Data science is a fast-growing field that’s projected to be consistently in-demand for the next decade. According to Paysa, the median salary for data scientists was $117,000 in 2018 and will grow 10% by 2023. It’s no wonder so many people are interested in learning more about this career path. But how do you decide whether it’s right for you? We’ll explore some of the pros and cons of being a data scientist, but first let’s start with a few definitions:
What is a data scientist?
Data scientist is a term that’s used to describe people who work with data. Data scientists use data to make decisions, build models and create new products. Data scientists can be found in many industries including finance, healthcare and insurance.
Data science has become an important part of many organizations because it helps them make better decisions based on facts rather than gut feelings or intuition alone. Data scientists are able to use their knowledge of statistics, machine learning algorithms and programming languages like Python or R for example in order to analyze large amounts of information quickly so that they can get results faster than other methods would allow for
What are the benefits of being a data scientist?
Data science is a highly flexible career path, meaning you can work in many different industries and roles. You could be involved with data science at a large company as part of their IT department, or you could be working for yourself as an independent consultant.
Regardless of what type of organization you choose to work for, there are some commonalities that apply across all industries:
- Variety – There are always new challenges to tackle and problems to solve. Data science isn’t just about analyzing existing datasets; it also involves developing new tools and processes that can improve productivity over time. For example, if your company has never used machine learning before but now wants some automated recommendations on its website (e.g., “if you liked this product then maybe try these other items”), then this would require building an entirely new system from scratch while also figuring out how best to use existing technologies like scikit-learn or TensorFlow so that they integrate seamlessly into this new system (which may involve writing code). The point here isn’t just about coding skills but rather being able to understand how everything fits together–from modeling algorithms down through infrastructure engineering–so that everything works smoothly together when deployed into production environment under real world conditions.”
How much does a data scientist make?
The average salary for a data scientist is $120,000 per year. The median salary is $115,000. This means that half of all data scientists earn more than $115,000 and half earn less than that amount.
Data scientists with more experience tend to make more money, so you might expect them to be earning closer to the top of the range–but even entry-level data scientists can make six figures! For example:
- A recent graduate with two years’ experience who has been working as an analyst at a small company may earn between $80 and $120k annually (depending on location).
- An experienced practitioner could potentially earn up to $300k+ per year working at large tech companies like Google or Facebook in Silicon Valley; however most employers will require at least five years’ experience before hiring someone so senior level positions are harder to come by unless you already have connections within those organizations which makes it harder for entry level people trying their luck there too!
What are the career paths for becoming a data scientist?
So what is a data scientist? A data scientist is someone who works with big data, but this doesn’t mean that they have to have an advanced degree in mathematics or computer science. In fact, most of them don’t. A lot of them are actually statisticians or researchers with backgrounds in the sciences and engineering who know how to use programming languages like Python and R as well as SQL databases like Postgres or MySQL.
Data scientists can work in many different industries including finance, healthcare, retail and marketing (to name just a few). They’re responsible for analyzing large amounts of information so they can make predictions about how people will behave based on their past behavior; these predictions could involve things like whether someone will buy something online or which ad would be most effective at getting them to do so.
Is there any downside to being a data scientist?
The downside to being a data scientist is that it can be difficult to find a job. Data scientists are in demand, but there aren’t enough of them to go around. If you’re thinking about becoming a data scientist, keep in mind that you’ll probably need to spend some time searching for positions and applying for jobs before landing your first one.
The other thing to keep in mind is that while this career path seems like it would provide satisfying work and career satisfaction, there are many people who feel differently about their jobs as data scientists than they expected when they started out on their career path
Are you interested in learning more about careers in data science?
If you’re interested in learning more about careers in data science, we can help. We’ll show you how to become a data scientist and what skills are required for the job.
If you’re interested in learning more about careers in data science, check out our blog post on the topic. We also have other resources available to help you get started on your journey towards becoming a data scientist!