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Is It More Data Or More Science In Data Science That We Need?
Artificial Intelligence & Machine Learning are undoubtedly the revolutionary innovations in the analytics industry that are transforming the 21st century. There are plenty of opportunities in these technologies that offer high salary packages & numerous opportunities for a rapid career development. However, beginners in Data Science shouldn’t get simply blinded by the shiny object of magical algorithms rather they should also be focusing on the necessary infrastructure that helps them in handling data in the first place.
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At present, we can find a number of companies that provide AI services. Before availing these services, one needs to evaluate the following aspects.
- Do they offer expertise in AI
- Are they capable of generating data
- Do they offer access to data
- Are the capable of building the infrastructure for managing the data
We would like to make it clear that having expertise in analyses and algorithms wouldn’t be sufficient as addressing the data would be ruled out from this equation & this isn’t the ideal approach.
Build With Purpose
Both data management and infrastructures are the complex tasks that need to be built with purpose. This process should be dealt with aspects like mining the accurate data, preparing the data & identifying the relevant instruments from the data retrieval. Whatever may be the format of the data, it must be collected as an asset. Data is undoubtedly a major asset as it tends to be having an intrinsic value which goes beyond the original purpose why it collected in the initial stage.
- Invest In Data
Data investment is very crucial for the success in the overall process. Aspects related to capturing, storing and retrieval of data serves the purpose only if the data is relevant for the objective it is being used. Without the right data, it becomes close to impossible in achieving the serried objective or the sole purpose for which this process is actually being carried out. Investing on the right data is surely a critical path that could possibly make a successful use of AI algorithms.
So, to achieve success in the Data Science process, a combination of dedicated infrastructures, more & better data, and the best algorithms are all equally essential.