R Programming for Statistics and Data Science

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365 Careers,365 Simona (The 365 Team)

6:24:32

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01 - Introduction

001 Ten Things You Will Learn in This Course.mp4

03:19

02 - Getting started

001 Intro.mp4

00:53

002 Downloading and installing R & RStudio.mp4

03:20

003 Quick guide to the RStudio user interface.mp4

07:37

003 RStudio-shortcuts.pdf

004 Changing the appearance in RStudio.mp4

01:47

005 Installing packages in R and using the library.mp4

05:11

03 - The building blocks of R

001 Creating an object in R.mp4

05:21

002 Exercise 1 Creating an object in R.html

003 Data types in R - Integers and doubles.mp4

04:40

004 Data types in R - Characters and logicals.mp4

03:18

005 Exercise 2 Data types in R.html

006 Coercion rules in R.mp4

02:39

007 Exercise 3 Coercion rules in R.html

008 Functions in R.mp4

03:22

009 Exercise 4 Using functions in R.html

010 Functions and arguments.mp4

02:30

011 Exercise 5 The arguments of a function.html

012 Building a function in R (basics).mp4

08:12

013 Exercise 6 Building a function in R.html

014 Using the script vs. using the console.mp4

02:55

external-links.txt

04 - Vectors and vector operations

001 Intro.mp4

01:10

002 Introduction to vectors.mp4

03:31

003 Vector recycling.mp4

01:39

004 Exercise 7 Vector recycling.html

005 Naming a vector in R.mp4

03:21

006 Exercise 8 Vector attributes - names.html

007 Getting help with R.mp4

06:37

008 Slicing and indexing a vector in R.mp4

07:01

009 Exercise 9 Indexing and slicing a vector.html

010 Changing the dimensions of an object in R.mp4

04:50

010 Course-notes-Section-II-III-IV.pdf

011 Exercise 10 Vector attributes - dimensions.html

external-links.txt

05 - Matrices

001 Creating a matrix in R.mp4

06:51

002 Faster code creating a matrix in a single line of code.mp4

02:46

003 Exercise 11 Creating a matrix in R.html

004 Do matrices recycle.mp4

01:36

005 Indexing an element from a matrix.mp4

04:37

006 Slicing a matrix in R.mp4

03:33

007 Exercise 12 Indexing and slicing a matrix.html

008 Matrix arithmetic.mp4

07:07

009 Exercise 13 Matrix arithmetic.html

010 Matrix operations in R.mp4

04:18

011 Exercise 14 Matrix operations.html

012 Categorical data.mp4

03:29

013 Course-notes-Section-II-III-IV-V.pdf

013 Creating a factor in R.mp4

06:00

014 Exercise 15 Creating a factor in R.html

015 Lists in R.mp4

06:01

016 Exercise Lists in R.html

017 Completed 33% of the course.html

external-links.txt

06 - Fundamentals of programming with R

001 Relational operators in R.mp4

05:07

002 Logical operators in R.mp4

03:22

003 Vectors and logicals operators.mp4

02:29

004 Exercise Logical operators.html

005 If, else, else if statements in R.mp4

05:48

006 Exercise If, else, else if statements in R.html

007 If, else, else if statements - Keep-In-Minds.mp4

03:50

008 For loops in R.mp4

06:24

009 Exercise For Loops in R.html

010 While loops in R.mp4

04:05

011 Exercise While loops in R.html

012 Repeat loops in R.mp4

03:05

013 Building a function in R 2.0.mp4

04:34

014 Building a function in R 2.0 - Scoping.mp4

05:16

015 Exercise Scoping.html

016 Completed 50% of the course.html

external-links.txt

07 - Data frames

001 Intro.mp4

00:55

002 Creating a data frame in R.mp4

05:54

003 Exercise 16 Creating a data frame in R.html

004 The Tidyverse package.mp4

03:19

005 Data import in R.mp4

03:28

005 pokRdex-comma.csv

005 pokRdex-tab.txt

006 Importing a CSV in R.mp4

03:14

007 Data export in R.mp4

02:31

008 Exercise 17 Importing and exporting data in R.html

008 employee-data.csv

009 Getting a sense of your data frame.mp4

03:58

009 pokRdex-comma.csv

010 Indexing and slicing a data frame in R.mp4

04:10

011 Extending a data frame in R.mp4

04:20

012 Exercise 18 Data frame operations.html

013 Dealing with missing data in R.mp4

04:48

external-links.txt

08 - Manipulating data

001 Intro.mp4

01:15

002 Data transformation with R - the Dplyr package - Part I.mp4

05:44

003 Data transformation with R - the Dplyr package - Part II.mp4

03:22

004 Sampling data with the Dplyr package.mp4

01:44

005 Using the pipe operator in R.mp4

03:27

006 Exercise 19 Data transformation with Dplyr.html

006 employee-data.csv

007 Tidying data in R - gather() and separate().mp4

07:27

007 billboard.csv

007 tb.csv

008 Tidying data in R - unite() and spread().mp4

02:44

008 weather.csv

009 Exercise 20 Data tidying with Tidyr.html

009 tb-untidy.csv

009 weather-untidy.csv

external-links.txt

09 - Visualizing data

001 Intro.mp4

01:01

002 Intro to data visualization.mp4

03:59

003 Intro to ggplot2.mp4

06:47

003 hdi-cpi.csv

004 Variables revisited.mp4

05:51

005 Building a histogram with ggplot2.mp4

06:31

005 titanic.csv

006 Exercise 21 Building a histogram with ggplot2.html

006 employee-data.csv

007 Building a bar chart with ggplot2.mp4

06:29

008 Exercise 22 Building a bar chart with ggplot2.html

009 Building a box and whiskers plot with ggplot2.mp4

06:18

010 Exercise 23 Building a box plot with ggplot2.html

011 Building a scatterplot with ggplot2.mp4

05:21

012 9.8-Scatter-Plot-Walkthrough.pdf

012 Exercise 24 Building a scatterplot with ggplot2.html

012 real-estate.csv

external-links.txt

10 - Exploratory data analysis

001 Population vs. sample.mp4

04:02

002 Mean, median, mode.mp4

05:04

003 Skewness.mp4

03:21

004 Exercise 25 Determining Skewness.html

004 skew-1.csv

004 skew-2.csv

005 Variance, standard deviation, and coefficient of variability.mp4

06:11

006 Covariance and correlation.mp4

06:41

006 landdata-states.csv

007 Exercise 26 Practical example with real estate data.html

007 practical-customer.csv

007 practical-product.csv

external-links.txt

11 - Hypothesis Testing

001 Distributions.mp4

06:32

002 Standard Error and Confidence Intervals.mp4

08:36

003 Hypothesis testing.mp4

08:02

003 R-Programming-for-Statistics-and-Data-Science-Course-notes-Hypothesis-testing.pdf

004 R-Programming-for-Statistics-and-Data-Science-Course-notes-Hypothesis-testing.pdf

004 Type I and Type II errors.mp4

03:22

005 Test for the mean - population variance known.mp4

07:00

005 ztest-a.csv

006 Exercise Test for the mean - population variance known.html

006 ztest-a.csv

007 R-Programming-for-Statistics-and-Data-Science-Course-notes-Hypothesis-testing.pdf

007 The P-value.mp4

04:45

008 Test for the mean - Population variance unknown.mp4

05:09

008 ttest-a.csv

009 Exercise Test for the mean - population variance unknown.html

009 ttest-a.csv

010 Comparing two means - Dependent samples.mp4

06:40

010 dependent-samples.csv

011 Exercise Comparing two means - Dependent samples.html

011 weight-data-exercise-kg.csv

011 weight-data-exercise-lbs.csv

012 Comparing two means - Independent samples.mp4

05:29

012 independent-samples.csv

external-links.txt

12 - Linear Regression Analysis

001 The linear regression model.mp4

05:26

002 Correlation vs regression.mp4

01:37

003 Geometrical representation.mp4

01:37

004 First regression in R.mp4

04:18

004 regression-example.csv

005 How to interpret the regression table.mp4

04:25

006 Exercise Doing a regression in R.html

006 real-estate-price-size-year-view.csv

007 Decomposition of variability SST, SSR, SSE.mp4

03:15

008 R-squared.mp4

04:52

009 Completed 100% of the course.html

external-links.txt

Description

R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R

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

Who is this 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

What You Need to Know?

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.

More details

Description
R Programming for Statistics and Data Science 2023
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?Â 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. Â
This course is packing all of this, and more, in one easy-to-handle bundle, and itâ€™s the perfect start to your journey.Â Â
So, welcome to R for Statistics and Data Science! Â
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 othersâ€™.Â Â
Laying strong foundations Â Â
This course wastes no time and jumps right into hands-on coding in R. But donâ€™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?Â Â
Well-paced learning.
Receive top class training with content which weâ€™ve built - and rigorously edited - to deliver powerful and efficient results.Â Â
Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.Â Â Â
Introductory guide to statistics.
We will take you through descriptive statistics and the fundamentals of inferential statistics.Â Â
We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.Â Â Â
Youâ€™ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.Â Â
The essentials of programming â€“ R-based.
Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations. Â
Data manipulation and analysis techniques in detail.
Learn to work with vectors, matrices, data frames, and lists.Â Â Â
Become adept in â€˜the Tidyverse packageâ€™ - Râ€™s most comprehensive collection of tools for data manipulation â€“ enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.Â Â
Create meaning-heavy data visualizations and plots.Â Â
Practice makes perfect.
Reinforce your learning through numerous practical exercises, made with love, for you, by us.
What about homework, projects, & exercises?Â Â
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 â€“ a verifiable certificate.Â Â
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. Â
Â 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

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R Programming

365 Careers

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365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 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 CPA2) 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, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook5) Blockchain for BusinessAll of our 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 data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur365 Careers’ courses are the perfect place to start.

365 Simona (The 365 Team)

Instructor's Courses

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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