Data science is a hot and rewarding career field. In 2021, data scientist jobs will be the second fastest growing occupation in the United States, according to the Bureau of Labor Statistics. Data scientists are people who use statistical and analytical techniques to interpret large quantities of data from different sources for businesses or other organizations. They may work on an organization’s IT team or as a freelancer. The demand for this skill set has risen quickly over recent years due to the growth of technology in all sectors, including healthcare, finance and education.
Data scientist jobs are in demand because they may require skills not common in other roles. Data scientists have a strong background in statistics and quantitative analysis, and these skill sets can be difficult to find without some level of training or experience. As data science becomes more pervasive across all sectors, the demand for this skill set will continue to grow as organizations need to extract value from the data they collect.
Reasons to pursue a career in data science
Data science is a stream that includes exploration, analysis, model creation, and the generation of meaningful information from raw data. Data science is booming day by day and it is one of the most popular things in the industry. In every organization, data is increasing tremendously, and the need to analyze this data and generate ROI is very clear. But to do this, there is a huge need for skillful data scientists or data engineers. A data scientist can learn many skills to turn this potential into real success by working in big organizations where there is an opportunity to build AI factories.
Need for Skilled Resources
According to a study by “Mckinsey Global Institute”, the US will face a shortage of data scientists that will be around 190,000 and 1.5 managers and analysts who can manage data using big data by 2018. Because tools and technology are now available, there is a high demand for data scientists in India, but skilled individuals are in short supply.
Underestimating the power of data
Due to digitalization, the demand for data scientists is increasing day by day. The collection of data from the physical world has increased in the past few years. With the increase in the 5G network, things will be easy on the ground level, but the amount of data will increase. To handle this data, there will be a huge demand for data scientists.
Handsome salary
With the increasing demand for a data scientist or analyst, there is a huge salary opportunity as it is one of the highest paying jobs in the market. According to one of the most popular job search websites, the average salary for data scientists is $62000 USD. When it comes to India, the average salary of a skilled data scientist is 19 LPA. This can be considered to be a handsome salary as compared to other domains.
surplus of roles and opportunities
Data scientist is a popular term, and many people only look for data scientist jobs. But within data scientists, there are many other sub-roles that can be dedicated to some particular work. These roles can be considered as other names of data scientists or very similar to the same. Data analysts, Business Analysts, Data Engineers, Database Administrators, and others are examples of such roles or positions.
Educational VS Real-world Data science
Being an experienced data scientist, I can tell you there is a huge difference between real-world projects and educational projects that are used in institutions to learn machine learning and other related things. In institutions, you generally train and test your machine learning model using any dataset. These datasets are used by every institution to teach students due to how these datasets are cleaned and pre processed. Here you input some data and get an output that is already known due to previous research. But if we talk about real world projects related to data science and ML, things are pretty different. There will be real challenges related to data or datasets. Sometimes it will be difficult to gather the actual data or sometimes the data will be too small to analyze anything.
By – Priyanka Dhillon
