Summary
To understand the market and predict the future of the enterprise, Data Science is playing a crucial role. These roles are played by the Data Scientists. The technology is not the same as it was a few years back. It is developing itself every day. With its development, data is becoming more important for businesses. The duties of a Data Scientist are to develop strategies for analyzing the data, prepare the data for analysis, visualize data, and many more.
Introduction
Data Science is a combination of statistics, artificial intelligence, scientific methods, and data analysis. It is a process to analyze the data that has been gathered from various sources like smartphones, web, customers, etc. Further, it is a process of extracting insights from the raw data by using scientific methods and processes. It spread out of the fields of data mining and statistical analysis. After seeing the rise in demand for a Data scientists more people are now enrolling themselves in Data Science courses. Acquiring Data Science Training can open doors for you among the leading companies in the world. Data Scientists use the programming language to understand the data such as R and Python and deploy models into applications. A Data Scientist always works with a team. Along with the Data Scientists, the team also includes business analysts, data engineers, an IT architect, and an application developer.
Who Uses Data Science?
In most organizations, the projects of Data Science are typically overseen by three types of managers, these are: –
Business managers:
The business managers work with the Data Science team to define problems and develop strategies for analysis. These managers may be the head of a line of the business-like finance, marketing, or sales, and have the Data Science team reporting to them. Moreover, the business managers work closely with the IT managers and Data Science to make sure that the projects are delivered.
IT managers:
The senior IT managers oversee the architecture and infrastructure that supports the operations of Data Science. Further, they continually monitor the use of resources and operations to make sure that Data Science teams are operating securely and efficiently. They are also looking after creating and updating an environment for the teams of Data Science.
Data Science managers:
These managers supervise the team and their daily work. They are the team builders who balance team development with monitoring and project planning. The most important player in this whole process is the Data Scientist.
What are the Advantages of Data Science?
Data is an asset to any organization, but only if well processed. It has several advantages to every industry based on its work. Mentioning below some of its advantages:
Improved healthcare:
People have seen some extraordinary improvements in the field of health care after its introduction to this sector. After the arrival of machine learning, it is now easy to detect the early stages of tumors. Moreover, there are several specialists in the healthcare industry who are using it to help their patients. To fight against some serious diseases and death-causing diseases like cancer, doctors are also using data to find a cure.
High demand:
It is in high demand in the present world. Therefore, many people are interested to learn it for their future growth. Data Scientists are highly demanded in the market because of the large amount of data that is emerging every day. The prediction is to generate approximately 11.5 million jobs in this field by 2026. It also makes it a promising career in the future. With the rate at which data is emerging every day, Data Scientists will be marketable people in society. Every company needs one Data Scientist and this is not going to end.
User experience customization:
It includes the use of machine learning that benefits industries to develop better products. These products are totally based upon the customer experience. For instance, the recommendation system that the e-commerce websites use shows the exact products that the consumers like to see or buy. It is done on the basis of historical searches and purchases by the user.
Entertainment:
Do you ever wonder how Netflix knows what shows you would love to watch? Or how does Spotify know which song suits your mood? By using it, the music streaming app curates the list of songs you want to listen to. Netflix also uses it to recognize your needs for selected shows among the various collections.
Finance:
Along with machine learning, it has saved millions of dollars in the financing industry. It has also saved unquantifiable amounts of time. For instance, the platform of JP Morgan’s Contract Intelligence (COiN) uses Natural Language Processing (NLP) to process and extract essential data from about 12,000 commercial credit agreements per year. After using Data Science, they are now able to do it in a few hours. However, without it, it would have taken approximately 360,000 manual labor hours to complete. Further, fintech companies like PayPal and Stripe are heavily investing in Data Science to create machine learning tools. It will help them to quickly detect and prevent fraudulent activities.
What are the Jobs in Data Science?
As we have discussed earlier, its scope is increasing every day. The need for Data Scientists in companies across the world is also increasing. Companies now have a proper team of people related to Data Science to understand their data and predict the market for further benefits. Here are some job roles for people who want a career under the umbrella of Data Science:
- Data Engineer & Data Analyst
- Machine Learning Administrator
- Database Administrator
- Statistician & Data Architect
- Data and Analytics Manager
- Business Analyst & Data Scientist
Conclusion
Data Science has various uses in enhancing a business’s performance. It makes use of some of the programming languages to conduct the processes. In conclusion, it’s a highly demanding skill and has lots of opportunities in it. Understanding What is Data Science? can make you proficient enough to use different aspects of data visualization, computer science, statistics, data analytics, Python, and R programming in Data Science. It helps you to convert voluminous data into meaningful content. Apart from these, you will also get depth knowledge of AI, Big Data Hadoop, machine learning, etc. Learning and understanding it can make you a renowned Data Scientist and you can also claim huge salary lumps too while working with the top industries.
