Top 5 Latest Trends in Data Science to Dominate in 2021
Latest Trends in Data Science
The data science industry is quickly evolving.
Understanding what’s beyond these technologies will help people stay in sync with the current and updated trends in the industry.
Here are a few glimpses into the upcoming trends we need to watch out for in 2021:
1.Automation in data science
Data science has been a great supporter of automation even though the field itself does multiple manual works – data cleaning, data acquisition, model training, result prediction, result interpretation, and feature engineering.
Performing all these tasks consumes a lot of time and requires extensive knowledge. But with the new concept called Automated Machine Learning (AutoML), all these tasks can be easily automated.
AutoML is designed in such a way wherein it can automate the entire process with the help of applying machine learning and artificial intelligence (AI) within its practice.
The process covers the complete data science pipeline from the raw data making it to deployable models. Now this includes feature selection, feature engineering, hyperparameter optimization, and data preprocessing.
AutoML, though in its initial stage is heralded to offer multiple benefits in the foreseeable future. In the present day, complete automation might not be possible, but it can still make certain tasks for a data science professional easier. Automated machine learning allows the data scientist team to try used cases in a much lesser time, therefore, finding AutoML effective is most of the cases.
The best part about using automated machine learning is the fact that it can test many different scenarios that even the data scientist team might not have imagined.
Not to mention, Google is one of the biggest investors in Cloud AutoML for NLP, tabular data, and computer vision.
Start automating data provisions to help build models easier and faster.
2.Answering the “whys”
Data science has made tremendous developments in the past years, yet the machine learning and AI model is unable to grasp the cause and effect, factors that are crucial in making machines intelligent.
Machine learning model can identify patterns in data which most humans cannot typically identify which is critical for making predictions. Precisely, this is when the question “why” needs to be answered in order to make data-driven decisions.
If you look at it from the business perspective, having the answer to any type of causal question is said to be the key to making decisions better. For instance, the yield of the farmers can be easily predicted using the AI and ML models, however, for the farmers to act they will still need to know the causes that are helping their crops to grow.
Besides this, you will find multiple causal questions that require answers. Why are firms leaving their organizations or what are the causes that force a customer to buy or not to buy a product?
Start identifying business questions that would require a causal answer, doing so will help you to correctly apply causal ML and AI models in your organization.
3.The data science democratization
The advent of cloud-based services has made it even easier for people to have access to the machine learning models.
Data as a Service (DaaS) is used by many individuals thus enabling the company to store, process, and integrate their data at a faster speed with low cost. Besides this, Machine Learning as a service (MLaaS) is a new concept that allows using machine learning tools as part of the cloud computing services. Such tools involve data visualization, natural language processing, and deep learning that aids predictive analytics. Organizations having the preference of MLaaS can now leverage AI and machine learning without needing to worry about establishing the data science team.
A career in data science is promising as we keep experiencing newer concepts in the industry.
You need to start establishing an AI and ML support division to help employees with the queries related to AI and machine learning.
4.Ethics of artificial intelligence (AI)
Though AI offers some of the greatest potentials, it still raises a certain level of ethical issues.
Most of the issues are related to whether AI can cause any harm to a human or a morally relevant being. Well, in broader terms, the ethical issue can be divided into three categories i.e. unethical use, unethical AI, and societal issue.
Unethical AI is when the system is not functioning properly or demonstrates a biased system which further leads to making unethical decisions. Most of the time, the problem arises because AI is only capable of acting as per the data that has been given by us. Therefore, if the training data has a gender bias, the model will eventually learn the same. Typically, even if the AI is unbiased, it can still create an ethical problem.
To combat this issue, you must establish an internal AI ethics council. Such a council will help regulate and monitor the AI within the organization.
5.Specialization of different data science roles
Job roles and skills are expected to change and evolve in the next decade. As a data science professional, it is critical to bring in the latest trend (AI and machine learning models) into production.
Increasingly, most of the engineering skills such as cloud architecture, database design, and software development will become vital in the foreseeable future.
As a result, job roles such as machine learning and AI engineer, and data engineer will precisely go hand-in-hand. While the data engineers will be responsible to develop, test, and maintain architectures and data pipelines while AI and machine learning engineer’s responsibility will involve the implementation and operation of machine learning models into production.
For this, you will need to conduct training programs to educate more data science talents.
An increased interest in the data science field will significantly grow in the years to come. Automation plays a major role in introducing machine learning and AI models to a broader audience making AI and machine learning the key drivers to accelerate growth in data science.