Challenges of data science technology | intellipaat

Identifying the Issue: The most difficult challenge data scientists face when examining a real-time problem is identifying the problem. They have to make the data readable for the average person in addition to understanding it. The analysis s conclusions ought to help the company fix its major kinks and hiccups. To make data meaningful, data scientists can use dashboard software that provides a variety of visualization widgets. Data quality: Deep learning and machine learning algorithms are more powerful than human intelligence. Algorithms are excellent at learning to carry out every instruction precisely, but the issue arose when the data was poorly curated. For instance, Microsoft s Tay chatbot became disorganized after learning about tweets posted online. Machine language is both a blessing and a curse because it gives computers the incredible ability to learn new information quickly, but they can only repeat what is taught to them. As a result, data quality is crucial, and data curation will be a herculean task for data scientists. Data Quantity: A data scientist s top priority is the creation of a strong model. Even a challenging problem necessitates a rigorous model with more significant model parameters. The more model parameters, the more data is needed. Finding high-quality data to train those models is also very difficult. Even algorithms or unsupervised learning need a lot of data to produce meaningful results. Multiple Data Sources: Data scientists can access the vast and wide range of data from numerous platforms and software thanks to big data. The data scientist, however, faces a challenge when handling such massive amounts of data. When used properly, this data will be of greatest use. Virtual data warehouses, which can efficiently connect data from numerous locations using cloud-based integrated data platforms, could help to some extent in solving this problem. The more comprehensive the data set, the more insightful findings there will be.



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