What is the difference between machine learning and data science career options abroad?

Before moving to the different career options in data science and machine learning, we first need to understand the difference between data science and machine learning. 

Data science is a very vast field that requires the analysis of large and small sets of data to derive useful information in order to formulate informed decisions. Therefore, data science may need to build, maintain and preserve a large number of databases.

Machine learning refers to making machines learn new things and make decisions based on algorithms and data analysis. In machine learning, devices make decisions independently without requiring any programming from humans. 

So now you may have understood that there is not much difference between data science and machine learning. In fact, both are a requirement of each other’s roles. Therefore there is not much difference between the different career options available in these fields. 

So now, let’s understand the top 3 job roles in each field.

Machine learning career options: 

Machine learning engineer: An ML engineer is the one that uses algorithms and data sets to run experiments with different ML libraries. There is a great demand in the market for skilled machine learning engineers. The average package that an ML engineer gets on the entry-level is $62,000. 

Human-centred Machine learning designer: This role requires systems identical to humans that are recognisable and processable by machines so that humans do not have to design or program for every new information manually. The average starting salary for this role is $61,700. 

Computational Linguist: In most simple terms, a computational linguist builds, maintains and updates voice recognition systems. The average starting salary for this role is $75,700. 

Data Science Career options: 

Application Architect: These professionals collect data collected by applications and analyse it to identify how these applications interact with their users and vice versa. The average starting salary for these roles is $95,000. 

Data Engineer: In most layman's terms, these professional processes the data gathered by or stored in databases. They also build data pipelines and data ecosystems. Yes, I know this might seem too confusing. It means creating a network of data sets to make it easily accessible to other data scientists. 

Business Intelligence Developer: A BI developer is the one that makes decisions about the business after analysing the data sets. They also forecast the upcoming market trends. The average salary for this role is $81,400. 

There are numerous job roles in data science and machine learning. I have mentioned the ones with the brightest future. I tried to simplify them as much as possible. So, what excites you the most?