In this guide, we’ll discover the qualifications/skills of data scientists based on data analysis.
If you want to become a data scientist, it is crucial to know the qualifications of the job. Let’s find out about it in a data scientist’s way! Given that the goal is to find a job as a data scientist, we’ll focus on what the employers are asking. We’ll analyze and summarize information based on actual job postings.
After reading this article, you’ll discover:
- Top 10 tools required for data scientists
- Top 10 skills needed for data scientists
- What is the minimum education required
- Comparisons of the above to previous year’s results
In the end, you should know more about the skills you should focus on and put them on your resume!
Please note that we’ll only cover the technical skills of data scientists since soft skills such as communication could be hard to summarize based on analysis. But of course, soft skills are also important for a data scientist.
Let’s get started!
Before we dive into the results, let’s briefly talk about the analysis methodology.
As mentioned, the analysis is based on actual job descriptions. We’ve included 3,679 job postings of search keyword ‘data scientist’ from 8 North American cities in November 2021. If you are interested in learning about the detailed analysis, please check out the previous analysis: How to use NLP in Python: a Practical Step-by-Step Example.
All right! Now let’s look at the analysis results.
Top tools required for data scientists
To process and analyze a large amount of data, data scientists rely on tools. Some companies are strict about the tools needed; some companies are open to different tools/languages. But there’s no doubt that knowing popular tools is an essential qualification for data scientists.
Among the job postings included in our analysis, 91.5% listed specific tools for the jobs. Below are the top 10 tools required for data scientists by employers:
- Amazon Web Services
Programming skills are important qualifications for data scientists. And Python is the indisputable most popular programming language for data scientists. 68% of the job postings mentioned Python. Some of the job descriptions went even further to specify Python-related packages such as TensorFlow, PyTorch.
Because Python is such a powerful yet relatively simple language. It not only offers tons of easy-to-use data science libraries but also has other general functionalities. For example, data scientists can also use Python to automate tasks, connect with cloud services, and participate in web development.
So if you are looking to become a data scientist, Python is a good starting point!
Further learning: if you are new to Python, the below courses can help you reach the intermediate level.
For Python basics, check out the FREE Python crash course.
To learn about Python for data analysis basics, check out the course Python for Data Analysis with projects.
SQL also appeared in 46% of the job postings. Nowadays, many companies in various industries are still using SQL-driven databases to store information. So it is a good idea to know this classic database query language. You can retrieve data and perform simple data analysis with SQL.
Further learning: if you are new to SQL, the below resources should get you fluent with the tool.
To learn SQL for data analysis, check out SQL Tutorial for Beginners: Learn SQL for Data Analysis.
To learn SQL for database management, check out Quick SQL Database Tutorial for Beginners.
R is often considered a competitor for Python. But, according to our job posting analysis, it’s not even close. Only 37% of the data scientists’ job descriptions mentioned R. With that said, R does have a solid academic background. This means the newest statistical models often get implemented in R before Python.
Cloud has grown its popularity in recent years. In general, it refers to the internet-accessible servers and their related services. More companies have moved their businesses to the cloud. As data scientists, you can take advantage of different functionalities of the cloud, including data storage and computing power. According to our list, among the various cloud providers, the most popular one is Amazon Web Services (AWS).
Further learning: if you want to start using cloud services, try using Jupyter Notebook on Google Cloud with our YouTube tutorial.
Big data-related tools such as Spark are in demand as well. They are essential when the company has a substantial amount of data.
As we all know, a picture is worth a thousand words. Data visualization is a critical part of data scientists’ jobs. Even though Python and R offer good visualization packages, some may still prefer a more intuitive way. Tableau provides an interactive platform that doesn’t need much coding.
Java is valuable for some employers as well. It is a general-purpose programming language that’s very popular among engineers.
The top 50 list of tools mentioned in data scientists’ job postings is below.
Note: each tool category is not counted mutually exclusively in the job postings, i.e., one posting could mention multiple tools. The ‘nothing specified’ means the job description didn’t list any main tools.
To catch the trend, we’ll also compare the list of the top tools to the same analysis conducted last year. Here are some highlights:
- Python, SQL, Cloud have gained popularity y-o-y.
- Spark, Hadoop, SAS have dropped in ranking y-o-y.
You can find out details about the top tools from last year below.
Top skills required for data scientists
Besides the tools, data scientists must also have technical skills to succeed. The top 10 skills required by employers are:
- Machine Learning
- Natural Language Processing
- Deep Learning
For many, machine learning is the only thing that data scientists do. That concept is not true, but it does have a reason. 66% of the job postings mentioned machine learning in the qualification descriptions. Data scientists need to know algorithms such as supervised learning, unsupervised learning, reinforcement learning, also models such as logistic regression, decision trees, neural networks.
Further learning: Machine Learning for Beginners: Overview of Algorithm Types
Statistics knowledge sets solid foundations for data scientists. 60% of the job postings mentioned it. We should learn basic mathematics, probability theory, data collection, experimental design, and other statistical concepts to become data scientists.
Research is also considered a vital skill for data scientists. 48% of the jobs talked about it. Data science is a rapidly developing field. Data scientists must be creative and adaptive; to explore and apply new concepts.
Data visualization is also gaining popularity. Data scientists need to present their analysis results as easy-to-understand charts or dashboards.
Subsets of machine learning or statistical skills, including prediction, recommendation, optimization, natural language processing (NLP), deep learning, are also in demand. This depends on the job since each role requires a different skill set. For instance, a credit card company needs data scientists to predict the customer’s behavior; a media company wants data scientists to analyze text and videos.
The top 50 list of skills mentioned in data scientists’ job postings are below.
Note: like tools, each skill category is not counted mutually exclusively in the job postings, i.e., one posting could mention multiple skills.
To catch the trend, we can also compare the list of the top skills to the same analysis conducted last year. There are no significant changes y-o-y. You can view details about the top tools from last year below.
Minimum education required for data scientists
Lastly, let’s look at the minimum education qualification for data scientists.
- 49% of the job postings asked for a bachelor’s degree as the minimum education level.
- 28% mentioned a master’s degree or above.
- 6% asked for Ph.D.
- Only minimal postings asked for MBA.
- 17% of the postings with degree requirements ‘not specified’ could be more open about it.
So good news, a bachelor’s degree in a related field might be enough. With that said, one would be more competitive with an advanced degree of master’s or Ph.D.
We can also compare the minimum education required to the same analysis conducted last year. There are no major changes y-o-y and you can view details from last year below.
In this post, you’ve discovered the qualifications/skills of data scientists. Hope now you have a better idea of how to become a data scientist.
Data science will continue to be in demand in the foreseeable future. Whether you already have these skills or not, it’s never too late to start learning!
We’d love to hear from you. Leave a comment for any questions you may have or anything else.
Editor’s Note: This post was updated and replaced the original post in 2020.