Latest Trends in Data Science

The latest trends that have gathered growing interest this year and will continue to grow in 2020 are as follows:

  • Automated Data Science:

Till today, Data Science requires a lot of manual work such as cleaning data, storing data, visualizing and exploring data, and modelling data to get actual results. The manual work is pleading for automation and thus automated Data Science and Machine Learning is been rise. Auto-Data cleaning is researched over the past years. Cleaning big data takes up most of a data scientist’s time. Including both startups and MNC companies such as IBM offer automation and tooling for data cleaning.

  • Data Privacy and Security:

Privacy and security are sensitive topics in technology. Major companies are in need to move fast and innovate, but lose the trust of customers over privacy or security issues which be further can be fatal. Thus, mandatory to make it a priority, at least to a minimum of not seeping private data. Data Science has become advance, in terms of the transformation of privacy and security protocols. That includes laws, processes, and different methods of starting and maintaining the security, safety, and integrity of data.

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  • Super-sized Data Science in the Cloud:

Data Science has evolved from a niche to its own full-on field, the data available for analysis has also shattered in size. Most of the companies are storing and collecting more data than before. Cloud computing provides the ability to access practically limitless processing power from anywhere. Cloud vendors such as Amazon Web Services (AWS) provides servers with up 96 virtual CPU cores and 768 GB of RAM. These servers set up in an auto-scaling group where most of them can be launched or stopped without delay — computing power on demand. Data Science is being done purely on the cloud owing to the sheer volume of the data

  • Natural Language Processing:

Natural Language Processing (NLP) is a technique used in data science after huge breakthroughs in deep learning research. Advancements in NLP through deep learning are driving the full-on integration of NLP into regular data analysis. Neural Networks (NN) can now eliminate information from large bodies of text incredibly rapidly. They’re able to classify text into various categories, regulate sentiment about a text, and complete analysis on the similarity of text data. In the end, all of that info can be deposited in a single feature vector of numbers.