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Top Data Science Trends To Follow In 2021

 

Data Science! 2020 came as a surprise or more as a shock. It was a crazy year no one could have expected what might happen. If we examined hot topics about this time last year, we focused it off on the normal patterns and changes that we might have predicted. With COVID-19 and the new standard, AI development, and data science were also impacted.

A greater focus has been placed on remote communication, and healthcare experts have turned to AI to assist with diagnosis. When we look to 2021, Artificial Intelligence and data science experts have differing views about what to consider for 2021. In this post, we have discussed the coming data science 2021 trends to keep a close eye out at. 

2021 Data Science Trends

Here are all the quickest data science trends that we are going to see in 2021. We have also discussed how these patterns will affect both data scientists’ jobs and daily life. If you are actively interested in the data science community, or only worried regarding your data security, below are all the top trends to watch. Check out data science tips and tricks here.

Deep fake video and audio

We will see more of deep fakes in 2021 than ever. Deep fake is the manipulation or the creation of the content using artificial intelligence to embody a fake person or someone else who is real. Usually this is a picture or clip of one person changed to somebody else’s depiction. Sometimes it can be audio as well.

In 2019, an Artificial Intelligence company deep-faked famous podcaster Joe Rogan’s accent so efficiently it immediately viral on the internet. Deep fake tech has continued to improve since. Open-source code makes deep fake techniques fairly available. There is a huge scope for this tech to be used vindictively. Along with fake news stories and bank crimes, deep fakes could also be militarized to delegitimize corporate figures as well as politicians.

Governments are now taking measures to protect against deep fake with laws and social media legislation using the technology which can identify deep fake stories. So, we can assume the fight with deep fakes has just officially started.

Increase in Python’s Demand


In the coming 5 years, Python is on pace to be the most widely used programming language. For data processing, Python is among the top programming languages. Oh, why is that? Since Python has a large number of free resources for data science, including Scikit-learn and Pandas libraries for artificial intelligence.

It can also be used for the creation of blockchain apps. Apply a gentle learning process for learners to this, and you will have a formula for achievement. The research firm RedMonk now rates Python as the third most influential programming language in general. Also, the trend of rise in popularity indicates that it is on track in becoming number one in the next five years.

End-to-end AI solutions

Since Google acquired a shareholding in the firm in December 2019, Enterprise Artificial Intelligence company Dataiku has become worth $1.4 Billion. They help business clients clean up their massive data sets and create models for artificial intelligence. Businesses such as Unilever and General Electric will gain useful, deep learning perspectives from their vast quantities of knowledge in this way.

And optimize major tasks for data processing. Originally, in all the various sections of the process, companies will have to pursue experience and piece it all together themselves. Although with a single product, Dataiku manages the whole data science process from beginning to end.

Today, organizations demand end-to-end data science technologies. And new companies that offer such technology will rule the market.

Kaggle’s popularity among data scientists

Kaggle has evolved steadily to be the biggest data science group on the planet. And it is not losing momentum, with more than 5M users spanning 194 countries. Most aspiring data scientists are now beginning to start their deep learning experience with Kaggle. And report in real-time the performance of their deep learning projects.

To overcome data science problems with neural networks, users can also exchange data sets and join contests. Or collaborate with the other data scientists in the internet-based data science workbench of Kaggle to create models. In reality, scientific publications have also been currently evaluating Kaggle competition outcomes.

Effective ventures from Kaggle’s hundreds of contests are likely to persist to move data science standards.

Privacy of Consumer Data 

In the aftermath of the Cambridge Analytica fiasco, user consciousness of data privacy grew. In fact, Statista reports that in the next year after the disclosures, more than 50% of the consumers became more involved in data protection. Social media platforms Like Facebook as well as Search Engines like Google, which openly accessed and exchanged user data earlier, have encountered both legal challenges and public criticism since then.

This wider trend in data protection means that vast data sets are soon going to be fortified off and tough to achieve. California Consumer Protection Act, which came into force at the beginning of 2020, would need to be navigated by companies and data scientists. And when it applies to the potential acquisition and use of customer data, this could be a threat to data science.

Conclusion

This concludes our list of the 5 top Data Science trends that we will see in action in 2021 and the coming years. If you are a Data Scientist or someone interested to launch their career in Data Science should be well informed about these trends and their usage.

Besides these trends, to become a successful data scientist, you should also get your hands on the popular data science training courses online, join boot camps, acquire certifications, and get familiar with the top data science tools. With the right skills in tools and software and knowledge of trends and predictions, you can easily become the best in the field.

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