# Harvard Data Science Course: R Basics

This is the Harvard Data Science Professional Certificate Program, you will learn the basics of R programming along with importance, advantages and scope.

## About Harvard Data Science course

This is EDXs’ first Professional Certificate Program in Data Science brought to you by the Harvard data science team. This course will introduce you to R programming basics, and you can better retain R when you learn it to solve a specific problem. You will learn the R skills needed to answer essential questions about differences in datasets.

## What is Data Science in simple words?

In simple words, Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from the raw data. But how is this different from what statisticians have been doing for years?

The answer lies in the difference between explaining and predicting.

As you can see from the figure, a Data Analyst usually explains what is going on by processing the history of the data. On the other hand, Data Scientist do not only conduct exploratory analysis to discover insights from it but also uses various advanced machine learning algorithms to identify a particular event’s occurrence in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

Data Science is primarily used to make decisions and predictions, using predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.

## Who is a Data Scientist?

You will find various definitions of Data Scientists, But In simple words, a Data Scientist practices the art of Data Science. The term “Data Scientist” has been coined after considering the fact that a Data Scientist draws a lot of information from the scientific domain and applications, whether it is statistics or mathematics.

## About the R language

R is a language and environment for statistical computing and graphics, developed at Bell Laboratories by John Chambers and colleagues. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques. It is highly extensible and provides an Open Source route to participation activity.

One of R language strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

## Why R for data science?

R is a language used for statistical computations, data analysis, and graphical representation of data. R is the second most popular language in data science. Even Google trends showcase the rapidly rising popularity of R Programming.

There are some distinct advantages associated with each. Here we discuss the advantages of R in data science and why it proves to be an ideal choice in this space.

Here are 6 reasons for choosing R for your next data science project or to begin your journey in this field:

### 1. Academia

R is a prevalent language in academia. Many researchers and scholars use R for experimenting with data science—many popular books and learning resources on data science use R for statistical analysis.

### 2. Data wrangling

Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is a critical and time taking process in data science.

### 3. Data visualization

Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles that are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation.

### 4. Specificity

R is a language designed especially for statistical analysis and data reconfiguration. All the R libraries focus on making one thing certain to make data analysis easier, more approachable, and detailed.

### 5. Machine learning

At some point in data science, a programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides ample tools for developers to train and illustrate an algorithm and predict future events.

### 6. Availability

The R programming language is open-source. This makes it highly cost-effective for a project of any size.

#### Apart from this, here are some benefits for choosing and using R:

- The style of coding is quite easy.
- It is s open source.
- The community support is overwhelming. There are numerous forums to help you out.
- Get high-performance computing experience.
- One of the highly sought skills by analytics and data science companies.

## What this Course Covers?

This course will cover R’s functions and data types, tackle how to operate on vectors, and use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else” and “for loop” commands and how to wrangle, analyze and visualize data.

## What will you learn from the Harvard Data Science course?

This is one of the basic course on R brought to you from the Harvard data science team; you will learn the following from the course:

- How to read, extract, and create datasets in R.
- Perform a variety of operations and analyses on datasets using R programming.
- Write your own functions/sub-routines in R programming.
- Basic R syntax.
- Foundational R programming concepts such as data types, vectors arithmetic, indexing, and perform operations in R, including sorting, data wrangling, and making plots.
- Fundamental R programming skills.
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice.
- Obtain experience with the tidyverse, including data visualization with ggplot2 and data wrangling.
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio.
- Implement machine learning algorithms.
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies.

## Syllabus on Harvard Data Science program

In this certificate program by Harvard data science, you will learn:

### 1. R Basics, Functions, and Data Types

- You will get started with R programming and learn about R’s functions and data types.

### 2. Vectors and Sorting

· You will learn to operate on vectors and advanced functions such as sorting.

### 3. Indexing, Data Manipulation, and Plots

- You will learn to wrangle, analyze, and visualize data.

### 4. Programming Basics

- You will learn to use general programming features like ‘if-else’ and ‘for loop’ commands to write your own functions to perform various operations on datasets.

## Prerequisites for the Harvard Data Science course

For the Harvard data science with R course, an up-to-date browser is recommended to enable programming directly in a browser-based interface.

*Note: *

*If you have already done this Harvard for data science using R programming course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

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## Description

## About Harvard Data Science course

This is EDXs’ first Professional Certificate Program in Data Science brought to you by the Harvard data science team. This course will introduce you to R programming basics, and you can better retain R when you learn it to solve a specific problem. You will learn the R skills needed to answer essential questions about differences in datasets.

## What is Data Science in simple words?

In simple words, Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from the raw data. But how is this different from what statisticians have been doing for years?

The answer lies in the difference between explaining and predicting.

As you can see from the figure, a Data Analyst usually explains what is going on by processing the history of the data. On the other hand, Data Scientist do not only conduct exploratory analysis to discover insights from it but also uses various advanced machine learning algorithms to identify a particular event’s occurrence in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

Data Science is primarily used to make decisions and predictions, using predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.

## Who is a Data Scientist?

You will find various definitions of Data Scientists, But In simple words, a Data Scientist practices the art of Data Science. The term “Data Scientist” has been coined after considering the fact that a Data Scientist draws a lot of information from the scientific domain and applications, whether it is statistics or mathematics.

## About the R language

R is a language and environment for statistical computing and graphics, developed at Bell Laboratories by John Chambers and colleagues. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques. It is highly extensible and provides an Open Source route to participation activity.

One of R language strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

## Why R for data science?

R is a language used for statistical computations, data analysis, and graphical representation of data. R is the second most popular language in data science. Even Google trends showcase the rapidly rising popularity of R Programming.

There are some distinct advantages associated with each. Here we discuss the advantages of R in data science and why it proves to be an ideal choice in this space.

Here are 6 reasons for choosing R for your next data science project or to begin your journey in this field:

### 1. Academia

R is a prevalent language in academia. Many researchers and scholars use R for experimenting with data science—many popular books and learning resources on data science use R for statistical analysis.

### 2. Data wrangling

Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is a critical and time taking process in data science.

### 3. Data visualization

Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles that are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation.

### 4. Specificity

R is a language designed especially for statistical analysis and data reconfiguration. All the R libraries focus on making one thing certain to make data analysis easier, more approachable, and detailed.

### 5. Machine learning

At some point in data science, a programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides ample tools for developers to train and illustrate an algorithm and predict future events.

### 6. Availability

The R programming language is open-source. This makes it highly cost-effective for a project of any size.

#### Apart from this, here are some benefits for choosing and using R:

- The style of coding is quite easy.
- It is s open source.
- The community support is overwhelming. There are numerous forums to help you out.
- Get high-performance computing experience.
- One of the highly sought skills by analytics and data science companies.

## What this Course Covers?

This course will cover R’s functions and data types, tackle how to operate on vectors, and use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else” and “for loop” commands and how to wrangle, analyze and visualize data.

## What will you learn from the Harvard Data Science course?

This is one of the basic course on R brought to you from the Harvard data science team; you will learn the following from the course:

- How to read, extract, and create datasets in R.
- Perform a variety of operations and analyses on datasets using R programming.
- Write your own functions/sub-routines in R programming.
- Basic R syntax.
- Foundational R programming concepts such as data types, vectors arithmetic, indexing, and perform operations in R, including sorting, data wrangling, and making plots.
- Fundamental R programming skills.
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice.
- Obtain experience with the tidyverse, including data visualization with ggplot2 and data wrangling.
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio.
- Implement machine learning algorithms.
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies.

## Syllabus on Harvard Data Science program

In this certificate program by Harvard data science, you will learn:

### 1. R Basics, Functions, and Data Types

- You will get started with R programming and learn about R’s functions and data types.

### 2. Vectors and Sorting

· You will learn to operate on vectors and advanced functions such as sorting.

### 3. Indexing, Data Manipulation, and Plots

- You will learn to wrangle, analyze, and visualize data.

### 4. Programming Basics

- You will learn to use general programming features like ‘if-else’ and ‘for loop’ commands to write your own functions to perform various operations on datasets.

## Prerequisites for the Harvard Data Science course

For the Harvard data science with R course, an up-to-date browser is recommended to enable programming directly in a browser-based interface.

*Note: *

*If you have already done this Harvard for data science using R programming course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

FAQ

## Specification:

- EDX
- Harvard University
- Professional Certificate
- Self-paced
- Beginner
- 1-3 Months
- Free Course (Affordable Certificate)
- English
- R
- Git RStudio
- Up-to-date browser required for programming
- Data Science Data Science with 'R' Data Visualization Data Wrangling Machine learning Practical Statistics Probability Regression Analysis

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