Since the invention of computers, their ability to execute a wide range of tasks has grown at an exponential rate. If we talk about the expansion of data science and analytics, for example, it has gone a long way with a rise in computer power and the vast quantity of data accessible.
This generation is seen as the generation of data where data is considered as the most powerful resource in the world, and hence data science for companies is a vital necessity. The reason for data science is to process, analyze and derive useful information from large sections of jumbled data.
Machine learning has grown in popularity as a result of the virtually unlimited amount of available data, inexpensive data storage, and the advancement of less expensive and more powerful computing. Many sectors are already creating more powerful machine learning models that can analyze larger and more complicated data sets while delivering faster, more accurate answers on massive sizes. Machine learning techniques help companies to discover economic possibilities and analyze the potential risks in the future.
What is driving the demand for data science and machine learning?
The omnipresence of data: In the past decades, organizations were largely concentrated on storing, cleaning, and handling data. However, now the companies see data as an asset that can be used to make insightful decisions by filtering this data through data science analysis. The graph below shows that the amount of data that will be generated in the future will be far more than today. With the rise in data consumption, the need for its evaluation will become a necessary practice in all the business to achieve their goals.
Use of data in human resource performance:
According to McKinsey’s research, the companies whose staff and management are undereducated on data principles, are underperforming. High-performing businesses credit at least 20% of their EBIT to data analytics. Furthermore, the performance difference between organizations with a data-educated staff and those without data skills is increasing every year. The actual bottom line is that training people to grasp data concepts is beneficial. It may even be necessary for long-term survival.
Shortage of skilled professionals and skill gap:
Organizations are now facing many hurdles while executing their aim of consistently innovating the use of data. They are now having difficulty in recruiting the required talent to achieve their goals. Because of a highly competitive employment market, high wage expectations, and a lengthy period to fill data-related jobs organizations are facing problems in executing their objective of having a data-driven approach.
Most of the courses and degrees lack technical skills.
In the following statistic from a KPMG CIO survey in 2019, it is clearly visible that there is a critical lack of data and analytical capabilities. Similarly, a Teradata poll revealed that half of company decision makers are concerned about a data skills gap and want additional data and analytics training. An analytical professional therefore possesses preferably highly specialized knowledge in one or two fields, such as data science, programming, or domains and adjacent abilities, such as critical thinking, communication, and visualization. This is a tough profile to search for in the present job market.
How to upskill yourself in data science and machine learning ?
As we have already discussed above, there are many factors leading to the demand for data science and machine learning so it becomes important to upskill yourself in machine learning and data science. You can simply search data science programs near me and you will get the list of data science courses near you. For example if you are from Noida you can simply search data science programs near me and you will get programs from DataTrained Education. Even Though, there are plenty of courses and programs flooding the internet, it is hard to find the best online data science programs in India.
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