Understanding the Why of Data Science and Machine Learning Is More Useful than Knowing the How

Author(s): Suhas Maddali

Originally published on Towards AI.

Learning the purpose of data science can be useful, especially in order to create a business impact in the organization.

A large number of corporations are moving toward the field of data science and machine learning. There are industries ranging from pharmaceuticals, retail, manufacturing, and automobile industries that are seeking ways to promote their products and services with the use of intelligent systems driven by artificial intelligence. To make things interesting, they are being used in the development of software for self-driving vehicles that are going to take the world by surprise in the next 2–3 years. In light of this, it is important to learn the most important technologies and innovations taking place, especially in the field of automation.

To learn these new technologies and tools, there are a massive number of online courses that teach the fundamentals along with practical use cases. Courses such as “Deep Learning Specialization” by Andrew Ng and “Machine Learning” by Stanford University do a good job of highlighting most of the important concepts that are often discussed in the AI space. These courses are focused on teaching the fundamentals and practical use cases of AI and deep learning. There is still one missing ingredient that is needed by data scientists and machine learning engineers to shine in their careers. This ingredient is called “purpose” or the “why” we are doing machine learning in the first place.

Learn the Purpose of Machine Learning

It is a good idea to know the purpose of why we are learning artificial intelligence and understand why there is a lot of demand in this area. When we are given a data set and all of its features, attributes, and target variable, most often than not, we jump into the processing of data and finally use various models to see how well they are performing on the unseen data. There can be good chances that the models might be doing well depending on the data (considering that the data and target are related). If questioned about why the model gave a particular set of results, it is possible to use tools such as LIME and SHAP to help with the explainability of the models. There is still one part that is quite essential before we start our machine learning journey. Before a machine learning project starts, it is important to question a lot about the purpose of the project. Getting to know the purpose of the project can help us steer in the right direction and also ensures that there is business impact and usefulness of the models in real-time.

Consider, for example, that heart disease in a patient should be diagnosed. In order to perform this task, a doctor must take a lot of time and effort (not to mention research) on a large number of symptoms and identify their causes of them before giving the results to the patient. However, this is a laborious task, and it can take a significant amount of time and effort. If these tasks are replaced by robots and intelligent systems (powered by AI), it leads to a reduction in the time it takes to solve this problem, along with increased efficiency and patient satisfaction.

Similarly, taking a look at another interesting use case of predicting the sentiment of users based on their reviews can be quite useful for machine learning in this case. When online shopping companies such as Amazon get the sentiment of users based on the reviews, they would be able to save a lot of time and understand how a product is doing overall based on the sentiment of the users, according to the texts. Similarly, there is no requirement to hire manpower to complete this task of classifying the text into positive and negative sentiments. As a result of this, companies are able to understand the products that are doing well and promote them so that they can gain more profits and business value.

Conclusion

As we have seen in the above example lines that learning the purpose of machine learning and data science can be handy, especially when starting a project before learning how things are carried out. I would also agree that learning the “how” can be important, but the most important and initial part would be to know the why before progressing further. Furthermore, there are plenty of resources out there that can teach the how of machine learning—but crafting and understanding the why of machine learning can be a somewhat tedious and arduous task that should be done with deliberation.

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Published via Towards AI