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Data Science Trends 2020

Data Science has played a vital role in the era of Digital Transformation. Since 2013 there is a major growth in the field of Data Science. With the help of Data Science organisations do not have to make guesses on the basis of small surveys. Data Science is all about Creating Values through Data. The trend of integrating data into the core business processes has grown significantly, with an increase in interest by over four times in the past 5 years.

It’s estimated that 1.7 MB of data will be created every second for every person on earth. With Exabytes of Data generated every day with the help of Big data Analytics organizations are now making informed business decisions.

Here are Top 3 Data Science Trends to look forward in 2020:

Data Cleaning Automation: As we know High Quality Data helps Data Scientists to provide Better Business Decisions to the organisations and they end up spending most of their time in Data Cleaning and Data Arrangements Organisations are now looking in Data Cleaning Automation so that Data scientists’ will spend more productive time and work towards marketing analytics, which can soon result in appropriately equipped and vetted data. Through 2022, data management manual tasks will be diminished by 45%. Currently there are 10 reliable automated Data Cleaning tools available.

Data Science in Cloud:

For companies in Banking or retailer domain who have data covering millions of customers it is not possible to create a Machine Learning Model in our Systems having something like 64 GB of RAM with an 8 core CPU and 4 TB of storage. Cloud vendors such as Amazon Web Services (AWS) offer servers with up 96 virtual CPU cores and up to 768 GB of RAM. These servers can be set up where hundreds of them can be launched or stopped without much delay — computing power on demand giving Data Scientists the ability to store and analyze petabytes of data, all in a single platform.Using BigQuery ML to Machine Learning pipelines can be build on huge datasets.

AutoML: It is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. It additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.

It includes the following steps:  Automated column type detection  Automated feature engineering  Automated model selection  Hyperparameter optimization of the learning algorithm and featurization  Automated selection of evaluation metrics / validation procedures  Automated analysis of results obtained  User interfaces and visualizations for automated machine learning.

Conclusion Data Science has become one of the growing fields in all industries, especially the IT industry. Thus, businesses adopting data science techniques and technologies must stay up-to-date with the latest trends. in order to achieve maximum growth.

Author: Prerna Nichani

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