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R from A-Z! Statistics, Advanced Regression,Visualizations, Probabilities, Inference, Simulations and Machine learning.

What you will 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.


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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 :

路 Practical

路 Highly analytical

路 Packed with quizzes and assignments.

路 Excel tutorials included.

路 R scripts and tutorials

路 Easy to understand and follow.

路 Learn by Doing, no boring lectures.

路 Comprehensive

路 Data-driven

路 Introduces you to the statistical R language.

路 Teaches you about different data visualizations of ggplot.

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

Looking forward to seeing you inside 馃檪

Haytham

English
language

Content

Introduction
Introduction
Get the Best out of this course
Curriculum
Types of analytics
Objectives of data science
Applications of data science
The data science Process
The data science space
Statistical daily task for a data scientist.
Statistics is at the heart of it.
Summary
Installing R and R Studio
Welcome to the World of R!
What is R statistical Language.
How to install R?
How to install Rstudio?
A walk through tutorial
Setup your project
Install packages
Summary
R fundamentals
Introduction
Different data structures and types in R
Do arithmetic calculations and write functions in R
Creating a list.
Importing Data in R and Basic exploration
Selecting data in a data frame
If else function
Conditions
Functions with Conditions
Forloops
Applying a function inside the loop
For-loop on a data-frame
Applying the function on a data frame
Assignment
Assignment Section 4 answer Part 1
Assignment Section 4 answer part 2
Summary
Descriptive statistics
Intro
Central tendency
Measures of spread
Calculating measures of spread and centrality Part 1
Calculating measures of spread and centrality PART 2
Detecting outliers
Detecting outliers in R
Data cleaning and manipulation
Intro
Intro to dplyr
Investigate with Dplyr
Unique invoices
Average Bucket value per country
Average items in an invoice
Joining
Changing date time to date
Pivot wider
Pivot longer
Separate and Paste
Putting it all together
Assignment : New York airlines
Assignment : Question 1 answer
Assignment question 2&3 answer
Assignment question 4,5,6
Assignment question 7
Summary
Visulalization
Introduction
Line plots
Scatter plots
Bar plots
Distribution plots
Box plots
Histograms
Histograms 2
Assignment
Assignment Solution Question 1 and 2
Assignment Solution Part 2
Summary
Propabilties
Intro
Probability introduction
Variance and standard deviation
Overlapping of probability
Desecrate and continuous probability
Conditional Probability
Question 1 Probability
Question 2 Probability
Binomial distribution
Question 1 Binomial
Question 2 Binomial
For looping on a binomial distribution
Poisson Distribution
Poisson distribution in R
Continuos Distributions
Normal distributions example
Uniform distribution example
Central Limit theorem
Associations
Calculating Relative risk in R
Association among numerical variables
Correlation Matrix
Cause and effect
Bayes theory
Fitting Distributions
Distributions Intro
Distribution shapes
Chi-square Tests
Chi-square test in excel
Part 2
Cover for 90% of distribution
Assignment Distribution in Excel
Assignment answer : Bike demand
Distributions in R
Assignment
Assignment answer
Simulations
Simulation Intro
Simulations
Restaurant Example 1
Customer’s number
Expected revenue
Conclusion
Waiting lines
Example
Waiting lines in Excel
Waiting lines in R
Simulating waiting lines 400 times
Simulation with Capacity Constraints
Waiting line at a call centre
Defining the right K
Capacity Constraints
Assignment
Assignment solution
Sequential service on one system
Many Services
Multiple service simulations in R
Conclusion
Assignment
Assignment Solution
Summary
Hypothesis testing and Confidence intervals
Hypothesis testing
Hypothesis testing
Sampling
Histogram for mean identification
Boxplot
Two sample T-test
Cats heart weight
One sample test
Pizza Place
Non Normality
Chi-Square test for independence
Chi-square test in R
Fisher test
UK drivers
T_test on drivers
Hypothesis test for binomial distributions
Revisiting Bayes theory
Bayesian inference
Calculating post estimate
Odds and odds ratio
ANOVA and regression
Analysis of variance
Analysis of variance inside R
Tukey Honest significant differences
Interpretation of Tukey
two way ANOVA
Intro to linear Regression
Linear Regression in excel
Sum of squared errors
Cleaning the data for regression
EDA for housing
one variable modeling
Multiple Regression
model interaction
Comparing models with ANOVA
Further data analysis
Regressing all the variables
feature importance
Step AIC
Quiz on regression and anova
Logistic Regression
Intro
Logistic Regression
City vs Price per square foot
Predicting one observation
Odds and probability question
fitting all variables
understanding multiple predictors
Testing Categorical variables
Conclusions about Multiple predictions
Comparing three models
Log odds of categorical Variable
Multinomial logistic regression
Predicting the multinomial
Testing social economic status
improving the model
Quiz
Regularization of Regression models
Intro
Regularized regression models
The loss function
Multi-Colinearity
Splitting the data
encoding
Training Ridge Regression
Cross Validation Ridge
Ridge coefficients
Lasso Regression
Visualization of lasso
Minimum squared error Lasso
Prediction after Cross validation
model matrix for logistic regression
Non Zero Coefficients
Lasso Coefficients
Regularized models