit distributiions.
How to make Business simulations.
Hypothesis Testing for different business problems.
Regression models understanding and inference.
Measuring the relative risk, odds and odds ratio of choices.
Making data driven decisions
Cleaning, manipulation and Visualization of data.
Feature selection and regularized regression models.
Binomial and multinomial logistic regression models magic!
How to detect and remove outliers.
Measures of spread and centrality.
The use of Bayesian analysis to estimate distributions.

Description

not only you learn R in this course, but you also learn how to use statistics and machine learning to make decisions!!!

It's been six years since I moved from Excel to R and since then I have never looked back! With eleven years between working in Procurement, lecturing in universities, training over 2000 professionals in supply chain and data science with R and python, and finally opening my own business in consulting for two years now. I am extremely excited to share with you this course. My goal is that all of you become experts in R, statistical thinking, and Machine learning. I have put all the techniques I have learned and practiced in this one sweet bundle of data science with R.

By the end of this course you will be able to :

Learn R from scratch.

What are probabilities? random experiments, random variables, and sample space?

How can we detect the outliers in data?.

How can we make our resources efficient using statistics and data?

How can we test a hypothesis that a supplier is providing better products than another supplier?

How can we test the hypothesis that a marketing campaign is significantly better than another marketing campaign?

What is the effect of the last promotion on the increase in sales?

How can we make simulations to understand what is the expected revenue coming from a business?

how can we build machine learning models for classification and regression using statistics?

what are the log odds, odds ratio, and probabilities produced from logistic regression models?

What is the right visualization for categorical and continuous data?

How to Capture uncertainty with Distributions? What is the right distribution that fits the data best?

Apply machine learning to solve problems.

Do you face one of these questions regularly? well then, this course will serve as a guide for you.

Statistics & Probabilities are the driving force for many of the business decisions we make every day. if you are working in finance, marketing, supply chain, product development, or data science; having a strong statistical background is the go-to skill you need.

Although learning R is not the main focus of this course, but we will implicitly learn R by diving deep into statistical concepts. The Crucial advantage of this course is not learning algorithms and machine learning but rather developing our critical thinking and understanding what the outcomes of these models represent.

The course is designed to take you to step by step in a journey of R and statistics, It is packed with templates, Exercises, quizzes, and resources that will help you understand the core R language and statistical concepts that you need for Data Science and business analytics. The course is :

· Practical

· Highly analytical

· Packed with quizzes and assignments.

· Excel tutorials included.

· R scripts and tutorials

· Easy to understand and follow.

· Learn by Doing, no boring lectures.

· Comprehensive

· Data-driven

· Introduces you to the statistical R language.

· Teaches you about different data visualizations of ggplot.

· Teaches you How to clean, transform, and manipulate data.