Why Should You Pick a Domain Specialization in Data Science Study?
Becoming a jack of all trades in data science is not the wisest strategy. Pursue a domain specialization if you want to make a secure data science career
Any knowledge is like a big ocean, you must swim across to stand on top. Every domain is essential in the market, and it is not easy to get domain knowledge. Domain knowledge makes you an expert in a particular domain.
Thorough knowledge of the industry will help you to work ahead of others in a particular domain. Domain knowledge will help you to understand different skills outside your existing skill. When talking about general talent acquisition, today, recruiters rank candidates with domain knowledge at the top. Data science will have a considerable impact more than past few years, and this would not be a generalized role. Domain knowledge is one of the most important skills to become a successful data science professional.
The internet and the advancement of applications have made education and learning much simpler. For example, many online platforms offer artificial intelligence, machine learning, and data science courses.
It is a long list but have you ever wondered if they offer a project completion certificate or if the course is domain specialized? They tell you all about the courses and offer you course completion certifications and projects. That's it. They will never tell you why domain knowledge or a domain-specialized course is so necessary today. But, recruiters look for domain expertise professionals rather than candidates with lucrative course completion certificates.
You might have gotten a shock. But it's a fact of the present day's data science industry in any domain.
Today, data science is the driving force of any industry more than ever before. Project certification with domain specialization is the future. Candidates applying for data science jobs include skills for algorithms developer, data engineer, data architect, data warehousing, machine learning engineer, or data analyst.
What does it mean by domain specialization?
A domain is a field of expertise a professional owns in a particular industry. For instance, if a person works in sales for a manufacturing company, domain specialization can help him advance his career and increase his level of specialization in the customer service field through data science and AI.
Domain knowledge is essential when applying for jobs in data science. For example, a software developer needs to build an app for a banking firm. Therefore, they must first understand the banking domain even before they start to create the app.
Let's further explore the importance and meaning of domain knowledge in data science.
What is domain knowledge in data science?
Domain knowledge in data science is the background knowledge by which different data science techniques are applied. This application can be data modeling, generating insights, data warehousing, data pipeline, and even making decisions based on these data. But nature, as well as the requirement of data, is different in every domain, and the segmentation is different, even in the market. So when you opt for a data science course, it's quite important that you pursue a domain specialization. Otherwise, your learning will be of no means in the job market. In fact, in the case of working professionals, this becomes the first criterion for having a successful career transition.
For example, the domain specialization can be banking, telecommunication, Oil Gas, healthcare, and many more.
Why is domain knowledge important?
Domain knowledge has been very crucial for the past few years. It even helps recruiters to know about their employees by their specialization. Employees with specified domain knowledge allow businesses to work on more projects with better outcomes in the minimal possible time. Domain knowledge is essential during a job search.
How does domain specialization help a data science professional?
Resume preparation :-
Domain knowledge in a resume makes the recruiter highlight a resume with domain expertise. Domain knowledge, when in a resume, can increase recruitment chances more than the one who has learned the courses and skills without the domain specification needed for the applied role.
Suppose you have applied for a marketing analyst position in a pharma company. You have data science certification along with marketing and pharma domain-focused data science knowledge. At the same time, a second candidate came with generic data science certification and some marketing working experience in an e-commerce company.
What do you think? Who owns the better chance of getting hired?
Requirement analysis :-
Experts with domain knowledge in data science can identify the business need as well as the respective solution in a better way. By static testing, they can even identify the defects faster.
Reduce training :-
Every professional who starts working must undergo domain knowledge and skills training. The organization has to spend less time on training if they recruit respective domain expert data scientists; it obviously lowers the training cost but increases the team efficacy. This is why companies now search only candidates with industry-specific data science knowledge.
Stand as an expert:-
When recruited in any firm, professionals are usually categorized as beginners or non-experienced. But with domain knowledge, you can say that you are an expert as you have all the industry knowledge even before you joined the organization. So, no worry about the designation or salary degradation.
On the other hand, domain-specific data science best practices are something that leads any data science project to its peak of success. For example, predictive analysis is the foremost thing that every data scientist needs to carry on. However, a predictive analytics project for the manufacturing industry will hugely differ from that of the pharma industry.
And here comes the significance of domain specialization in data science.
How do domain knowledge criteria in mid-to senior-level hiring help the organization?
It helps the organization to grow its workforce with team members honing specific domain specialization. In addition, it helps HR when hiring the right candidate for a particular domain. These tests can even be conducted with psychometric, behavioral, and cognitive assessments relating to the candidate's specific domains.
Professionals with domain knowledge are experts in a specific domain. Therefore, it even helps retain employers, and employees with domain knowledge stay longer in the organization and perform better.
How to master data science skills with domain specialization?
These are the few steps you can follow to increase your domain knowledge :
Improving domain knowledge starts by researching topics that you have an interest in. It will help you to learn and combine the information in an understandable and accessible format. Suppose you are from the sales domain and preparing for a data science career switch. Before you start attending your first data science course class, you must be well aware of what's the actual scopes and uses of data science in the sales domain. Do extensive research on the same.
Consulting an expert
Another way to build domain knowledge is by consulting or learning from an expert on a particular subject. Verified data science and AI experts will give you the appropriate ability. You can learn domain knowledge via live online and offline classes. While consulting an expert, It would be advisable to write down all the crucial inquiries in advance of the planned call, conference, or class. To improve learning, you can even write down or record the information the expert is attempting to impart.
Enroll in courses with domain specialization
Plenty of AI and data science courses exist there, but most of them do not come with domain specialization. So, choose a data science course with the domain elective module option only. All of these custom domains offer advanced certification programs, supporting job applicants and candidates for AI and data science fields throughout the hiring process.
When opting for AI and data science courses, make sure they offer domain specialization courses.
Try to opt for an online training program with hybrid classes (where you can do projects in offline mode), not video-based learning.
Work on capstone projects relevant to the domain you want to specialize in.