Statistical Thinking and Data Science with R.

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.

Language: english

Note: 4.6/5 (85 notes) 15,547 students

Instructor(s): Haytham Omar

Last update: 2021-12-06

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.

 

Requirements

  • Motivation to learn R.
  • Boost your skills to advance your career.
  • Nag for numbers and analytics.

 

Statistical Thinking and Data Science with R.Statistical Thinking and Data Science with R.

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.


Looking forward to seeing you inside 🙂

Haytham


 

Who this course is for

  • Business Executives
  • Business analysts
  • Aiming at a career in data science
  • understanding the fundamentals of Statistics
  • Learning R
  • Learning about data manipulation.
  • Learning statistics.

 

Course 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
    • Why R
  • 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
    • Central tendency assignment
    • 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
  • Probabilities
    • Intro
    • Probability introduction
    • Variance and standard deviation
    • Overlapping of probability
    • Desecrate and continuous probability
    • Conditional Probability
    • Question 1 Probability
    • Question 2 Probability
    • Rolling the dice
    • Binomial distribution
    • Question 1 Binomial
    • Question 2 Binomial
    • For looping on a binomial distribution
    • Binomial assignment
    • 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
    • Simulation assignment
    • 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
    • Tests for association
    • 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
  • Machine learning
    • Intro to machine learning
    • Decision Tree demo
    • Kmeans
    • Overfitting
    • Kmeans in R
    • Total sum of squares
    • Silhouette
    • Interactive three dimensional scatter plot
    • Assignment
    • Forecasting with machine learning
    • Supervised learning : Decision Tree
    • Comparing Models
    • Classification Data orientation
    • Exploring the data
    • Correlation Matrix
    • Splitting
    • Training and testing
    • Control the fitting
    • Logistic Regression classfication
    • Probabilities of logistic regression
    • Confusion matrix
    • ROC
    • Decision Tree model
    • Assignment
    • Conclusion
    • Summary

 


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