Data scientists face several challenges in their day to day professional life, demonstrating skills needed to convert data-based scientific inferences into easily accessible insights for businesses. As such, they are under pressure of straddling both the worlds – IT and business boardrooms. However, this software may not be the only way to achieve such luxurious goals.
Over-fitting is a common problem that data scientists have to face when they apply machine learning. There are many more challenges that go beyond machine learning and technology, commonly encountered irrespective of tasks at hand. Noted below are 3 common challenges data scientists have to face and overcome:
#1: Deducing the Issue
Data science is used to address specific problems. So, the first challenge data scientists encounter while examining a real-time problem is deducing the issue. They not only need to understand the data but they also have to make it understandable to others. The outcome of their analysis is commonly used to resolve major business glitches and problems, create efficient supply chain, nurture customer relationships, automate operations, establish strategic competitive benefits, and launch new revenue lines.
In order to resolve all these issues, it is very important to interpret the problem. Data scientists can use dashboard software such as ClicData, which offers an array of visualization widgets for making data actionable and meaningful. It may be beneficial in selecting appropriate tools for graphic display.
#2: Handling Multiple Sources of Data
The expanse of data landscape is vast. Big data allows data scientists to reach out to the vast landscape of data from various platforms as well as data sources. It can be the most useful tool if utilized properly. However, with multiple data sources, handling bulk data becomes a great challenge for data scientists.
Using cloud-based integrated data platforms, it is possible to build virtual data warehouses that can effectively connect data from innumerable locations, in different formats, at any time, collected in real-time as well as in batches. The more inclusive your reach the more useful insights and inferences.
#3: Predicting Outcomes
Exploring and predicting the outcome isn’t always the same as the final result. Even when a dataset is given, you may face unexpected results. In such cases, data scientists should focus on supervised learning for future exploration, model selection, and appropriate selection of algorithm should be used. Given adequate processing power and time, data scientists can develop models of rational predictive strength with little interpretation.
These are only a few problems almost all data scientists face at one point of time in their career. Whilst machines and software may be helpful, human ability is the key to theorize such issues, interpret the challenges, analyze and explore for better solutions.