Why is Data Science Always in Demand?
Simple answer - Data driven decision making!
Data science is used to power the biggest businesses in the world. Data science is used by each of them to develop algorithms that increase customer happiness and boost revenue.
For instance,
- Google — Ranking of webpages to ensure the top links have an answer to every question
- Amazon — Product recommendation based on consumer behavior
- Facebook — Targeted ads towards specific audiences
- Instagram - User stories based on the images liked by a person and targeted ads
And it's not just businesses, every person in this world having a smartphone or access to the internet generates data.
Rising Data Volumes
2.5 quintillion bytes (2.5 followed by 18 zeros) of data is created each day. Over the last two years alone 90% of the data in the world has been generated. This massive amount of data generated everyday, has to be evaluated, cleansed, processed, stored, and analyzed.
Businesses generate and are confronted with massive amounts of data every day in the digital age. Companies must now embrace a variety of data technologies and intelligent data management systems if they wish to extract significant information from the enormous and varied stockpile.
Factors responsible for Data Science Growing Demand
- The market for big data analytics is predicted to grow from $241 billion in 2021 to over $655 billion by 2029.
- Up to 97.2% of businesses are investing in AI and big data.
Keeping these facts in mind, below are some of important elements that have contributed to the development of data science.
Data Driven Activities
Every big company has to engage a data science professional who can gather, analyze, and interpret massive amounts of data in order to use it for commercial development. This is particularly true for the consumer market as the complete product strategy depends on the consumer’s data.
Outweighs the Competition
Although there are many highly qualified data scientists, the demand is greater than the supply. Compared to other common IT careers, it is still an emerging field. Data science has less competition as a result, increasing your chances of becoming well-known quickly. There is a mismatch between the demand and supply for data science professionals.
Flexible Learning
If you want to learn and advance your knowledge in this field, you don't really need face-to-face instructions. Regardless of when or where you wish to take them, there are numerous online data science courses with affordable pricing available. You can challenge yourself and redefine yourself by enrolling in a data science course.
Aeronautical Engineer to a BI Analyst @ Blend360 with 10 LPA!
Centre of Decision Making
Although data science requires a wide range of abilities and responsibilities, becoming a decision-maker gives you more opportunities to excel. Data science specialists develop a wide range of abilities from technology to statistical understanding. They are thus at the making of crucial decisions that will lead to better outcomes.
Low Entry Barrier
Due to the fact that this industry is more recent, there is a chance to face lesser competition. Professionals with various backgrounds, like natural science, arts, engineering etc, can pursue a career in data science. A problem-solving process is all that is needed to become a successful data science expert.
Nidhi Kulkarni, a dietician becomes a successful data science professional
Gaining Knowledge About Cutting-Edge Technology
A set of technical skills, including the use of cutting-edge technologies, are required to succeed as a data science specialist. A skilled data scientist is knowledgeable about analytics, the theoretical underpinnings of computer science, communication, and the display of data-driven insights. Data science, as a result, pushes people to learn about cutting-edge technologies like artificial intelligence, and machine learning, etc.
Data Science is Constantly Evolving
Data science is a very vast field of study that is constantly evolving. Individuals who are drawn to this stream can take advantage of different possibilities and follow their interests through specializations.
Key Areas to Specialize in Data Science
Supply is low for a Data Science Professional
As data science is still a relatively new subject even in 2023, there is a shortage of data science professionals. Few years ago, programming languages with minimal computational primitives made it impossible to master data science. As the years went by, computer power began to increase tremendously and with the help of the latest technologies, data science has become more conceivable.
It was difficult to forecast the exponential expansion and traditional education was not prepared to fulfill the needs of those who wished to learn this expanding field. There were very few programmes accessible for ambitious data science professionals, and those that did not fully equip students for the field.
Future Scope of Data Science
More than 150 zettabytes of big data will need to be analyzed by 2025. Additionally, it is expected that $12 billion would be spent on big data application database solutions and analytics by 2027.
Jobs in data science are predicted to increase by 30%. In 2023, there will be an anticipated 2,720,000 employment openings for data scientists. Every business/sector has understood that it needs data science professionals to play with data in order to maximize corporate profitability. Data Science Impact on various Indian sectors like retail, banking and healthcare etc.
Zettabytes (a billion Terabytes) of data are handled by organizations that manage these varied classifications. This data will continue to grow enormously over the future and as a result, there will be a greater need for knowledgeable data science professionals.
In addition to the position of data scientist, there are various other employment opportunities in this subject, including:
- Data Analyst: By analyzing data with data analysis technologies, data analysts collaborate with their teams to develop business plans and insights.
- Data Engineer: Data engineers, who offer a structured, uniform data flow, enable data-driven models, such as machine learning models and data analysis..
- Machine Learning Engineer: You will be tasked with developing the models and algorithms required for automated machine operation in your capacity as a machine learning engineer.
- Business Analyst: By applying data analysis, business analysts assist organisations in improving their processes, goods, services, and software.
- Data Architect: A data architect is a specialist who develops the firm's data strategy, which covers data security, data quality standards, and data movement inside the company.
- Data Administrator: A database administrator is in charge of the administration, management, coordination, and use of the database management system.
The term "data science" is quite imprecise. It includes several academic disciplines. The area of data science has grown exponentially, and with it have the roles inside it. Hence, learn Data Science to make a blazing career in Data Science in 2023.
To upskill yourself, join OdinSchool's Data Science Course. Break into the data science industry with credible hands-on experience!
