Post Menu and Details.
- Work in Data Science Without Being a Developer
- Why data science?
- Ways you can break into data science without coding
- Two tracks to become a data scientist
- Final thoughts
Reading time: ~4 minutes
Do you want to get into data science, but coding is not your thing? I get you. I don’t have a lot of programming expertise, but for some basic HTML and Python for Data Science (that I taught myself). Still, I’ve managed to work in amazing, well-paid tech and data science-related positions.
Work in Data Science Without Being a Developer
So don’t be discouraged. In this post, I’ll show you seven ways you can get into the awesome, well-remunerated data-science field without being a programmer.
Data science is a discipline that focuses on giving meaning to raw data, converting it into actionable insights. There is a short supply of specialized data scientists, and with the growth of big data, the demand for this career is soaring.
Being a data scientist is relatively well paid, with a salary range in the U.S. from $82.339 and $166.855 per year, according to Glassdoor.
First, a disclaimer. You need coding for many data science jobs. In most cases, to work in a data science project, or app development, you need to code. Still, there are many data science roles that don’t require programming expertise.
While you don’t need to be an expert programmer, you do need data science knowledge. No-coding doesn’t mean any math or statistics. You should be comfortable around statistical concepts, variables, and most of the data science curriculum.
So, here are six jobs you can do in data science without being an expert programmer.
Companies transitioning to a data-driven business need help to tackle this challenging digital transformation. They look for consultants to help them coordinate the transition and benefit from leveraging data for business decisions, strategies, and processes.
Achieving a company culture that uses data as a resource requires more than a cool tech stack. It requires changing the culture and mindset of employees and management. You can help devise and implement strategies that enable the company to achieve those goals.
A data scientist typically uses several software tools for managing, analyzing, and visualizing data. How can they familiarize themselves with the technical functions of all these solutions? Technical writers help with that.
Technical writers produce helpful and official software documentation. They also write manuals and operation guidelines which explain how a specific software works and what you can do with it. For example, below you can find a tutorial overview from https://dagshub.com/, a comprehensive data science portal and platform.
As a technical writer, you need to understand both software and data science. You’ll work with users, developers, and marketing teams to create documentation that meets legal requirements and helps the user.
You must understand how the software works, which data science processes it addresses, which methods are used, the software applicability to different use cases, and the user profile. The technical writer plays a critical role in helping the user get the most of the software.
A person recruiting for tech, AI, and machine learning positions, needs to understand the basics of the jobs they are hiring for. They need to understand what the job entails, what to look for in a candidate, and more. That’s why large enterprises have a specialized recruiter for data science and tech positions.
These recruiters have deep knowledge of data science and can detect an error or flaw in a CV. If you have good communication skills and empathy besides technical knowledge, starting out as a technical recruiter might be an option for you.
A typical team of data scientists would work on a lot of projects. However, not all data scientists are good project managers. That’s why large organizations or complex projects hire project managers to supervise and coordinate.
When you work as a project manager, you’ll plan, manage, and execute data science projects. You’ll measure how the project is progressing and what its risks are. Therefore, you need to have not only project management knowledge but data science skills too.
Data scientists often need to present their results in a report. The trick is that, most probably, your audience will be businesspeople with little or no data science knowledge. Thus, it is important to have understandable reporting and data science visualization that shows non-tech users the results. That’s why in large organizations working with data science, you’ll find people that specialize in creating outstanding visualizations for the data.
While you don’t need coding experience for this job, you need to be an expert in data visualization tools, such as Tableau, QlikSense/QlikView, and PowerBI.
There is a growing trend for no-code tools, and it includes data science platforms now. The increase in the number of no-code solutions for data science comes from the shortage of skilled personnel and it enables non-tech staff to help with data science modeling.
No-coding-required solutions also reduce manual errors and speed up the development process. To work with these platforms, you’ll need to have deep data science knowledge. You work the same as with tools that require programming, from preparing and cleaning the data, data engineering, modeling, testing, and more, but the tool has all the coding already built-in.
Skilled data scientists are in high demand and it’s a career that many people are interested in. So how do you start? There are two main tracks people entering the field take.
Coming from STEM:
People with an advanced academic degree in a scientific field can easily transition into the data science field. Typically, you’ll have strong mathematics, algebra, and statistics skills you can transfer.
The data science track:
Fresh data science grad students, with engineering or statistics degrees that are interested in data science, often opt for doing an MSc in the subject. These degrees are very comprehensive and include a capstone project or thesis.
As you can see it is a myth that you can only be a data scientist if you have great coding skills. There are plenty of jobs in the field that can be done with little or no coding. What matters most for a successful career in this industry are not the coding skills but the data science, analytics, and problem-solving skills.
Thank you for reading!