When we entered the era of Big Data, the one thing we needed was the storage to store that data. The thing that concerned enterprises the most was to build solutions for data storage. A lot of organizations dived into this pool, and we were successful in creating the storage solution. These solutions include Hadoop and other frameworks.
Now, when we have found the storage solutions, the processing of this data is the central focus. The data in this enormous amount was stored but we did not know how to treat that. Right at that time data science comes into the show. Data science is the solution we were looking for as it processed our large amount of data and turned into some valuable outcomes for us. The data we had was coming from a lot of different sources like text files, instruments, sensors, and many more. We had business intelligence tools, but they were not competent enough to treat data this large. That was the reason we had to look elsewhere because we needed advanced tools and algorithms to process this huge data to bring out some meaningful outcomes. This is the sole reason why data science became so popular very quickly.
In data science, there are some major areas like predictive analysis, text analysis, descriptive analysis, and inferential analysis. All of these areas are a vast world. For doing something efficiently, we need professionals. In the same way, to perform data analysis, we need some professionals who can do it efficiently enough to pull out some fruitful results for us. These professionals are called Data Scientists.
Who is a Data Scientist?
A data scientist is a data science professional who is responsible to collect data, analyze it, and process it to pull out valuable insights. These insights are then used to make decisions for the betterment of the business. To fulfill his job, a data scientist needs to have hands-on experience in advanced analytics technologies. These technologies include predictive modeling and machine learning techniques.
There are a lot of data analytic tools available in the market. And knowing how to use them is an add-on for the data scientist. As it saves a lot of time and makes the work easier for him and the team.
Reasons Behind Data Scientist Leaving their Jobs
There are no limits when it comes to data science. This industry has seen tremendous growth in the recent past and will continue to do so. The job roles like data scientist and artificial intelligence engineers are taking over the industry. According to a survey, there has been a growth of 29% in the demand of data scientists within just a year. According to another survey by IBM, there will be around 2.7 million job openings related to data science.
Even after all of these positive things, there is something that is bothering data scientists. As they are quitting their jobs or changing them quite rapidly. There must be a back story to this. Let's look into the reason why this is happening. Meanwhile, if you want to know more about data science and data scientists, you can join our training program.
- Expectations vs. Reality
The most severe issue in the field of data science is expectations. In industry, things are not as we expect, and this is where expectations and reality part ways. It is about what data scientists think they would do in industry and what they do in the industry. You will find different reasons if you go from one data scientist to another. The experience of a data scientist also plays a vital role here.
If we take an example of junior data scientists who are just entering the industry, there is a big lot whose knowledge is coming from books or some online courses. They do not know how to work with datasets in the real world. Some of them would not even know how does a pipeline work in data science, and how to model data and deploy the application.
When an organization is hiring a data scientist, they expect you to know some basic things about data science like how to collect, store, process, and how to deploy the data. This is a big mismatch and it is becoming a major issue why they are quitting their jobs this rapidly.
- Mapping Data Science with Business Goals
Another problem that leads to data scientists leaving their jobs is the hype that has been created around data science and artificial intelligence. All the business owners want to show the world that they are the best, in business and for that, they invest in AI technology. But it does not work the way they expect it to. If you learn data science, you will get to know how time-consuming these processes are. The projects of data science take a lot of time to complete, due to all the experiments and hit and trial method. This is something that does not sit well with the owners.
- Lack of Upskilling for Data Scientists
Everyone loves new challenges, as it allows them to learn new skills. But, in data science, there are no new challenges after a specific time, and you keep doing the same stuff over and over. Every data scientist would love to work on new frameworks with new tools. After a specific time, this brings a lack of motivation, and data scientists try to move away.
- No Clear Benchmarking in Salaries
Everyone loves new challenges, as it allows them to learn new skills. But, in data science, there are no new challenges after a specific time, and you keep doing the same stuff over and over. Every data scientist would love to work on new frameworks with new tools. After a specific time, this brings a lack of motivation, and data scientists try to move away.
- Short-term Projects
It is the time when companies are more inclined towards short-term projects, and they outsource these projects to the data scientists. Companies are afraid to keep a data scientist for the long-term, they are targeting freelancers for that.
The data science industry looks like a goldfish to us, but to data scientists, it is not anymore. Companies are only focused on fulfilling their business goals, and for that, they keep pressuring data scientists, which is not right at all. That is all from us. If you want to know more, you can contact our exerts.