Top Ten Common Prejudices About Data ScienceOctober 10, 2018
Data science is ruling almost every society today. From marketing to retail, hospitality to travel, entertainment and sports along with finance and insurance, data science is everywhere. Data science has left no stone untouched to deliver what it claims and hence adds value to society. Owing to such a robust technology, several enterprises are finding it hard to cope with the same. Few beliefs it’s a threat whereas some consider it a massive success.
In the cacophony of above, there are quite a lot of people or say industries that are still not open to this technology and consider the term daunting. Many organisations are struggling to uncoil its true meaning. So, here we unbox few of the myths about data science rumoured in the digital world.
10 Most Common Misconception About Data Science
- Data science finds its application only in humongous data: Contradicting, It’s not always necessary to have a pool of data to perform operations. Data science can very well work on Volume-based, veracity based, velocity based and variety based data.
- The higher the data density, the more accurate the results are: People often believe in the more, the merrier. However, this does not imply to data science. The more significant amount of data calls for complexities and boosts oversights. Quantity is not proportional to quality. Exposure to a set of data would make you wander from where to start.
- Artificial Intelligence would soon replace data scientist: The year 2012 tagged Data Scientist as one of the sexiest hops across the globe. We need Data Science to implement artificial intelligence. But considering the former to overtake or entirely replace is not feasible. Data science helps in gathering data required by AI, but the way the AI functions need the guidance of data scientist.
- All a data scientist needs to do is learn a tool: Few of the lot considers data science to be as simple as learning an instrument. But no, data science scans beyond. Data science spawns algorithm and tools and is way beyond just learning to code. You need to dive in deeper to have an understanding of the data and work upon the statistics to shine better.
- Data Science Do Not Yield Monetary Benefits: Data Scientist study insights of data and then strive to guide you further. Data science applied to the channels of marketing of the company helps you optimise them and then invest in the best. This is a way would boost ROI.
- Business Intelligence and Data Science Are Same: Where Business Intelligence is all about what, when, how, who, data science scrawls across why did it happens, will it happen again? Data science is about prediction whereas business intelligence is about reporting or drafting a visualised representation of data.
- Data Science Is Big Data: This is one of the most excited prejudices related to data science. Giving the controversy a more unobstructed view, Big Data is data science, but data science is not big data. You can consider big data as a subset of data science.
- Data Science Is Magic: Well it might seem so, but technology is never magic. No push buttons or magic wand to provide results. It requires skills and expertise in the domain to do things that appear like magic.
- Data Scientist Must be Proficient In Coding: Yes they must know how to code, but it does not signify that they must excel. Nonetheless, data scientist need to analyse data and statistics to predict and not sit to code.
- All Data Owns Respective Value: Partly true but mostly a myth. Just because you have data does not notice that you can solve issues. Not every data aids results. You need to have appropriate data to optimise results.