Data Scientists: An Ultimate Guide For Them On Domain Knowledge
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About this a lot has been written. Both pro and con arguments have been made. According to proponents, understanding the domain enables us to formulate the best hypotheses, which data science may help test and either confirm or refute. Without domain knowledge, data science could become a protracted fishing trip in the ever-growing data lake. The "Iceberg of Business" could begin to thaw before you make the necessary adjustments.
Data science is currently all the rage! But, unfortunately, a quick Google search for the keyword returns hundreds of lessons & courses on the first page of results rather than a Wikipedia page for the subject.
Although not necessarily terrible, the readily available online learning tools encouraged many self- learners, including me, to dip our toes in the water. With it, it is easier to understand attempting to learn on our own. However, I was surprised to find out how infrequently, if at all, "Domain Knowledge" was mentioned in those resources. One can expect to test the traditional paradigm of generating hypotheses before modeling by the vast data sets available today, combined with the mathematical techniques and computational power to crunch these figures. However, with their approach to language learning, Google has demonstrated an entirely new way of comprehending the world without using any a priori models or hypotheses. So are subject-matter experts required? Is domain expertise required? "Domain knowledge" – what is it? How much is sufficient?
Every area of software engineering discusses the necessity for domain knowledge. For example, one requires domain expertise for business analysis and testing. But how much domain expertise is needed for data science?
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What Exactly is Domain Knowledge?
We must first comprehend what "Domain Knowledge" entails. This phrase comes from software engineering to understand the environment in which the target (i.e., software agent) functions. One can modify this concept for data science by noting that it understands the setting in which you process the data. To put it in another way, it is the understanding of the field to which the data belongs.
Why is this crucial for data scientists, then? Simply put, without adequate knowledge of the subject where the data comes from, you cannot fully exploit the potential of an algorithm. Tell us how difficult it was to construct a complicated data model in an unfamiliar field. It is easier to address a problem than we understand it.
On the other hand, having extensive knowledge in the field will significantly increase the accuracy of the model you wish to create. It is why data scientists typically have a thorough understanding of the various fields they work in. Of course, they might not be the most excellent experts in everything (after all, who could be?) A successful data scientist, however, typically concentrates on multiple areas of expertise.
How can I Develop my Domain Knowledge?
A long-term career strategy depends on developing domain knowledge. But how is this to be done? It's critical to recognize right away that this will take time. Try to work in the same industry for a few years for this reason.
Of course, you can still change occupations or employers. But one thing to think about while changing employment is sticking with the same application area for a time: Make an effort to stay put for a long when working on applications for the real estate, e-commerce, or insurance industries. Working in other departments of the same organization is yet another technique for broadening your domain knowledge in a particular industry.
Lastly, every industry has a unique body of literature; look for decent primers and review articles. Usually, you need more specialization than what you need right now. Last but not least, finding time to chat with people in this sector or attend professional conferences can be beneficial.
How Domain Knowledge Affects Data Science?
You may have studied data science and machine learning and used machine learning predictors on test data using algorithms like regression and classification. However, it is only possible to fully utilize an algorithm and its data when we have domain knowledge. In addition, using such data knowledge improves the model's accuracy.
When working with pertinent data, for instance, the expertise of the automotive business can be applied as follows: Let's imagine we have two features. First, using distance and volume as a starting point, we can use the method to add additional features like speed.
When we train a machine learning model, this may impact the output and lead to greater accuracy.
How Does Domain Knowledge Benefit a Business?
Contrary to what was said earlier, note how few contestants on Kaggle possess any significant topic matter expertise. Nevertheless, despite their absence, they continue to dominate the leaderboards and win tournaments.
And the reason for this is that, fortunately, someone somewhere had the intelligence to consider and simplify the process of making forecasts. Because Scikit-Learn and other high-level predictive analysis libraries handle much of the backend labor, the libraries nonetheless produce outstanding results even with the default settings. Any Tom, Dick, or Harry can train a model on the dataset and submit it to Kaggle with just a few lines of code, easily attaining at least a top 50% score on the leaderboard.
On the other hand, firms need help to maintain their position in the market while operating under severe financial and time limitations. Not to mention, they are in the market to sustainably increase their own profit margin. In addition, it's generally not financially feasible for most organizations to invest in internally building an algorithm tailored to their industry. As a result, they hired for the crucial Data Science position in the hopes that the new hire would aid in solving the issue they were having. Additionally, if a chance presents itself, seize it or move forward with it.
Process of Data Science and Domain Knowledge:
We will talk about domain knowledge's relevance to each step of the data science process in this section. The four sub-processes that make up the data science process are listed below.
1. Problem Identification:
Identifying the issue to be solved is the first step in any data science project. It entails setting the appropriate performance requirements after providing a general overview of the situation.
For a straightforward issue like predicting credit default, where the problem definition is as basic as estimating the likelihood of default based on information about previous borrowers, defining the problem is an easy first step. Consider a situation in robotics or medicine where a person with the necessary subject expertise can articulate the pattern they are searching for in the data.
2. Feature engineering and data cleaning:
One can gather most data regardless of the field, is rarely accurate and usable. You can use data cleaning and feature engineering to prepare the data for modeling. One requires data transformation for feature engineering and data cleaning. Data that has been incorrectly converted can produce false findings.
For instance, one might scale down cash flows while evaluating the relationship between, let's say, stock price and financial outcomes like cash flows. However, because the naive scaling procedure uses future data to scale past data, it would create a look-ahead bias in the data. Any analysis that uses data that you wrongly convert may produce erroneous results.
Additionally, selecting the features from the data that will have the greatest predictive potential requires domain knowledge.
3. Model construction:
Fitting a model to data is the first step in the modeling process. One can identify the problem in the first stage, and you can resolve it using the model created here. The effectiveness of the data science process depends on selecting an acceptable model. Again, the selection is based on the application area and is made better by in-depth domain expertise.
4. Measurement of performance:
The last step in the data science process, performance measurement, involves evaluating the model's performance using fresh data or data that wasn't used when it was being developed. Again, the selection of performance thresholds and measures is mostly influenced by subject expertise.
A false negative, which assumes a possible defaulter has good credit, is more expensive than a false positive when creating a model to forecast credit defaults, for instance (predicting a non-defaulter to be a defaulter). Different disciplines would exhibit these asymmetries, and it would be easier to identify them with domain knowledge. A person with domain knowledge is the only one who can accurately forecast the costs of model failure in the future.
Can Brand-New Data Scientists Learn Their Domain Knowledge Without Having any Work Experience?
Data science application in practice is industry-specific. Different industries employ different procedures, information, and techniques. SMEs should never be excluded from the process, but data scientists with domain expertise are better at fostering innovation and change because they can understand the perspectives of their stakeholders. Companies frequently seek employees with relevant prior expertise who can start working immediately as the demand for data scientists grows. Unfortunately, many new data scientists need help to acquire these abilities through traditional training or online learning. They frequently encounter job postings that request domain experience when they first enter the workforce. Thus, the never-ending cycle of relevant experience without the chance to earn it begins. Without context, understanding data science is akin to riding a bike for the first time. Although you know the necessity for balance and how to pedal, riding a bike is quite different. Even if most domain knowledge is gained through practical experience, novice data scientists can show their expertise and compete for jobs by adhering to these rules.
In Data Science, Domain Expertise is more Crucial than ever:
It is simpler to understand the advantages of engaging specialists to assist them with their data science needs as more businesses embrace the worlds of data, IoT, and the cloud. As a result, the range of industries data professionals may (and will) service will increase.
Data scientists must be ready for the various industries that are now adopting data-driven techniques, even though it is impossible to be a domain specialist in everything relating to data. Due to this, domain knowledge in data science is now more crucial than ever.
Why is Domain Knowledge Important for a Data Scientist?
A data scientist should be aware of three interconnected yet distinct components of domain knowledge, which can be characterized concerning the —
- The original issue that the company is working to address or profit from.
- The collection of specific knowledge or experience that the company possesses.
- The precise knowledge required for domain-specific data collection methods.
Therefore, it is acceptable to say that a Data Science role should strongly emphasize domain knowledge. A firm would only locate a qualified candidate for a Data Science position if the community preached about the same thing beforehand. But remember that despite all the advice, learning domain knowledge while working is still possible, and doing so is relatively easy. However, refusing to do so would be completely negligent.
This essay will persuade you that domain expertise is crucial for most data analysis initiatives, from problem formulation to results interpretation. However, you can only access domain knowledge in a few specific ways. The most typical approaches to get domain expertise are to 1) read relevant literature, attend formal lectures and presentations, and 2) build strong bonds with and work with domain experts. Ask yourself (or domain experts) the questions at each stage of your data analysis initiatives after you access domain information. Ideally, this will assist you in better comprehending and interpreting your data, goals, and outcomes.
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