<commit_before>---
layout: post
author: <NAME>
categories:
- Careers
tags:
- Learn-Python
- Python
- R-Bloggers
- Learn-R
- R
title: Which data science skills are important ($50,000 increase in salary in 6-months)
date: 2022-03-14 09:00:00 -0400
excerpt: ''
image: "/assets/data_science_plan.jpg"
image_preview: "/assets/data_science_plan.jpg"

---
In late September 2021, David was a Research Analyst with Texas A&M University.

In March of 2022, less than 6-months later, he has accepted a position with Microsoft as a Machine Learning Support Engineer. In one of my webinars, David explained that he had just increased his salary by $50,000. 

![](/assets/david_espinola_review.jpg)

I told him that's just the beginning.

![](/assets/david_espinola_response.jpg)

How was David able to get a job at an elite company like Microsoft?

What skills was David learning that he could transition so quickly?

The rest of this post will show you how David did it. This post includes 4 case studies, research and 2 surveys all to answer questions like...

* What skills separate data scientists like David from everyone else
* How to pick a language (and your choice may surprise you)
* How to pick a development environment
* How to learn the skills
* Why this approach to learning skills works (based on examining 4 case studies)

# Why you should learn these skills

If you want to become a data scientist, you'll need to learn how to generate value for your organizations. I've written about [how data scientists create value for organizations here](https://www.business-science.io/business/2019/05/23/how-to-become-a-fiancial-data-scientist.html). But, in general, you complete a process (called the data science process), which involves learning these skills. 

![](/assets/data_science_plan.jpg)

Data scientists that learn these skills can convince employers to hire them. And that's really important. Here's why. 

According to Glassdoor, learning these data science skills [can have this effect on your salary](https://www.glassdoor.com/Salaries/pittsburgh-data-scientist-salary-SRCH_IL.0,10_IM684_KO11,25.htm?clickSource=searchBtn) (if you live in Pittsburgh PA like I do, I encourage you to check your own locale). 

![](/assets/glassdoor_data_scientist_salary.jpg)

<p class="date text-center">Glassdoor: Data Scientist Earnings in Pittsburgh, PA</p>

But that's just the start. 

Like I told David, your career will accelerate. 

Here's what the career path looks like for a [Senior data scientist in Pittsburgh PA](https://www.glassdoor.com/Career/senior-data-scientist-career_KO0,21.htm). 

![](/assets/data_scientist_career_path.jpg)

<p class="date text-center">Glassdoor: Senior Data Scientist Earnings in Pittsburgh, PA</p>

Now I know what you're thinking: _"That salary is great. BUT, I'll never be able to master this list of skills. Especially not in 6-months."_

Actually you can. 

Here's how.  

# How to master learning data science

Mastering data science isn't hard. It just requires:

* Motivation: You'll need to dedicate about 10-hours per week 
* A plan: You'll learn from David and several others in this 

You'll need to start by picking a language. 

# 1. Pick a language

Who do you think is going to win this battle?

![](/assets/r_vs_python.jpg)

Well it's neither.

Because C++ is the true superior programming language. 

![](/assets/c-plus-plus.jpg)

I'm just kidding. 

But in truth, it actually doesn't matter. You can succeed with both. 

I know both. 

I teach both. 

But, if we want to really answer this question, we should tackle this like data scientists. You know, with data to support our decision. 

![](/assets/data_scientists_ibm.jpg)

So, let's tackle this like data scientists. 

## How to pick a language

If I were picking a language for the first time, I would consider a few things:

1. How useful is the language for data science
2. The demand for the job market
3. The competition in the job market

## 1.1 How useful is the language for data science?

So if you look at [the history of python](https://www.geeksforgeeks.org/history-of-python/), it clearly says it is a general purpose, high level programming language. It has an emphasis on code readability and to express concepts in fewer lines of code.

![](/assets/history_of_python.jpg)

<p class="date text-center"><a href="https://www.geeksforgeeks.org/history-of-python/">GeeksforGeeks: History of Python</a></p>

Meanwhile if you look at R, it was closely modeled on the S language for statistical computing and graphics.

![](/assets/what_is_r.jpg)

<p class="date text-center"><a href="https://www.r-project.org/about.html">R-project: What is R?</a></p> 

So Python is a general purpose language (but has been adapted for many tasks like data science) while R has been developed for the sole purpose of statistics. 

But I wasn't satisfied with that, so I dug a little deeper. Here's what I've found. 

![](/assets/data_science_language_strengths.jpg)

* **Python:** Great for Machine Learning and Deep Learning but misses the mark on reporting (very important) and has fewer libraries for important analyses like econometrics. 
* **R:** Has well developed tools for business analysis and data science. Strong in everything except deep learning. But, deep learning is rarely used. And when you need deep learning or extra APIs, you can integrate R with Python. 

So I'm going to give this one to R. 

## 1.2 The demand for the job market

Next is demand for the job market for Python and R. Currently there are 21,271 Data Scientist jobs for Python. A

![](/assets/data_science_jobs_python.jpg)

And, there are 8,713 Data Scientist Jobs for R. 

![](/assets/data_science_jobs_r.jpg)

So for every 1 R data science job there are 2.4 for Python.

I'll give this one to Python.  

## 1.3 The competition in the job market

Next, what we need to consider is how many people you will be competing against to get these jobs. 

* **Python:** There are over 8,000,000 people that know python (and that number is growing fast)
* **R:** It's estimated that 250,000 to 2,000,000 people that know R and that number is also growing fast. 

So for every 1 R user there are potentially 4 to 32 more python users. 

![](/assets/python_r_competition.jpg)

So R positions are going to less competitive by 10X or more. Dang!

This one clearly goes to R. 

## R is a solid choice

R is a solid choice, and it's one of the reasons that students like David are able to quickly transition into a data science role. And keep in mind, you can always pick up Python later.

![](/assets/python_vs_r_results.jpg)

# 2. Pick a Development Tool

Next, we need to pick an integrated development environment. This is a fancy term for the thing you will type your code into. 

![](/assets/rstudio_ide.jpg)

I ran a poll to see what everyone's using for R (and I did the same thing for Python too if you want to see those results).

## Their favorite way to code in R<commit_msg>Update from Forestry.io <NAME> updated _drafts/which-data-science-skills-are-important.md<commit_after>---
layout: post
author: <NAME>
categories:
- Careers
tags:
- Learn-Python
- Python
- R-Bloggers
- Learn-R
- R
title: Which data science skills are important ($50,000 increase in salary in 6-months)
date: 2022-03-14 09:00:00 -0400
excerpt: ''
image: "/assets/data_science_plan.jpg"
image_preview: "/assets/data_science_plan.jpg"

---
In late September 2021, David was a Research Analyst with Texas A&M University.

In March of 2022, less than 6-months later, he has accepted a position with Microsoft as a Machine Learning Support Engineer. In one of my webinars, David explained that he had just increased his salary by $50,000. 

![](/assets/david_espinola_review.jpg)

I told him that's just the beginning.

![](/assets/david_espinola_response.jpg)

How was David able to get a job at an elite company like Microsoft?

What skills was David learning that he could transition so quickly?

The rest of this post will show you how David did it. This post includes 4 case studies, research and 2 surveys all to answer questions like...

* What skills separate data scientists like David from everyone else
* How to pick a language (and your choice may surprise you)
* How to pick a development environment
* How to learn the skills
* Why this approach to learning skills works (based on examining 4 case studies)

# Why you should learn these skills

If you want to become a data scientist, you'll need to learn how to generate value for your organizations. I've written about [how data scientists create value for organizations here](https://www.business-science.io/business/2019/05/23/how-to-become-a-fiancial-data-scientist.html). But, in general, you complete a process (called the data science process), which involves learning these skills. 

![](/assets/data_science_plan.jpg)

Data scientists that learn these skills can convince employers to hire them. And that's really important. Here's why. 

According to Glassdoor, learning these data science skills [can have this effect on your salary](https://www.glassdoor.com/Salaries/pittsburgh-data-scientist-salary-SRCH_IL.0,10_IM684_KO11,25.htm?clickSource=searchBtn) (if you live in Pittsburgh PA like I do, I encourage you to check your own locale). 

![](/assets/glassdoor_data_scientist_salary.jpg)

<p class="date text-center">Glassdoor: Data Scientist Earnings in Pittsburgh, PA</p>

But that's just the start. 

Like I told David, your career will accelerate. 

Here's what the career path looks like for a [Senior data scientist in Pittsburgh PA](https://www.glassdoor.com/Career/senior-data-scientist-career_KO0,21.htm). 

![](/assets/data_scientist_career_path.jpg)

<p class="date text-center">Glassdoor: Senior Data Scientist Earnings in Pittsburgh, PA</p>

Now I know what you're thinking: _"That salary is great. BUT, I'll never be able to master this list of skills. Especially not in 6-months."_

Actually you can. 

Here's how.  

# How to master learning data science

Mastering data science isn't hard. It just requires:

* Motivation: You'll need to dedicate about 10-hours per week 
* A plan: You'll learn from David and several others in this 

You'll need to start by picking a language. 

# 1. Pick a language

Who do you think is going to win this battle?

![](/assets/r_vs_python.jpg)

Well it's neither.

Because C++ is the true superior programming language. 

![](/assets/c-plus-plus.jpg)

I'm just kidding. 

But in truth, it actually doesn't matter. You can succeed with both. 

I know both. 

I teach both. 

But, if we want to really answer this question, we should tackle this like data scientists. You know, with data to support our decision. 

![](/assets/data_scientists_ibm.jpg)

So, let's tackle this like data scientists. 

## How to pick a language

If I were picking a language for the first time, I would consider a few things:

1. How useful is the language for data science
2. The demand for the job market
3. The competition in the job market

## 1.1 How useful is the language for data science?

So if you look at [the history of python](https://www.geeksforgeeks.org/history-of-python/), it clearly says it is a general purpose, high level programming language. It has an emphasis on code readability and to express concepts in fewer lines of code.

![](/assets/history_of_python.jpg)

<p class="date text-center"><a href="https://www.geeksforgeeks.org/history-of-python/">GeeksforGeeks: History of Python</a></p>

Meanwhile if you look at R, it was closely modeled on the S language for statistical computing and graphics.

![](/assets/what_is_r.jpg)

<p class="date text-center"><a href="https://www.r-project.org/about.html">R-project: What is R?</a></p> 

So Python is a general purpose language (but has been adapted for many tasks like data science) while R has been developed for the sole purpose of statistics. 

But I wasn't satisfied with that, so I dug a little deeper. Here's what I've found. 

![](/assets/data_science_language_strengths.jpg)

* **Python:** Great for Machine Learning and Deep Learning but misses the mark on reporting (very important) and has fewer libraries for important analyses like econometrics. 
* **R:** Has well developed tools for business analysis and data science. Strong in everything except deep learning. But, deep learning is rarely used. And when you need deep learning or extra APIs, you can integrate R with Python. 

So I'm going to give this one to R. 

## 1.2 The demand for the job market

Next is demand for the job market for Python and R. Currently there are 21,271 Data Scientist jobs for Python. A

![](/assets/data_science_jobs_python.jpg)

And, there are 8,713 Data Scientist Jobs for R. 

![](/assets/data_science_jobs_r.jpg)

So for every 1 R data science job there are 2.4 for Python.

I'll give this one to Python.  

## 1.3 The competition in the job market

Next, what we need to consider is how many people you will be competing against to get these jobs. 

* **Python:** There are over 8,000,000 people that know python (and that number is growing fast)
* **R:** It's estimated that 250,000 to 2,000,000 people that know R and that number is also growing fast. 

So for every 1 R user there are potentially 4 to 32 more python users. 

![](/assets/python_r_competition.jpg)

So R positions are going to less competitive by 10X or more. Dang!

This one clearly goes to R. 

## R is a solid choice

R is a solid choice, and it's one of the reasons that students like David are able to quickly transition into a data science role. And keep in mind, you can always pick up Python later.

![](/assets/python_vs_r_results.jpg)

# 2. Pick a Development Tool

Next, we need to pick an integrated development environment (IDE). This is a fancy term for the thing you will type your code into. 

![](/assets/rstudio_ide.jpg)

<p class="date text-center">The RStudio IDE</p>

I ran a poll to see what everyone's using for R (and I did the same thing for Python too if you want to see those results).

## What's their favorite way to code in R?

Here's the results. It's a landslide victory for RStudio. If you are going to learn R, pick RStudio. 

![](/assets/r_poll.jpg)

## What's their favorite way to code in Python?

I ran the same poll for python. And here's where it gets more complicated. About half enjoy coding in Jupyter but a third like VSCode, and some are even using RStudio to code in Python! So it becomes much tricker to pick the right one. 

![](/assets/python_poll.jpg)

Keep in mind of my 61,000+ followers, many are likely to be people who follow my content and therefore are interested in R programming in addition to python. But still, it's not an easy decision for python users. 

# 3. Learn the skills
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