Data Science, AI and machine learning without programming

Regardless of your level of expertise, if you want to work effectively with data and enjoy it, you need to know about methods that do not require writing code.

The methods of working with data that do not require programming have become very popular and indispensable in our information age. If you use these methods, this does not mean that you do not know how to program. In fact, to work with these methods, you need to have some code writing skills. Data Science and artificial intelligence are quite complex areas for applying methods that do not require writing code, but a number of companies and startups have been able to use the full power of artificial intelligence to push the boundaries of technology. Now the tedious tasks of data cleaning and generating forecasts can be automated – and you don’t have to program for this. Obviously AI – One of the leading companies that managed to create a platform that enables specialists in the field of Data Science and AI to do their work without resorting to writing software code.

Why use code-free methods?

You might be wondering why I suggest that Data Science and AI experts start using the “no code” methods. In this post I am going to talk about the many benefits that the use of such methods provides. However, it should be noted that this approach is not intended to replace traditional methods of writing programs, but in order to complement and improve the work performed by specialists in the field of Data Science and engineers in artificial intelligence.

It’s super fast

Using work methods without writing code (similar to those that Obviously AI offers) gives you flexibility in work and allows you to reduce the time spent on solving tasks that require writing special programs by 5 times. Using the “no code” methods, you can save a lot of time when cleaning and processing data, and you can use this time to make other corrections, which will allow you to achieve maximum productivity. Obviously AI also gives you the ability to make amazingly accurate predictions based on your data! A good knowledge of concepts related to your area of work is also a plus and will help you understand the results of your forecasting done with tools like Obviously AI.

It is fun and interesting.

Using “no code” working methods can be interesting. Obviously AI has a very attractive user interface that gives you a constant feeling of engagement. You can literally see fascinating visualizations as you clear your data or make predictions based on them. Compared to traditional methods, working with data without writing your own programs captivates and helps you focus on the current task.

It improves productivity.

The combination of a good theoretical base and understanding of programming concepts and principles increases labor productivity when using tools that do not require code writing. The reason is that the work can be done very quickly, and you can concentrate on specific tasks without scattering your attention. You can work with large amounts of data in seconds and automate a number of tasks, which will save you a lot of time and relieve unnecessary stress. Machine learning without writing code allows enterprises to focus on making decisions and implementing specific measures, while working on the AI technologies they need without developing a cumbersome infrastructure.

About Obviously AI

Obviously AI has simplified methods of working with machine learning technologies to three stages. Now you can download the data, press a couple of buttons and generate forecasts based on your data without writing a single line of code. This platform allows anyone to use machine learning and makes it easy for everyone, regardless of technical background. Predict revenue, optimize your supply chain, personalize marketing, create personalities. Now you can know what will happen next. Find out more here.

Learn the details of how to get a sought-after profession from scratch or Level Up in skills and salary by completing SkillFactory paid online courses: