Statistical Thinking and Data Science with R.
Statistical Thinking and Data Science with R., available at $79.99, has an average rating of 4.8, with 246 lectures, 4 quizzes, based on 191 reviews, and has 17915 subscribers.
You will learn about 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. learn how to use tidy models the standard machine learning package in R This course is ideal for individuals who are Business Executives or Business analysts or Aiming at a career in data science or understanding the fundamentals of Statistics or Learning R or Learning about data manipulation. or Learning statistics. It is particularly useful for Business Executives or Business analysts or Aiming at a career in data science or understanding the fundamentals of Statistics or Learning R or Learning about data manipulation. or Learning statistics.
Enroll now: Statistical Thinking and Data Science with R.
Summary
Title: Statistical Thinking and Data Science with R.
Price: $79.99
Average Rating: 4.8
Number of Lectures: 246
Number of Quizzes: 4
Number of Published Lectures: 243
Number of Published Quizzes: 4
Number of Curriculum Items: 255
Number of Published Curriculum Objects: 252
Original Price: $84.99
Quality Status: approved
Status: Live
What You Will Learn
- 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.
- learn how to use tidy models the standard machine learning package in R
Who Should Attend
- Business Executives
- Business analysts
- Aiming at a career in data science
- understanding the fundamentals of Statistics
- Learning R
- Learning about data manipulation.
- Learning statistics.
Target Audiences
- Business Executives
- Business analysts
- Aiming at a career in data science
- understanding the fundamentals of Statistics
- Learning R
- Learning about data manipulation.
- Learning statistics.
Update: Machine Learning with tidy models is included in the last Chapter.(August 2023)
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 Rand 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
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Get the Best out of this course
Lecture 3: Curriculum
Lecture 4: Types of analytics
Lecture 5: Objectives of data science
Lecture 6: Applications of data science
Lecture 7: The data science Process
Lecture 8: Why R
Chapter 2: Installing R and R Studio
Lecture 1: Welcome to the World of R!
Lecture 2: What is R statistical Language.
Lecture 3: How to install R?
Lecture 4: How to install Rstudio?
Lecture 5: A walk through tutorial
Lecture 6: Setup your project
Lecture 7: Install packages
Lecture 8: Summary
Chapter 3: R fundamentals
Lecture 1: Introduction
Lecture 2: Different data structures and types in R
Lecture 3: Do arithmetic calculations and write functions in R
Lecture 4: Creating a list.
Lecture 5: Importing Data in R and Basic exploration
Lecture 6: Selecting data in a data frame
Lecture 7: If else function
Lecture 8: Conditions
Lecture 9: Functions with Conditions
Lecture 10: Forloops
Lecture 11: Applying a function inside the loop
Lecture 12: For-loop on a data-frame
Lecture 13: Applying the function on a data frame
Lecture 14: Assignment
Lecture 15: Assignment Section 4 answer Part 1
Lecture 16: Assignment Section 4 answer part 2
Lecture 17: Summary
Chapter 4: Descriptive statistics
Lecture 1: Intro
Lecture 2: Central tendency
Lecture 3: Measures of spread
Lecture 4: Calculating measures of spread and centrality Part 1
Lecture 5: Calculating measures of spread and centrality PART 2
Lecture 6: Detecting outliers
Lecture 7: Detecting outliers in R
Chapter 5: Data cleaning and manipulation
Lecture 1: Intro
Lecture 2: Intro to dplyr
Lecture 3: Investigate with Dplyr
Lecture 4: Unique invoices
Lecture 5: Average Bucket value per country
Lecture 6: Average items in an invoice
Lecture 7: Joining
Lecture 8: Changing date time to date
Lecture 9: Pivot wider
Lecture 10: Pivot longer
Lecture 11: Separate and Paste
Lecture 12: Putting it all together
Lecture 13: Assignment : New York airlines
Lecture 14: Assignment : Question 1 answer
Lecture 15: Assignment question 2&3 answer
Lecture 16: Assignment question 4,5,6
Lecture 17: Assignment question 7
Lecture 18: Summary
Chapter 6: Visulalization
Lecture 1: Introduction
Lecture 2: Line plots
Lecture 3: Scatter plots
Lecture 4: Bar plots
Lecture 5: Distribution plots
Lecture 6: Box plots
Lecture 7: Histograms
Lecture 8: Histograms 2
Lecture 9: Assignment
Lecture 10: Assignment Solution Question 1 and 2
Lecture 11: Assignment Solution Part 2
Lecture 12: Summary
Chapter 7: Probabilities
Lecture 1: Intro
Lecture 2: Probability introduction
Lecture 3: Variance and standard deviation
Lecture 4: Overlapping of probability
Lecture 5: Conditional Probability
Lecture 6: Question 1 Probability
Lecture 7: Question 2 Probability
Lecture 8: Binomial distribution
Lecture 9: Question 1 Binomial
Lecture 10: Question 2 Binomial
Lecture 11: For looping on a binomial distribution
Lecture 12: Poisson Distribution
Lecture 13: Poisson distribution in R
Lecture 14: Continuos Distributions
Lecture 15: Normal distributions example
Lecture 16: Uniform distribution example
Lecture 17: Central Limit theorem
Lecture 18: Associations
Lecture 19: Calculating Relative risk in R
Instructors
-
Haytham Omar-Ph.D
Consultant-Supply chain
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 3 votes
- 3 stars: 17 votes
- 4 stars: 38 votes
- 5 stars: 132 votes
Frequently Asked Questions
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