Importance of Domain Knowledge in Data Science

Domain knowledge is that area of data science which is hardly discussed compared to other areas like programming skills, visualization skills, algorithms or statistics.

In data science, having domain knowledge will ofcourse differentiates you from other experts of the field, but it won’t entirely differentiates you even, because having domain knowledge is different in terms of delivering work, handling out resources and procedure.

Domain Knowledge

Domain knowledge is knowledge of a specific, specialized discipline or field, in contrast to general knowledge, or domain-independent knowledge.

The role of a domain expert is much more specific, the domain in here will be data science so they must be having proficient knowledge in every processes that happens inside this domain. From the technique in selecting data to the technique of processing of that data, a domain expert should be well experienced in every step of process.

Responsibilities of a Domain Expert

Subject matter experts will be needed to know what goals a business is shooting for, what type of data to add potentially and provide a feedback loop. A data scientist will mostly have intuition over the model, about its working and whether the users will be satisfied by its way of working. Domain experts could only have explanation and foolproof idea about whether the application of AI has improved the business function or not. Understanding what it is that influences the decision-making process in the business domain, it will be best left to domain experts; data scientists will need to interface and work continuously to improve models. Subject experts understand the decision features, the decision influence and the business characteristics and translate it to data scientists.

Importance of Domain Knowledge in Data science

It is difficult for anyone in random to come up with project ideas in a domain without enough experience and knowledge, it will further be difficult to determine what type of data will be useful for that project. One will need to have experienced vision over the structure and purpose of a project, should have knowledge in what types of variables might be related to the expected outcome so that to be sure in gathering right type of data.

Knowing the domain is useful not only for figuring out projects and how to approach them, but also for having rules of thumb for sanity checks on the data. knowing the way how data is gathered, like whether is it hand entered or did it happen by the machines that could give false readings for obvious reasons? knowing this will help data scientist with data cleaning and can prevent them from going too far down the wrong path.

Usually the most challenging part in building a ML model is feature engineering, comprehending the variables and how to relate to outcome is extremely important. Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms, feature engineering can be considered as applied machine learning itself.

Once features are generated, knowing the possible relationships between the variables will be very credible, helps in keeping the process, being able to glance at the outcome of a model and determine if they make sense goes a long way for quality assurance of any analytical work.

Finally, one of the major reason why a strong understanding of data is essential is because you have to interpret the results of analyses and modeling work. Having idea about expectant results, knowing which results are important and which are trivial is important for the presentation and communication of results. It’s also important to know what results are actionable.

How to gain Domain knowledge?

Being a domain expert is not an usual thing, they hold a different purpose in Data Science which is difficult to pull off, only by having more than enough experience in working as Data Scientist or in any significant role one will gain the expertise in the domain. It is true that you will have to have practical experience in different categories of domain to become a domain expert but it doesn’t have to specifically come out in a traditional way. Things have changed now, now you can also be a domain expert by being trained to the role.

Numerous data science courses are available that will train aspirants to become a data scientist but very few courses are there that trains one to become a domain expert, because it is a risky job to perform. We know what is required to be learnt for one to become a data scientist, its curriculum is static but it is difficult to make one train in every process of data science to make them a domain expert. This is mostly why there are very few courses for domain experts.

As I told, things are constantly changing and with that the opportunity for you to be trained for the role of domain expert has also changed. There is a course specially designed to train students for major roles like domain expert, project manager, data manager, etc in Data Science domain. It is the Data Science course for Managers by Learnbay, this course is different from any other data science course, as it works towards training you for major roles of Data Science.