4. Future of Data Science

Future of Data Science

Data Science is a colossal pool of multiple data operations. These data operations also involve machine learning and statistics. Machine Learning algorithms are very much dependent on data. This data is fed to our model in the form of training set and test set which is eventually used for fine-tuning our model with various algorithmic parameters.

By all means, advancement in Machine Learning is the key contributor towards the future of data science.

In particular, Data Science also covers:

  • Data Integration.
  • Distributed Architecture.
  • Automating Machine learning.
  • Data Visualisation.
  • Dashboards and BI.
  • Data Engineering.
  • Deployment in production mode
  • Automated, data-driven decisions.

i. Data Science currently does not have a fixed definition due to its vast number of data operations. These data operations will only increase in the future. However, the definition of data science will become more specific and constrained as it will only incorporate essential areas that define the core data science.

ii. In the near future, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.

In India alone, there will be an acute shortage of data science professionals until 2020. The main reason for this shortage is India is because of the varied set of skills required for data science operations.

There are very few existing curricula that address the requirements of data scientists and train them. However, this is gradually changing with the introduction of Data Science degrees and bootcamps that can transform a professional from a quantitative background or a software background into a fully-fledged data scientist.

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