Data science has emerged as one of the lucrative fields to pursue. There is plenty of demand for data scientists in India. A career in data science can be extremely challenging at times. Data scientists face serious challenges in their day to day operations. Here are the 4 most common challenges faced by data scientists.
Data scientists need to involved in the ‘why’ of making things happen. One of the most common problems faced by data science professionals lies in analyzing a problem and designing the solution to fix it. Often data scientists opt for a mechanical approach and work on data sets, tools without a clear definition of the business problem.
Solution: A well-defined workflow before beginning the actual data analysis work is an important step towards problem identification. The first step in this process is to identify the problem and design a solution to tick off the most important steps.
Data scientists need to access the right set of data to come up with a relevant output. Gaining access to a variety of data in the most appropriate format is a crucial part of data scientist’s daily job. Data can be spread unevenly across various lines of business but getting access to that data can also pose a challenge.
Solution: Data scientists need to master data management systems and other tools like Stream software, which is useful for filtering and aggregation of data.
Data scientists need to be good with high-end tools and mechanisms. The job role requires them to perform as a link between the technical and non-technical teams. Along with the deep technical foundation, they need to have domain expertise in whatever department or area they are focused on. You can easily enter the field with confidence provided you have the necessary skills to take you to greatest heights.
Solution: As a data scientist, you need to work a lot on gaining insights into the business, understand the domain-specific problems. You also need to focus on the business requirements.
Data security has emerged as one of the biggest and business critical issues of today’s world. Since data is extracted through a number of interconnected channels, it is vulnerable to hacker attacks. Data science professionals face obstacles in data extraction, usage, building models or algorithms. The process of obtaining consent from users causes the major delay in turnaround time for data scientists.
Solution: There is no shortcut to this challenge. Data scientists have to comply with the established global data protection norms. Companies need to actively use advanced solutions that involve machine learning to safeguard against cybercrime and fraudulent practice.