Data science means you need the analytical know-how of math and statistics, the coding skills needed to work with data, and an area of subject matter expertise. In this article, I’ll walk you through who uses data science and what types of data science jobs are.
Who Uses Data Science?
Without the subjects of data science, you might as well call yourself a mathematician or a statistician. Likewise, a software programmer without subject matter expertise and analytical know-how might better be viewed as a software engineer or developer, but not a data scientist.
As the demand for information about data grows exponentially, every field is forced to embrace data science. As such, different flavours of data science have emerged. Here are some titles under which experts in each discipline use data science:
- Ad Tech Data Scientist
- Director of the digital banking analyst,
- Clinical data scientist
- Geo-Engineer Data Scientist
- Data scientist in geospatial analysis
- Data Scientist – Retail Personalization
- Clinical informatics analyst in pharmacometrics.
Types of Data Science Jobs
I hope now you know who uses data science and what kinds of skills you need as a Data Scientist. Now let’s have a look at the types of Data Science Jobs:
In some companies, being a data scientist is synonymous with a data analyst. Your job may consist of such tasks as extracting data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards.
You can occasionally analyze the results of an A / B test or take the lead of your company’s Google Analytics account.
Some companies get to the point where they have a lot of traffic (and an increasing amount of data), and they start looking for someone to put in place a lot of the data infrastructure that the company has. will need to move forward. They are also looking for someone to provide analysis.
You will see the vacancies listed under “Data Scientist” and “Data Engineer” for this type of position. Since you will be (one of the) first data hire solid expertise in statistics and machine learning is less important than strong software engineering skills.
Machine Learning Engineer:
There are several companies for which their data (or a data analytics platform) is their product. In this case, the data analysis or machine learning going on can be quite intense.
This is probably the ideal situation for someone who has a formal background in math, statistics or physics and who hopes to continue on a more academic path.
Data Science Generalist:
Many companies are looking for a general data scientist to join an established team of other data scientists. The company you are interviewing for cares about data, but probably isn’t a data company. Equally important is that you can perform analysis, touch production code, view data, etc.
Typically, these companies are looking for generalists or looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning.
Skills Required in All Types of Data Science Jobs
Data science relies heavily on the math and statistical skills of a practitioner precisely because these are the skills needed to understand your data and what it means. Skills are also valuable in data science, as you can use them to perform predictive forecasting, decision modeling, and hypothesis testing.
The most important skill in data science jobs is math. Mathematics uses deterministic numerical methods and deductive reasoning to form a quantitative description of the world, while statistics is a form of science derived from mathematics, but which focuses on using a stochastic approach; an approach based on probabilities and inductive reasoning to form a quantitative description of the world.
Data scientists use mathematical methods to build decision models, generate approximations, and make predictions.
Coding is inevitable when working in data science. You need to be able to write code so that you can tell the computer how you want it to manipulate, analyze, and visualize your data.
Programming languages such as Python and R are important for writing scripts for manipulating, analyzing, and visualizing data, and SQL is useful for querying data.
Although coding is a requirement of data science, it doesn’t have to be as scary as people claim. Your coding can be as sophisticated and complex as you want, but you can also take a fairly straightforward approach.
I hope you liked this article on the types of data science jobs that companies demand. Feel free to ask your valuable questions in the comments section below.