These are the 7 biggest trends in data science for 2021.
We’ll also explain the way these trends will impact both data scientists’ work and day-to-day life.
Whether you’re an active member of the data science community, or simply concerned about your data privacy, these are the top trends to follow.
1. increased use of deepfake media (audio and video)
“Deep fake” searches – interest often spikes when public figures are deep faked and the media gets hold of it.
Deepfakes use artificial intelligence to manipulate or create content to represent someone else.
Often this is an image or video of one person modified to someone else’s likeness.
But it can be audio too.
Back in 2019, an AI company deepfaked popular podcaster Joe Rogan’s voice so effectively it instantly went viral on social media.
And the tech has only improved since.
Open source software makes deepfake technology relatively accessible.
There’s huge scope for this technology to be used maliciously.
Another voice deep fake was used to scam a UK-based energy company out of €220,000.
Deepfake tech is already being used to facilitate scams.
The CEO believed he was on the phone with a colleague and was told to urgently transfer the money to the bank account of a Hungarian supplier.
In fact, the call had been spoofed with deep fake technology to mimic the man’s voice and “melody”.
In fact, there’s growing search interest in a practice known as “voice phishing”. Which is essentially the “official” term for the practice.
Searches for “voice phishing” are up 3x over the last 5 years.
As well as hoaxes and financial fraud, deepfakes can also be weaponized to discredit business figures and politicians.
Governments are starting to protect against thiswith legislation and social media regulation.
And with technology that can identify deepfake videos.
There’s a growing niche of tech startups focused on identifying deepfake video content.
But the battle with deepfakes has only just begun.
2. more applications created with python
“Python” searches – Python is on track to become the most popular programming language in the next 5 years.
Python is the go-to programming language for data analysis.
Why is this?
It can even be used to develop blockchain applications.
Add to this a friendly learning curve for beginners, and you have a recipe for success.
Python now has the highest number of Stack Overflow questions per month.
Python is now ranked as the 3rd most popular language in general by the analyst firm RedMonk.
And the popularity growth trend shows it’s on track to become number 1 in the next 5 years.
3. increased demand for end-to-end ai solutions
“Dataiku” searches – this company was growing quickly even before Google acquired them.
The AI startup helps enterprise customers to clean their large data sets and build machine learning models.
This way, companies like General Electric and Unilever can gain valuable, deep learning insights from their massive amounts of data.
And automate important data management tasks.
Previously, businesses would have to seek expertise in all the different parts of the process and piece it together themselves.
Dataiku champions “Collaborative Data Science” between all parts of the organization.
But Dataiku handles the entire data science cycle from start to finish with a single product.
And because of this, they stand out.
Businesses want end-to-end data science solutions. And startups that provide this will eat the market.
4. companies hiring more data analysts
“Data analyst” searches – interest in this data science role displays hockey stick growth.
Demand for data analysts has shot through the roof over the last 5 years.
Data analysts are in increasing demand.
And, thanks largely to data coming in from the Internet of Things (IoT) and advances in cloud computing, global data storage is set to grow from 45 zettabytes to 175 zettabytes by 2025.
So the need for experts to parse and analyze all of this data is set to rise.
Why are so many data analysts required?
After all, there are plenty of data analytics programs out there that can sort through it all.
And “digital transformation” has supposedly replaced many human-led business tasks.
Sure, machines can help analyze data.
But big data is often extremely messy and lacking in proper structure.
Which is why humans are needed to manually tidy training data before it’s ingested by machine learning algorithms.
It’s also increasingly common for data people to be involved on the output end too.
AI-produced results are not always reliable or accurate, so machine learning companies often use humans to clean up the final data.
And write up an analysis of what they find in a way that non-tech stakeholders can understand it.
Amazon’s Mechanical Turk is the biggest platform where “Turkers” complete data labeling and cleaning jobs.
The data science and machine learning methods of the 2020s will be less artificial and automated than initially expected.
Augmented intelligence and human-in-the-loop artificial intelligence will likely become a big trend in data science.
5. data scientists joining kaggle
“Kaggle” searches – this data science platform has over 5 million users across 194 countries.
Kaggle has grown quickly to become the world’s largest data science community.
Many budding data scientists now start with Kaggle to begin their machine learning journey.
And post the progress of their machine learning projects in real-time.
Users can even share data sets and enter competitions to solve data science challenges with neural networks.
Or work with other data scientists to build models in Kaggle’s web-based data science workbench.
Kaggle competitions can have hefty prize sums.
Academic papers have actually been published based on Kaggle competition findings too.
Successful projects from Kaggle’s hundreds of competitions will likely continue to push boundaries in the field of data science.
6. increased interest in consumer data protection
“Data privacy” searches – people are searching about their data privacy in greater numbers by the month.
Consumer awareness about data privacy rose in the wake of the Cambridge Analytica scandal.
In fact, Statista states that more than half of all consumers became more interested in data privacy in the year following the revelations.
Platforms like Facebook and Google, which previously harvested and shared user data freely, have since faced legal backlash and public scrutiny.
Facebook now has a large guide on privacy basics and what it does with your data.
This broader data privacy trend means that large data sets will soon be walled off and harder to come by.
Businesses and data scientists will need to navigate legislation such as the California Consumer Privacy Act which came into effect at the start of 2020.
And this could become a bane for data science when it comes to the future acquisition and use of consumer data.
7. ai devs combating adversarial machine learning
“Adversarial machine learning” searches – data scientists now seek ways to combat this practice.
Adversarial machine learning is where an attacker inputs data into a machine learning model with the aim to cause mistakes.
Essentially an optical illusion designed for a machine.
Adversarial Fashion’s clothing lines trick machine learning models with bold patterns and lettering.
Anti-surveillance clothing takes this approach to the masses.
According to a Northeastern University study, this clothing can help prevent individuals’ automated tracking via surveillance cameras.
Data scientists will need to defend against adversarial inputs like this. And provide trick examples to models to train on so as not to be fooled.
Adversarial training measures for models like this will become essential in the next decade.
Those are the 7 biggest data science trends over the next 4-5 years.
Data science, like any science, is changing by the day. From data governance to deepfake technology, the data science industry is set for some major shakeups.
Hopefully keeping tabs on these trends will help you stay one step ahead.