With more than half of all companies, both big and small, now adopting big data strategies, data scientists are becoming a job that’s in high demand. When looking at data science jobs, there’s a lot of variety from type of job to the size of a company. However, when you know what you want out of a data science job, you can make a more informed decision about who to work for.
If you wish to learn more about Data Science, then take a data science online courses become an expert in it.
Here is everything you need to know about being a data scientist.
Understanding Their Job
When you’re looking at life as a data scientist, you might think that it would be glamorous. However, most scientists consider themselves to be “data janitors” When you’re working with data, you need to start off by scrubbing it clean and getting rid of the excess to get “clean data”.
If you don’t have clean and quality data, you won’t get accurate results from whatever you do with that data. If you want to tackle any problem, you need to ensure that the data you’re using is tightly controlled and free from any unforeseen issues.
You need to be able to understand all of the elements of an issue that you’re handling and measuring. If you’re trying to calculate “sales” for a product, you might need to whittle down a whole lot of factors before you get to a figure that you can use. If you’re not able to get data that’s as pure as possible, you might make bad recommendations or conclusions that don’t correlate with the reality.
Scientists or Analysts
There’s a type of person working with data who is considered a data scientist another that would be called a data analyst. Depending on the company that you work for, you might have a role that’s more suited to one than the other.
Data science teams usually consist of one or two people that manages any of the data scientist related tasks, looking carefully at data and controlling for future research. If you’re at a company that allows you to have a more fine-grained effort, you can be an analyst, handling less technical elements of the work. There will be another person who is more technical and does the qualitative or machine learning work who would be considered a data scientist.
As the industry is still fresh with a lot of room for growth, definitions aren’t set in stone. Some people manage the efforts of both roles. When you’re looking for a job in the field, look at the company and the requirements rather than the title.
Startups and Large Companies
Data scientists are now being employed at companies of all sized. They help large companies make decisions about where to move their troops next and tell small companies where they can enter a crack in the market.
Choosing to join a startup or a large company all comes down to how you like to work. Your working style and your preferences are going to make all the difference with your decision.
Startups are going to offer you a lot of freedom and micromanaging. There might be ebbs and flows or it could be a high-intensity job.
Large companies offer a lot more structure as well as some benefits you don’t get from small companies. You’re less likely to be blindsided by a company shuttering suddenly. You’re also likely to be able to get better health and vacation benefits from a larger company.
If you like a company, then you should work with them. Starting your career, don’t write off a company based on size alone.
What About Automation?
While automation has been a great tool for companies looking to use data science to their advantage, some people worry that it’ll eat all the jobs up one day. However, all fields are seeing changes due to automation and machine learning. While humans had to once sit around and do all of the calculations, now humans are able to let computers do that work and make the decisions about risks.
In a lot of industries, humans can be replaced. However, in a lot of cases, people still need to handle all of the communication and creative thinking. The interpretations and understanding of problems need to be managed by people.
Machines and automation are here to make life easier. They can do a lot of the nitty-gritty work of data sanitization that now takes humans forever to do.
Data-driven automation is going to add a lot of value to medicine by aiming to diagnose health issues faster and easier. In language processing and translation, we’ll see machine learning and data science helping to improve communication across borders.
Working With Others
This isn’t something you’ll find in guides to data science for beginners.
Once you’ve figured a few things out in the world of data-driven analytics or as a data scientist, you need to apply what you’ve figured out. However, that’s where the human element comes in again. If you’re not prepared to work with others and build a convincing case with your employer, you might struggle to make your work stick with them.
Management is going to be resistant to change, no matter what industry you’re in. Most companies like to make decisions and then find data to prove the issue. However, that’s not the way to do things.
As a data scientist, you’ll need to walk a delicate line between making your boss look good and doing the right thing. Data scientists struggle with this every day.
Data Science Jobs Are in Demand
When you’re looking at the wide variety of data science jobs out there, it turns out that there’s a lot to be excited about. Data scientists are a powerful group of people who are driving the future of the industry and will shape the world we come to know.
If you want to use data science to improve the kind of inventory at a company, check out our latest guide.