The data science field is continuously expanding as our digitized technologies produce unprecedented volumes of information. The Internet enabled frictionless global information sharing, but at the same time emerged sophisticated data-capturing technologies, like the CERN particle accelerator, exponentially increasing the amount of available data.
Data scientists play pivotal roles in gathering, aggregating, interpreting, and visualizing information. In the top 100 best jobs list compiled by US News, the information security analyst takes a fifth, data scientist the twenty-second spot, followed by a database administrator, and market and operations research analyst professions.
Data scientists are welcome in most businesses, especially in large companies that deal with vast amounts of user or scientific data. They are essential in healthcare, gathering and interpreting large diagnostic datasets. Data scientists also optimize public transport, scrape the web to improve marketing campaigns, and work closely with machine learning algorithms.
As you can see, data scientists often work with projects aimed at public well-being, and this is where open-source technologies jump in. Unlike proprietary software, open source is usually aimed at solving problems that are common across many industries. For example, Facebook’s ReactJS open-source JavaScript library was not developed to drive more revenue to the company. Instead, it provides tools for everyone to build interactive user interfaces more efficiently. Simultaneously, Facebook became a part of the open source community, participating in developing the World Wide Web and attracting talent already familiar with their technology.
Open Source Software and Data Science
There are undeniable similarities between data science and open source. Firstly, most software was open source when the Internet was called ARPANET in the hands of the defense ministry and Cambridge and Massachusetts scientists. As science is always a collaborative effort, they shared the programs and code to develop a computer network system that the military could use.
Unlike corporate interest, open-source software isn’t usually a revenue-driving force. That doesn’t mean that businesses cannot profit by developing open-source technologies. However, in most cases, the core of the service is closed-source to protect corporate secrets and maintain a competitive advantage.
Data scientists easily adapt to open-source projects as they are used to collaborative scientific methods. Furthermore, unrestricted access to information is essential for data analysis, and there’s no better format than open source to manage publically available datasets. For example, Google and the World Bank grant free access to numerous datasets that can be used for space research, medical, or environmental purposes. Data scientists excel at extracting and interpreting such information to find correlations and shift research and development toward a solution.
To summarize, open-source software and data science align on many occasions. It is certainly possible to avoid using open-source tech as a data scientist, but those who successfully handle such projects bring great value to the workplace.
How to Begin a Data Scientist Career
Participating in an open-source project is one of the best ways to get experience before applying for a job. Sadly, many businesses look for overqualified developers setting unrealistic expectations. Juniors find competition especially hard, and open source can mitigate that.
It’s always best to show your skills with results. As a future data scientist, you can participate in projects that improve web scraping, data storage, machine learning software, etc. Remember that information security specialists are in the top 10 best jobs in the US, so data scientists oriented at cybersecurity can expect speedy employment and hefty salaries.
It’s worth mentioning that cybersecurity skills are becoming mandatory for most IT employees. Last year the FBI reported that losses from cyberattacks increased by 64%, and the primary cause for data breaches is human errors. In other words, businesses perceive cybersecurity as a serious threat to steady profits and business longevity, and data scientists that have at least basic cybersecurity knowledge are HR’s priority. Such knowledge includes:
- Data encryption. You should know how to store and transfer data in an encrypted format to prevent data leaks. Safely managing data transfers to and from the cloud server is a significant advantage.
- Personal online hygiene. Hackers should not be able to brute force your work-related accounts or intrude on corporate networks by hacking your email. Know how to protect business accounts with a password using a password manager, identify Phishing scams and social engineering, and remotely connect to business intranets via VPN software.
One of the data science prerequisites is knowing a coding language. Once again, open source proves an invaluable technology, as the primary coding language for data scientists is Python, which is open source. Although you can specialize in other languages, such as SQL, Java, and Matlab, the first steps will be much easier, focusing on Python.
Lastly, data scientists often deal with publicly available online data. Linux is a widely popular open-source operating system that powers 96.3% of the top one million web servers. Knowing your way around this OS will open lucrative data science career options.
Summary
We hope this article illustrates the importance of open-source software for the data science field. And if you decide to take this challenging yet rewarding career path, we recommend reading about the six essential Python data science tools to kickstart your career.