If there’s a fuel to the tech industry which is driving it, then it is the data we collect each day, every day. There’s is no doubt about the importance of data that is collected in organizations day after day. From digital transformation to growth verticals, data is the supporting factor of almost every company procedure. Data science developers can help you extract value relevant to any workflow through this collected information. So, it is not hard to gauge the significance of data.
For this reason, there are multiple data science trends that we are or will observe in the year 2020. In this article, we will discuss the top 4 of these trends that you may have already started noticing.
Learning Of Blog
- Some Data Science Trends We Will Observe in 2020
o Python Will Lead
o NLP Will Also Follow
o Cloud-Based Execution
o Data Privacy
If you are impressed by the way data science developers are surfacing and improving the businesses’ growth markers, read on to understand some data science trends.
Some Data Science Trends We Will Observe in 2020
Have you ever utilized data to create reports on the performance of your team? Or have you ever created a forecast of sales based on past verticals?
We have all utilized data science in some of the other manner at some point in the business. This utilization is about to increase in the coming years. Let’s see how:
1. Python Will Lead
In the past few years, we have seen multiple programming languages emerge from nowhere and become important in the market. But, we have also seen the increasing usage of Python programming language through the years. Don’t you think? You can understand the popularity of python programming language with the help of the statement released by Stack Overflow’s survey
Python, the fastest-growing major programming language, has risen in the ranks of programming languages in our survey yet again, edging out Java this year and standing as the second most loved language (behind Rust).
While an array of supporters are still on the side of R, the python programming language is leading. Most of the data science developers utilize python for processing datasets, and this trend is not going anywhere for a long time. We will only see it increase this year. The reason behind this popularity is its simple syntax, which even non-programming background professionals can learn.
2. NLP Will Also Follow
Other than python, natural language processing usage in data science will also increase. This is because NLP can bring a value proposition to the business by allowing direct questions in normal language for enhanced insights. We have always adored conversational analytics and NLP usage for analytics. This is because even non-programming or data science professionals are able to gain insights with NLP. In 2020, we will see no change in the importance of NLP. It will boost business intelligence and analytics adoptions amongst business users and front-office employees.
The driver of this trend will be the NLP’s ability to integrate into routine analytics making working easier for every business user. This empowers users to analyze and interpret valuable information through NLP.
3. Cloud-Based Execution
We are sure that you saw that one coming. Today, considering the COVID-19 conditions, the cloud is everywhere. Most of the organizations have started using advanced cloud applications and implementations to collaborate with the team. In a situation like this, how can on-demand computing not thrive? Although its progress seemed natural till the last year and we were slowly proceeding towards cloud implementations, now the requirement has accelerated. Most of the data science developers are in their homes working remotely. They need cloud-based computing power to access data and resources on the go.
If we consider the growth of data science and cloud alongside, it is highly likely that we will see the entire data science structure move to the cloud in the next few years only. This will help in managing a large volume of data and improving computing capabilities.
4. Data Privacy
People are more cautious about their data today. Hence, even with the large amount of data that is being collected, data science developers need to ensure the privacy of this data. This is because in between aggressive marketing trick, data security can’t be overlooked.
In fact, multiple security and privacy regulations make it almost impossible to overlook data security. For example, GDPR. Looking at this will see companies use a stronger architecture for maintaining and properly utilizing the data of users to avoid theft and threat of any manner.
Data science developers have started moving in a direction where data science is utilized daily for valuable insights in a secure manner. With that, the industry is also changing and new trends are evolving. To keep pace, understand the above data science trends and start implementing these changes in your company.