In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).
We have been able to process such a voluminous amount of data. We are able to analyze and draw insights from this data owing to these advanced computational systems. However, despite all these advancements, data remains a vast ocean that is growing every second. While the huge abundance of data can prove useful for the industries, the problem lies in the ability to use this data.
As mentioned above, data is fuel but it is a raw fuel that needs to be converted into useful fuel for the industries. In order to make this raw fuel useful, industries require Data Scientists. Therefore, knowledge of data science is a must if you wish to use this data to help companies make powerful decisions. According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary.
This makes Data Science a highly lucrative career choice. It is mainly due to the dearth in Data Scientists resulting in a huge income bubble. Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.
A Data Scientist enjoys the position of prestige in the company. The company relies on his expertise to make data-driven decisions and enable them to navigate in the right direction. Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales.
