What areas of Data Science or Machine Learning are growing in importance?

 What areas of Data Science or Machine Learning are growing in importance?

In terms of your business, Data Science or Machine Learning has amazing benefits. It is quite essential to understand that the solution to the issue is not the way to find one. It means that you need to figure out what you are trying to improve or change before hiring a team of data science or machine learning experts if your company has a lot of data that you do not quite know what to do with.

Today, we are going to share 6 rapidly-growing areas in which data science has shown significant growth!

Data-Science-and-Machine-Learning

1. Explosion in Deep Fake Video And Audio

In terms of manipulating or creating content for representing someone else as Deepfakes uses artificial intelligence. Often, it is a video or an image of a person who is modified to the likeness of someone else. But, it can also be audio.

2. More Applications Created With Python

For data analysis, Python for data science and machine learning data science is the go-to programming language. Why is it so?

Python is known for having a massive number of free data science libraries that include machine learning and Pandas libraries.

These are used for developing the applications of blockchain.

You can get a recipe for success as you add to this a friendly learning curve for beginners. The analyst firm RedMonk has ranked Python as the 3^rd most popular language.

And, within the next 5 years, the popularity growth trends show that it is on track to become the number 1.

3. Increased Demand for End-To-End AI Solutions

According to TechCrunch, Enterprise AI company Dataiku is now worth $4.6B since Google bought a stake in this company in December of 2019.

In terms of cleaning the larger data sets and building machine-learning artificial intelligence models, the AI startup can help the enterprise customers here.

Companies like General Electric and Unilever can gain some deeper and valuable insights through their massive amounts of data this way by automating essential data management tasks.

Businesses would have to look for expertise in every different region of this process and piece it together on their own previously.

Dataiku, therefore, can handle the complete cycle of the data science field from start to end with a single product.

4. Companies Hire More Data Analysts

There are several programs for data analytics out there that can sort out things.

Several human-led business tasks have been replaced through digital transformation. The data can now be analyzed with the help of a machine.

However, big data lacks proper structuring and is quite messy.

It is the reason why humans require to tidy train data manually prior to it being ingested by the algorithms of machine learning. On the output end, too, it has become quite common for data people.

Fields of Machine learning companies are often using humans for cleaning up the final data since the AI products are not always reliable or accurate.

To help the non-tech stakeholders understand it, a write-up analysis will help them in finding a way. Compared to what was expected initially, the data science and machine learning methods of the 2020s will be less automated and less artificial.

In data science, augmented intelligence and human-in-the-loop artificial intelligence will likely become a huge trend.

5. Increased Interest in Consumer Data Protection

It was in the wake of the Cambridge Analytica scandal, consumer awareness about data privacy rose.

Platforms like Facebook and Google have since faced legal backlash along with public scrutiny, having previously harvested and shared the user data freely.

The larger data sets will soon be walled off and quite tough to come by through this broader data privacy trend.

The legislation needs to be navigated by the data scientists as well as the businesses.

It can, however, become a bane for data science arriving at the future acquisition along with the use of consumer data.

6. AI Devs Combating Adversarial Machine Learning

An attacker can input the data into a machine learning model with the aim of causing mistakes with the help of adversarial machine learning.

An optical illusion is designed for a machine mainly. This approach is taken to the masses with the help of anti-surveillance clothing.

They are designed mainly for confusing the algorithms of face detection with bolder shapes and patterns.

It is this clothing that can aid in preventing the individual's automated tracking through the surveillance cameras, according to the study of Northeastern University.

Like this, the data scientist will have to defend against the adversarial inputs. This will also offer the trick instances for the models to train on to not be fooled any longer.

In the next decade, the adversarial training measures for the models like this will be important.

Wrapping Up

For the next 4-5 years, these 6 areas would see a significant improvement.

Transformations are now being seen by data science each day. The data science industry is set for a few major shake-ups from governance to deep fake technology.

You will now be able to stay a step ahead when you have a proper understanding of this platform.

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