A career in data science is not the same as a career that involves large amounts of programming. A data professional uses programming as a tool to extract valuable insight from large data sets but is not expected to be an expert in coding. However, the ability to identify and use programming functions and libraries to extract insight from data is a vital characteristic of a trained data professional.
1. Main difference between a programmer and a data scientist
The goal of a data scientist is to wrangle and analyse structured and unstructured data sets using a range of tools and techniques, including programming and statistical models to extract insight that will directly improve business processes.
A programmer is generally tasked with building new applications by writing code that improves or updates the functionality of existing processes and software products.
Therefore, the biggest difference between a data scientist and a programmer is the level of programming knowledge required and the way it is applied on the job: a data scientist is more concerned with the analytical side of things whereas a programmer focuses more on the product development side of things.
2. Can I become a data scientist without knowing how to code?
If you are not a programmer and you have been struggling thinking about the amount of programming you will be forced to do as a data scientist, take a deep breath. You are not required to be a programmer by profession to have a promising data science career.
(If you already know how to code, skip ahead to point 4).
The real question is: “Is there a way to complete my data science tasks that require coding if I don’t know how to code?” …
A true data scientist doesn’t need to know how to code everything, they just need to know how to use coding functions and libraries for data science to make sense of data in order to find solutions to business problems. Python is the data industry’s programming language of choice. Yes, you can work as a data scientist without being a coding expert providing you understand how to use Python to extract the level of data insight your project requires.
This brings us to employer preference. The decision to hire a data professional with or without coding skills will ultimately depend on the employer and their business needs.
For example, an employer may need you to have programming skills for their data scientist role if you will be tasked with using Python for statistical programming on the job.
Alternatively, an employer may not require you to have programming skills for their data scientist role because you won’t be required to constantly code on the job. Your role could heavily involve using cloud applications to perform data analysis or using drag and drop interfaces that automate and optimise parts of the data analysis process, giving you more time to evaluate your findings and communicate them to stakeholders.
Employers today recognise the overall business benefits and importance of hiring data professionals with an aptitude for soft skills as much as the technical requirements of a job. In today’s data science job market, a data scientist’s ability to adapt, consult, communicate and find creative solutions is valued just as much if not more than an extensive technical ability.
We’re here to help you successfully launch your data career so, our recommendation? Learn to code in Python but learn to code in Python for data science. If coding isn’t your forte at the moment, you could also start off in less programming heavy data science roles. Give yourself the time to continue to build your programming skills while still exercising the rest of your data skillset.
3. How to build up your Python programming skills for data science if you are from a non-programming background
If you are not from a coding background, learning how to code in Python will help strengthen your career prospects as a data scientist because it’s the coding language demanded by industry, due to its user-friendly nature and open source accessibility. The Python programming environment also has a growing open-source library, which is helping industry progress together for a data-driven future.
The fastest way to learn the practical applications of Python in data science as a non-programmer is to take a vocational training program taught by industry experts with industry experience. These programs focus on teaching you how Python is used to extract data insight and gives you the opportunity to apply this knowledge in an industry setting.
To prepare for a training program like this, try these 3 tips to build your Python programming skills for data science:
- Start with self-learning – yes, it’s hard work but often the hardest task is to start. This can take a number of forms, you could read about the practical applications of Python in data science via industry blogs, practice coding using Python on free online sites or watch videos of others using Python for data science. The goal here is to familiarise yourself with the Python programming environment and understand its purpose in data science. With online resources at your fingertips, you could start right now!
- Practice until Python becomes part and parcel of your data science skillset – it’s a common misconception that you need to be an expert in programming to be a successful data scientist. Not true! You need to learn how to use Python to extract data insight and you need to learn how to do this well. Learning how to use Python for data science, data analytics and machine learning will also help you to future-proof your career. More and more businesses are looking to make the switch to Python and if you can provide them with the ability to use Python for data science, you will be who they need on their team.
- Land an unpaid internship where Python is a requirement – the true objective here is to give yourself the opportunity to apply your skills in a professional setting without the added pressure of trying to keep your job. Landing an unpaid internship will help you gain confidence in your ability to use Python on the job with guidance from mentors and less accountability as you are still in the learning stages of your journey.
4. I already know how to code and have experience as a developer, why should I become a data scientist?
You will possess the skills employers want to hire. Did you know, that if you upskill to data science, data analytics, machine learning and AI from a software engineering or developer background, you will instantly become one of the most in-demand data professionals on the market.
Employers are looking to hire data professionals with domain knowledge, technical know-how and ability to communicate and consult with stakeholders. A diverse skillset will equip you to provide businesses with something they struggle to find: one employee solutions. Hiring you would mean they could task you with wrangling business data, analysing, visualising data insight and then building a solution using your development skills based on this insight.
3 reasons why you should switch to data science from software engineering:
- You’ll be upskilling, not upending your career – software engineers and developers have transferrable skills to data science. Your ability to code will make it easier for you to upskill and learn the data science and analytics tools and techniques required to create predictive models and machine learning algorithms. This will enable you to extract data insight and help stakeholders improve business decisions and implement solutions.
- You’ll be in-demand across industry sectors – professionals with dual skillsets are in high demand and low supply. By becoming trained and gaining skills that can help any business achieve their data-driven initiatives in addition to the development of their products and systems. You will immediately increase your career prospects and earning potential by demonstrating the key characteristics of a data professional that employers are looking to hire: the willingness to adapt to industry needs.
- You’ll become a leading team member – a software engineer or developer with data skills will become a vital team member which stakeholders look to for insight and guidance. You will become a trusted part of the decision-making process when stakeholders are trying to gain a competitive advantage and make sense of their digital data in order to identify trends and implement changes to boost profits based on consumer needs. You will become the person that can help them analyse consumer data and have the aptitude to work with a team or independently to build and test the solution as well.
5. Is it better to know how to code before transitioning careers to data science?
If you have no coding experience and want to work in data science, you’re not being tasked with the impossible. You can grow your skills and become trained in the level of programming expertise needed to complete tasks on the job, which is the applicable level of programming knowledge employers will expect you to have.
If you have coded before and want to work in data science you are one step closer than professionals that can’t code but you are not yet equipped with all the skills required to work in the industry as a data professional. You are however, in a perfect position to learn and grow your career.
So, knowing how to code before upskilling to data science would definitely help you in your learning journey but data science is not all about coding, it is all about understanding data.
To become a data scientist, fill the industry’s growing skills gap and meet employer demands, you need to be prepared to keep learning and aligning your skillset with changing industry standards. Employers want to hire data professionals that are ready to adapt to evolving business needs.
Now is a strategic time to become a professional employer’s ‘must-have’ on their team. If you are currently in a role where you have mastered the domain knowledge and want to expand your career prospects, take the initiative to upskill and secure your career for the future by acquiring the data skills industry needs the most.