Top Four Data Science Trends, 2021

The Data Science space continues to evolve in quite interesting ways. Some say, Data Scientists might even evolve themselves out of jobs. Here are our top four Data Science trends for 2021.

Creating Applications with Python: Suddenly feel like we mention python in every single blog post? That’s because Python has become an integral part of Data Science. Python has a number of libraries and a large community that keeps adding up to the vast amount of resources on python. Python is one of the most popular programming languages, ranked 3rd by the Analytics firm Redmonk. If you’re starting out your Data Science journey, make sure you hop on the python train.

Deep Fake Technologies: Nope, it’s not just used for actors and celebrities. It gets bigger than that. Over the past years, Deep fake technology has improved and is lighting a huge red blinker for cybersecurity. If you’re wondering what Deep Fake technology is all about, deep fakes are so-named because they use deep learning technology, a branch of machine learning that applies neural net simulation to massive data sets, to create a fake. For example in movies, AI will effectively learn a source’s face and be able to reproduce it onto a target like a mask. It’s not just restricted to videos, but machines can learn and mimic a person’s natural voice tones. In 2019, there was a high profile case where criminals used Deep Fake technology to impersonate the CEO of German conglomerate and stole almost $250,000.Technology can be an enabler, but it gets as positive as it does negative. Team Gleason, a non-profit organization that provides technology, equipment and services to patients living with Amyotrophic Lateral Sclerosis(ALS) worked with technology companies to develop Deep Fake scenarios to enable them speak to loved ones even after losing their ability to talk.

The rise in demand for end to end solutions: Previously, most companies had to employ Data experts to handle all the different parts that constitute the Data Science process which presents a huge cost to businesses. Which explains why Dataiku is doing so well and is worth 1.4billion dollars. Dataiku handles the Data Science process from start to finish, making it easier and quite cheaper for organisations to become data-driven. There is no doubt that it’s such a smart idea; we can be sure to see more end-to-end solutions like Dataiku from start-ups.

Demand for Data Scientists and Analysts: You must be thinking, “why the need when there are a number of applications that can take care of everything? ”You can’t have a Machine work on raw unstructured Data all on it’s own. Sure it can help analyze the Data but it needs to be trained in order to understand what it’s even supposed to reproduce as results. Humans are needed to tidy up the Data before the machine is trained to recognize patterns and anomalies.

The Data Science space gets more interesting everyday, and we are definitely going to see more cool stuff popping up in the years to come.

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