Data science is booming day by day. In the market, the job place is quite saturated.
There are many skills you need to become a data scientist. There is a misconception that a data analyst and a scientist are pretty similar to each other. Well, this is a myth. There is a huge difference between a data scientist and a data analyst. The major difference between data scientist and data analyst is a data scientist has all the skills that a data analyst has. A data scientist has other many skills too. Like advanced statistics, programming, machine learning, predictive analysis, deep learning, etc. In this blog, we will discuss about what are the skills that a data scientist require.
Here is a step-by-step guide to the skillset of a data scientist.
- Knowledge of Programming Language: A data scientist needs to have some basic programming knowledge. There are many programming language like Python, R, Java and many more. But the best one to go is Python or R. Because there are a huge libraries in these two.
- Knowledge of Statistics: A data scientist must have knowledge in statistics. Because statistics plays an important role in data science. This is a must for a data scientist.
- Knowledge of Math: The role of math in data science is vast. Specifically, you need to focus on statistics, linear, algebra, probalilities, and differential calculus. Because most of the algorithms basic is basically based on these things.
- Database Management System: The fourth skill of a data scientist is database management system. As a data scientist you have to retrieve the data that is stored in the data base. This data base is both sequel data base and no sequel database.
- Machine Learning and Deep learning: Just four or five years back, if someone knew only machine learning it was enough in the field of data science. But now the case is completely different. Now there is a lot of competitions. Nowadays, every client recruits a data scientist on the basis of both machine learning and deep learning. Not only deep learning but also advance deep learning like object detection, EULA algorithms, different kinds of transfer learning. There are a lot of libraries about deep learning. One has to have skill in these libraries too.
- Knowledge in Big Data: Again, companies nowadays hire someone with knowledge of big databases or Hadoop data base. Now every company has a big Hadoop database. So, this is another skill that is required in data science.
- Reporting Tools: A data scientist should also have some amount of knowledge in reporting tools. Because, at the end of the day, you need to publish the reports and provide them to the stakeholders. So knowledge in reporting tools is another skill that a data scientist should have.
- Model Deployment: In data science, after creating a model, you have to deploy that model to see whether that model is scalable or not. A data scientist at least needs to know two to three deployment services. These things will make him understand what is the advantages and disadvantages of those services, which one is better and all these. This thing will make a data scientist really skilled.
- Cloud Computing Services: Now, most of the companies are using AWS and Azure. So, it’s better for a data scientist to know about cloud computing services.
These are the skills that a data scientist needs.
