Updated on: November 2, 2021
Today, data science has many opportunities for people and has better career prospects for those that have some knowledge and skills in it. But still, the market lacks too many development criteria. Even though there are many challenges that are being faced and a lot of massive data in the market, there is a shortage of experienced data scientists who can handle it properly. Learn What makes Data Science Hard To Learn?
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This all happens due to the skills gap which hampers the field of data science. Let’s discuss the problem faced by data aspirants and how this data science is difficult to learn.
What makes Data Science Hard To Learn?
The issue arrives while learning Data Science is given below:
- Hard problems: The profile of a data scientist is to tackle hard problems, which are mainly focused on developing models for businesses. To solve these problems, a high sense of problem-solving mathematical aptitude is required. A professional data scientist is required to generate insights and search for patterns after analyzing the data. These data scientists are experienced enough to solve hard problems. As it is a complex field to understand, especially for those who do not have any experience prior,
Many companies are facing problems while selecting experienced candidates in this field, and still, there are some variations in the massive ocean of data problems. For many industries, this is still a big challenge to think about. In view of solving these issues, companies are required to have an analytical approach and must understand the problems thoroughly.
- Large scale data: In today’s world, a large amount of data is present and is increasing day by day at an exponential rate, and one day it will definitely become a problem for data scientists. A data scientist is required to analyze all the big data and needs to generate insights and derive meaningful information. But managing all this huge data becomes a burden sometimes for these data scientists. Sometimes, the data which is presented is not always organized in the proper way, i.e., it is not in the proper rows and columns, which additionally creates challenges for the professionals.
There are some big data tools like Spark and Hadoop which are utilized by these data scientists to handle this big data. It is very difficult to work in multiple roles as the primary job of a data scientist is to analyze data.
- Technical Expertise – Data Science has its roots in a variety of disciplines, including statistics, mathematics, and data programming, all of which help to shape the data science structure. expertise, one must understand the subsets of these disciplines too. Getting expertise in all these three fields is not quite possible at one time, but individually, it can be easier to acquire knowledge and get experience. Sometimes, it takes years for an individual to become an expert in one single field. Let’s say in order to become an expert in a programming discipline, it takes years to become an expert in some domain. The same is true in the case of statistics, where the same time period will be abolished to clear that up.
Right now, in today’s digital world, programming has really become a skill that each and every person is going to learn. As a result, many data science professionals now hold PHD degrees in subjects such as statistics or finance. So in order to become an expert in this field, you must become an expert or master in these disciplines.
- Domain Knowledge: A data scientist does not become a perfect professional while solving projects, camps, and getting knowledge with the help of online resources. Well, these are only fundamental concepts. The main knowledge he acquires is the domain knowledge that brings him into the main picture. As experience increases, domain knowledge also expands. Those who are professionals in the engineering and IT sectors might find it difficult to adjust to this new role in data science as they are dealing with customer sales.
Must read:
Beginners Guide to Data Science
Data Science Career Path
The aforementioned events are occurring because data science requires domain knowledge in order to identify useful variables, develop business models, and eliminate bias, which can only be identified with the help of this domain knowledge. Many industries are taking the help of data science experts nowadays. Every industry, such as hotels, banking, and hospitality, is incorporating or managing data science into their operations.
As we all know, the customer is the end user for many business domains, and these data scientists have the duty to manage the data in such a way to make better products with proper analysis and to achieve better results. For this reason, in-depth knowledge of it is required.
Final words
We can conclude from the preceding explanation that data science is a practical field requiring extensive practice, and that in order to solve its problems, we must approach it correctly. Many professional data scientist experts are lacking due to this highly difficult field, and that is why many universities are addressing this problem and are planning to start teaching structural knowledge to their students.
