Why is data science such a crucial field? Learn how to be a data scientist, and why you should want such a job.
Do you know what a career as a data scientists looks like? If you’re passionate about seeking the truth in data, consider the path of data-informed projects and development.
A 2019 January report from Indeed showed a 78 percent increase in demand for data scientists between 2015 and 2018. That’s quite the dramatic swing.
And what’s more interesting is that there’s a lot of demand and less supply on the data scientist job market right now. So you’re moving in the right direction if you’re considering the career move. Wondering how to be a data scientist?
Let’s pan out what a data scientist is not before diving into this data-driven science. First off, you don’t get involved with any machine learning (ML).
That branch originated from Artificial Intelligence. As data scientists, you only use ML as a tool. Secondly, data science aren’t just numbers presented on an Excel chart. With any deeper insights into them, they are nothing.
So now that we have cleared that out, let’s dive into the basics.
What is a Data Scientist?
Before learning how to be a data scientist, let’s quickly go over the basics of this profession. If you’re a scientist within this branch, you train to understand data science. This science encapsulates programming skills, statistical analysis, visualization techniques, and business senses.
Now, this last one is interesting. What we mean by business sense is to have the capacity to care about translating any business question into ones that are answerable all the while using available data within your reach.
Take for example user interaction data from Internet-connected devices. How do you mine that data? What information can you obtain to help build better products?
A key element to the success of many companies, specifically tech giants, is its ability to apply analytics to a lot of unstructured data coming from vast spaces.
If you’re one for the information, it takes a special way of connecting all the dots in the randomness of the world. Sometimes you’ll find data that you deem unuseful, but that conclusion should never be repetitive.
As data scientists, you can dive into interesting niches. One of them is production analysis.
To keep up with this role, you have to excel at delivering data-informed stories to make change happen. Whether it’s a product or strategy, deliverance and precision are key elements to improve any current system.
Another responsibility you can have as a data scientist is the development of algorithms. This job is all about incorporating data-driven features into products by way of recommendations or search results.
Setting Jobs Apart
Both of these jobs require analytical outlooks, but the difference is that product analysis are essentially problem solvers while algorithm developers need to understand more sophisticated technical knowledge like machine learning (although you don’t work directly with it) and some software engineering skills.
But, in general, as a data scientist, you will have to do the following tasks:
- Data Analysis
Regardless of what path you choose to follow, know that the field of data science is on the rise right now. According to a Forbes article, data management has become a necessity for e-commerce companies with large data pools and no one to analyze them.
It’s clear that data processing and analysis is as valuable as increasing profits in a time where “big data” is the norm for businesses and organizations. This gives you an idea of how many employers out there are looking for data scientists.
Do you want to be a good candidate? Let’s look at the in’s and out’s of getting into this highly demanding field.
How to Be A Data Scientist
Let’s mark off the general data scientist skills sought by the employer first. We used the modeled that based results from a Glassdoor study and a 2018 data science trends and analysis report.
The average percentage of listings was from 0 to 70 percent. Both analysis and machine learning were top skills wanted. They marked 70 and 75 percent on the results. The following skills follow close by:
- Computer Science
If you want to put these skills to the test, why not practice starting a data project? You’re likely to set up for success if you train as much as you can before applying for the job.
The good news is that there are plenty of online sources that help you maximize your analytic and machine learning skills. Want exercises to improve those skills? View here if you’re up for the task.
Although there are many paths to landing a job in data science, missing out on college education is not one of them. At the very least, you will need a four-year bachelor’s degree and most likely a graduate degree.
And if you’d like to receive a higher median salary, holding a Ph.D. is the way to go. If your goal is also to advance to a leadership position, a master’s degree or a doctorate is a requirement.
Degrees, Classes, and Continuing Education
A degree in data science will offer you the required skills to analyze and process data sets. This type of degree involves a lot of technical information but can have a creative element to them.
Even if you don’t pursue a data science degree, other computer-based degrees can also help you develop techniques for a data scientist career. Such degrees include:
- Social Science
- Applied Math
- Computer Science
When it comes down to specialization, you have many options to choose from. Maybe you’re interested in business and a specific part of the economy (such as insurance or agriculture) or other fields like marketing or pricing. Whatever your interest is, there are a lot of topics and data to cover.
This should be a time of excitement for you if you plan to excel in your field as a data scientist. The world continues to become more data-driven. This leads to decision-making processes that have an expansive effect on everything from cell phones to medical procedures.
And knowing how to be a data scientist can help you safeguard a spot on the field’s aggressive hiring strategy.
If you’re a well-organized person with a knack for analysis and numbers, continue your development and become the best data scientists in the field.