The technological advancement witnessed in almost every field is leading to the growth of new methods and techniques that make work easy and quick. One such development is the introduction of data science in different sectors, especially in finance.
What is data science?
Data science unites statistics, data analysis, informatics, and their related methodologies. It uses theories and methods from a wide range of disciplines, including those connected to computer science, information science, statistics, mathematics and domain knowledge.
However, data science is distinct from computer science and information science because it primarily focuses on unstructured or raw data. The life cycle of data science, which includes several roles, tools, and procedures, can provide analysts with useful insights.
Who is a data scientist?
Data scientists are professionals who use sophisticated techniques and tools to analyse a large amount of data to find hidden patterns, gather important knowledge, and provide information for making business decisions. A data scientist's work combines computer science, statistics and mathematics.
Understanding the foundations of these fields is one of the data scientists' requirements before analysing the results to create realistic plans for corporations and other groups.
What is data science course eligibility?
The World Economic Forum projects that by 2022, the data scientist profession will have had the greatest global growth.
Anyone can learn data science, as eligibility for data science course is easy to fulfil. The bare minimum prerequisites for traditional data science courses are the knowledge of high school topics. Engineering, marketing, software and IT professionals can also enrol in part-time or external data science programmes.
A Bachelor's Degree in Science or Engineering and fundamental knowledge of statistics and mathematics are prerequisites for these programmes. To enrol in an undergraduate data science course, students must receive more than 50% in mathematics, statistics and computer science in their Class 12 examinations.
Another data science course eligibility for individuals wanting to pursue a PhD in the field of data science is a minimum of 55% mark in the postgraduate programme. Students with higher GPAs are more likely to be given preference.
What are the uses of data science in different fields?
The use and importance of data science in different fields is as follows:
There have been technological developments in the healthcare industry as well, thanks to data science. Medical professionals are learning new approaches to diagnose the disease, practise preventative medicine and investigate better treatment options as a result of the vast data network that is already accessible through any means, from electronic medical records to clinical databases to personal activity monitors.
The application of data science in data security is crucial in the healthcare sector as patients' data are sensitive. It is widely acknowledged that the security provided by data science is significantly impacting the healthcare sector, helping it reach new heights and discover better practices.
Data science is becoming increasingly visible on the road. Predictive analytics, a data science application, has been employed by Tesla, Ford and Volkswagen in their autonomous vehicles.
A large number of tiny cameras and sensors are used by these autos to exchange real-time information. Self-driving cars can adjust to speed limits and prevent risky lane changes using machine learning, predictive analytics and data science, even when carrying passengers.
Have you ever questioned how Spotify manages to suggest the ideal song for your present mood? Alternatively, how does Netflix figure out what TV episodes you'll binge-watch?
These enormous media streaming corporations analyse customer preferences using data science and then carefully select the most pertinent content from their enormous libraries.
The banking sector has been able to save millions of dollars and countless hours of work as a result of machine learning and data analytics. For example, the JP Morgan Contract Intelligence platform processes and extracts crucial data from a myriad of commercial credit agreements annually using natural language processing.
The ability to quickly do jobs that once needed several thousands of hours of manual effort is entirely due to data science. Fintech firms like Stripe and Paypal also make investments in data science to develop machine learning capabilities that swiftly detect and prevent fraudulent activities.
Cybersecurity is a field where data science plays the most crucial role. Using data science and machine learning, international cybersecurity organisations discover thousands of new malware samples every day. In the future, our safety will significantly depend on the data scientist's ability to identify and assess new criminal tactics.
Product sales and marketing
Many firms rely on data scientists to create time series forecasting models that aid in inventory management and supply chain efficiency. Making proactive recommendations based on budget projections from financial models is another responsibility given to data scientists occasionally.
Some companies even segment their consumer bases based on their activities, adjusting their future marketing communications to appeal to particular groups based on their previous contacts with the company.
Airline route planning
The airline business is expanding because data science has made it easier to predict flight delays. It's also useful to decide whether to land at the destination right away or to make a stop somewhere in between.
The bottom line
Data science, artificial intelligence, and machine learning are all becoming increasingly important for businesses. Businesses, regardless of size or sector, need to build and implement data science capabilities as soon as possible if they want to remain competitive in this big data era. Otherwise, they have the risk of lagging behind.
Students must consider opting for Data Science & AI course after going through the data science requirements for a better understanding of data and operations related to it.