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2021-01-11 07:55:58
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Development Data Science R

R Programming for Statistics and Data Science 2021

R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R
Rating: 4.5 out of 54.5 (2,951 ratings)
17,823 students
Created by 365 Careers, 365 Simona (The 365 Team)
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn the fundamentals of programming in R
  • Work with R’s conditional statements, functions, and loops
  • Build your own functions in R
  • Get your data in and out of R
  • Learn the core tools for data science with R
  • Manipulate data with the Tidyverse ecosystem of packages
  • Systematically explore data in R
  • The grammar of graphics and the ggplot2 package
  • Visualise data: plot different types of data & draw insights
  • Transform data: best practices of when and how
  • Index, slice, and subset data
  • Learn the fundamentals of statistics and apply them in practice
  • Hypothesis testing in R
  • Understand and carry out regression analysis in R
  • Work with dummy variables
  • Learn to make decisions that are supported by the data!
  • Have fun by taking apart Star Wars and Pokemon data, as well some more serious data sets
Curated for the Udemy for Business collection

Course content

12 sections • 126 lectures • 6h 41m total length

  • Preview03:19

  • Intro
    00:53
  • Downloading and installing R & RStudio
    Preview03:20
  • Quick guide to the RStudio user interface
    07:37
  • RStudio's GUI
    3 questions
  • Changing the appearance in RStudio
    01:47
  • Installing packages in R and using the library
    05:10

  • Creating an object in R
    05:21
  • Exercise 1 Creating an object in R
    00:25
  • Data types in R - Integers and doubles
    04:40
  • Data types in R - Characters and logicals
    Preview03:17
  • Objects and Data Types
    4 questions
  • Exercise 2 Data types in R
    00:17
  • Coercion rules in R
    02:39
  • Exercise 3 Coercion rules in R
    00:20
  • Functions in R
    03:22
  • Exercise 4 Using functions in R
    00:47
  • Functions and arguments
    02:30
  • Exercise 5 The arguments of a function
    00:25
  • Preview08:12
  • Objects and Functions
    3 questions
  • Exercise 6 Building a function in R
    00:25
  • Using the script vs. using the console
    02:55

  • Intro
    01:10
  • Introduction to vectors
    03:31
  • Vector recycling
    01:39
  • Exercise 7 Vector recycling
    00:43
  • Preview03:21
  • Exercise 8 Vector attributes - names
    00:17
  • Introduction to vectors
    2 questions
  • Getting help with R
    06:37
  • Getting Help with R
    2 questions
  • Slicing and indexing a vector in R
    07:01
  • Extracting elements from a vector
    3 questions
  • Exercise 9 Indexing and slicing a vector
    00:13
  • Changing the dimensions of an object in R
    04:50
  • Exercise 10 Vector attributes - dimensions
    00:26

  • Creating a matrix in R
    06:51
  • Faster code: creating a matrix in a single line of code
    02:46
  • Creating a matrix
    3 questions
  • Exercise 11 Creating a matrix in R
    00:16
  • Do matrices recycle?
    01:36
  • Indexing an element from a matrix
    04:37
  • Slicing a matrix in R
    03:33
  • Exercise 12 Indexing and slicing a matrix
    00:29
  • Matrix arithmetic
    07:07
  • Exercise 13 Matrix arithmetic
    00:44
  • Matrix operations in R
    04:18
  • Matrix operations
    4 questions
  • Exercise 14 Matrix operations
    00:28
  • Categorical data
    03:29
  • Preview06:00
  • Factors in R
    2 questions
  • Exercise 15 Creating a factor in R
    00:14
  • Lists in R
    06:01
  • Exercise: Lists in R
    00:55
  • Completed 33% of the course
    00:32

  • Relational operators in R
    05:06
  • Logical operators in R
    03:22
  • Vectors and logicals operators
    02:29
  • Relational and Logical operators in R
    5 questions
  • Exercise Logical operators
    00:01
  • If, else, else if statements in R
    05:47
  • Exercise If, else, else if statements in R
    00:56
  • If, else, else if statements - Keep-In-Mind's
    03:50
  • For loops in R
    06:24
  • Exercise: For Loops in R
    00:05
  • While loops in R
    04:05
  • Exercise: While loops in R
    00:05
  • Repeat loops in R
    03:05
  • Loops in R
    4 questions
  • Building a function in R 2.0
    04:33
  • Building a function in R 2.0 - Scoping
    05:16
  • Exercise Scoping
    00:10
  • Completed 50% of the course
    00:38

  • Intro
    00:54
  • Creating a data frame in R
    05:54
  • Exercise 16 Creating a data frame in R
    00:16
  • The Tidyverse package
    03:19
  • Data import in R
    03:28
  • Importing a CSV in R
    03:14
  • Data export in R
    02:31
  • Exercise 17 Importing and exporting data in R
    00:26
  • Creating data frames
    5 questions
  • Getting a sense of your data frame
    03:58
  • Indexing and slicing a data frame in R
    04:09
  • Data frame operations
    4 questions
  • Extending a data frame in R
    04:20
  • Exercise 18 Data frame operations
    00:38
  • Dealing with missing data in R
    04:48

  • Intro
    01:15
  • Data transformation with R - the Dplyr package - Part I
    05:44
  • Data transformation with R - the Dplyr package - Part II
    03:22
  • Sampling data with the Dplyr package
    01:44
  • Using the pipe operator in R
    03:27
  • Manipulating data
    5 questions
  • Exercise 19 Data transformation with Dplyr
    00:33
  • Tidying data in R - gather() and separate()
    07:27
  • Tidying data in R - unite() and spread()
    02:44
  • Tidying data
    5 questions
  • Exercise 20 Data tidying with Tidyr
    00:28

  • Intro
    01:00
  • Intro to data visualization
    03:59
  • Preview06:47
  • Variables: revisited
    05:51
  • Building a histogram with ggplot2
    06:31
  • Exercise 21 Building a histogram with ggplot2
    00:22
  • Building a bar chart with ggplot2
    06:29
  • Exercise 22 Building a bar chart with ggplot2
    00:28
  • Building a box and whiskers plot with ggplot2
    06:17
  • Exercise 23 Building a box plot with ggplot2
    00:37
  • Building a scatterplot with ggplot2
    05:21
  • Exercise 24 Building a scatterplot with ggplot2
    00:29

  • Population vs. sample
    04:02
  • Preview05:04
  • Skewness
    03:21
  • Exercise 25 Determining Skewness
    00:06
  • Variance, standard deviation, and coefficient of variability
    06:11
  • Covariance and correlation
    06:41
  • Exercise 26 Practical example with real estate data
    00:36

Requirements

  • You’ll need to install R Studio. We will show you how to do it in one of the first lectures of the course
  • All software and data used in the course are free.

Description

R Programming for Statistics and Data Science 2021

R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you?  Data scientist is the hottest ranked profession in the US.

But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title.  

This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey.  

So, welcome to R for Statistics and Data Science!  

R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’.   

Laying strong foundations  

This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R.

What makes this course different from other courses?  

  • Well-paced learning.

Receive top class training with content which we’ve built - and rigorously edited - to deliver powerful and efficient results.  

Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.  

  • Introductory guide to statistics.

We will take you through descriptive statistics and the fundamentals of inferential statistics.  

We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.     

You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.  

  • The essentials of programming – R-based.

Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.  

  • Data manipulation and analysis techniques in detail.

Learn to work with vectors, matrices, data frames, and lists.  

Become adept in ‘the Tidyverse package’ - R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.  

Create meaning-heavy data visualizations and plots.  

  • Practice makes perfect.

Reinforce your learning through numerous practical exercises, made with love, for you, by us.

What about homework, projects, & exercises?  

There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck.

You: Is there something to show for the skills I will acquire?

Us: Indeed, there is – a verifiable certificate.  

You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R.  

 If that sounds good to you, then welcome to the classroom :)

Who this course is for:

  • Aspiring data scientists
  • Beginners to programming
  • People interested in statistics and data analysis
  • Anyone who wants to learn how to code and apply their skills in practice

Featured review

Levent Mehmed
Levent Mehmed
12 courses
3 reviews
Rating: 4.5 out of 57 months ago
The cours was very good! I could learn more than what I should learn at my college. Because the Corona virus we moved to online classes @ my college and it went not so good. So I bought this course to help me out. It did help me indeed! Only bad thing is we use here some librarys when our teachers didn't use any library but ggplot. But I'm pretty sure that is to make it us harded :D However I have taken everything what I need from this course.

Instructors

365 Careers
Creating opportunities for Business & Finance students
365 Careers
  • 4.5 Instructor Rating
  • 392,752 Reviews
  • 1,343,210 Students
  • 70 Courses

365 Careers is the #1 best-selling provider of finance courses on Udemy. The company’s courses have been taken by more than 1,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.  

Currently, the firm focuses on the following topics on Udemy:  

1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA

2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics

3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing

4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook

5) Blockchain for Business

All of the company’s courses are:  

Pre-scripted  

Hands-on  

Laser-focused  

Engaging  

Real-life tested  

By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.  

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start. 

365 Simona (The 365 Team)
Data Science Instructor
365 Simona (The 365 Team)
  • 4.5 Instructor Rating
  • 2,951 Reviews
  • 105,994 Students
  • 1 Course

My name is Simona and I am your friendly neighborhood Data Science instructor. 

I am a Cognitive Science researcher by formal training, a Data Science and Statistics enthusiast by heart. As a graduate from the University of Edinburgh, I have a rigorous academic approach and an uncompromising drive for excellence, and I am super excited to share my experience with you!

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