Future of Data Science: Predictions and Trends for 2025

Years back the size of computers was as large as a room, and data was few. Now in the current age the size of the computer is getting smaller, and compact and the data is exceeding. Previously, data in our society was not that large, it can be calculated, analyzed by two or three people. But now we have Bigdata, technology had given software, algorithms and manpower as well and yet data is remaining unstructured and unanalyzed. In today’s time DataLakes, Data Warehouses are introduced to store the data.  

Have you thought that, autocorrect suggestion that we get in our keyboard while chatting, e-mails can recognize that which is useful to us or put it on spam? It’s all Data Science. The person who gets the knowledge to analyze unstructured and raw data to make meaningful is known as Data Scientist.

What is Data Science?

Data science is the connection which needs various techniques, processes, algorithms, and knowledge to interpret and analyze the data. Data science provides automatic insights and meaningful results of raw data. Those insights are used to solve business problems. Data science is a vast term which includes beneath it is Artificial Intelligence, Machine Learning and Deep Learning. It is predicted that by 2025 in the future in data science will reach sixteen million.

Evolution of Data Science in Digital age

If we talk about data science, it is not introduced in today’s time. The term data science was introduced in the early 60s, but at that time people used to work manually. Till the year 1960 and 70. It started evolving through statistical software started developing. Before the 1990s it was known as a statistical term of interpreting. After 1990 it started knowing as data science. It started taking shapes for different fields and algorithms also introduced. Now if we see this digital fire is increasing exponentially. It is not possible to imagine the world without data in today’s time. 

Component of Data Science

There is various component of data science let us see gist about these components:

Data

Data is prominent in data science because data is everything. We all know data is available everywhere, especially after the introduction of social media. You need to gather a lot of data for more accurate results.

Statistics

Statistics came from mathematics, but it is very essential in data science. It is used to collect, analyze and interpret the data to give meaningful insights. Both probability and stats are important for data science prediction. Probability predicts the chances of the occurrence of an event in numeric form.

Programming language

The programming language which is generally used in Data science analysis is Python and R. Python helps to give thorough outlook and R is for statistical analysis.

Data Visualization

Data Visualization gives insights into the form of graphs, pie charts, maps and others. It gives confidence to non-technical people to access it better. Data visualization makes it easy to understand the massive data in visual form.

Machine learning

Machine learning makes work easy for humans. Many algorithms are applying to solve the problems. It made the system automatic by training the machines and it carries out the task. Data science is fully dependent on machine learning. It is easy to make predictions through ML. 

Domain Knowledge 

To know the insights of Instagram post you need the knowledge of interpreting and understanding that data. From getting the problem to solving it, this needs expertise in Data science. Data scientists have deep knowledge of domains or subjects. Every domain need expertise but if we talk about Data science it is mandate. 

Future of Data Science

After seeing the current boom in data science, it seems like the future is promising and vast promising. More growth opportunities are unlocked in the field of data science. Data science benefits technical fields only, it is widely used to collect data for insurance, healthcare, pharmaceutical, manufacturing etc.

Trends to watch in Data Science 

AI is growing fast

Automatic Intelligence is increasing expeditiously, and it is impacting many industries. AI can solve business problems that can help to grow faster than before.  It is notable that AI will be used more for growth purposes in upcoming years. Even Google found that 64% of developers feel a “sense of urgency” to use generative AI.

Hiring data analyst 

As we have seen, the growing competition among every sector whether it is insurance, manufacturing, or financial services. They all want to grow and for more improvement they are taking help of AI and BI although it is all coming under data science. So, in upcoming years it is predicted that there will be more hiring of data scientist to solve, analyze and interpret the problems of organizations.  

Predictive analysis  

Predictive analysis is prediction and forecasting of future with the help of previous data and statistical tools. Through this forecast we can analyze the upcoming trends of industry and perform in our business to grow. It is all to make insightful decisions for your organization.  

Eruption in deep fake photo and video 

AI is giving mesmerizing miracles like it is giving deep fake photos, videos, and audios, as well. It is a great innovation to copy exactly like real product. But nowadays, people are misusing that innovation, leaking privacy, and trustworthiness of data, which is harming people personally. 

Use of NLP is increasing 

The expansive advancement of Natural language programming (NLP) is increasing human-machine interactions. By introducing chatbots, voice assistance and more. This opens doors to profound understanding, creating more intuitive and conversational experience which is fueling the next generation of intelligence system.

Challenges of Data Science

Due to innovations and improvements in data science on a regular basis, it still has challenges. Let us discuss them accordingly: 

Lack of professionals 

Data science is growing day by day and getting tougher as well. Although we have data scientists, they need to be more accurate if we talk about data science. It is essential to have deeper knowledge of this domain. We find there is a lack of professionals in data science. 

Lack of domain knowledge 

It is true that if a person can’t be an expert overnight, it needs lots of experience, but in data science it is necessary to have domain knowledge. It will be tough for newbies to survive in that domain. A fresher may have all the knowledge to deal with statistical tools but may face difficulty in getting accurate results. 

Data availability of quality data 

One of the magnificent challenges is to identify quality data. Inferior quality like missing numbers in data, inaccurate data can give incorrect results, and this might affect the business also. So, it is vital to have the right data. 

Privacy and security of Data  

It is crucial to maintain the privacy and security of data currently, where hackers are getting more advanced. By following GDPR and CCPA rules, we can ensure data security and privacy. 

Quick adapting of environment 

Though we all know data science will all time evolving field. Scientists must learn the upcoming trends and adapt to them. It might be challenging to all of them to grasp quickly.

The Role of Data Scientists in the Upcoming Years

The role of data scientists in upcoming years is increasing fast because opportunities in this area are increasing. Let us jump into it to see the roles: 

Gathering of data

Data scientists collect data from various sources such as online sources, databases can extract the relevant data and information from gained data.

Cleaning of data 

This step is particularly important for accurate results. Before the process of analyzing, scientists’ clean data to remove errors and anomalies. They can work more easily with structured data. 

Technical knowledge 

Data scientists in the coming years is crucial in technical knowledge like machine learning and algorithms. They forecast the trends based on past data and work on the algorithms to predict the results. 

Visualization tools 

Visualization can help those who are not familiar with technical terms. Visualization tools like DOMO, Tableau, PowerBI help scientists to provide accurate results. 

Continuous learning 

This field is continuously evolving and expanding. Scientists must learn every now and then and should be updated from the trends and emerging technologies. 

How Beyond Key helps

In the end, Data science have massive scope of growth, and you can ultimately use it when you have relevant knowledge and information about it. At Beyond Key, our experts can guide you with coming trends and forecasting. They dive deep into the challenges that you are facing and deliver accurate and powerful insights. 

Are you ready to innovate your business with data science? Contact us today to find out more about data science and its use case.