Back then, major businesses and multinational corporations only had their wits and limited data to depend on for making big decisions. However, in our modern era, the playing field has changed. With the proliferation of data, there’s so much going around that should be analyzed to give companies an edge against their competition and industry.
As a result, data science has been catching steam over the past few years. The most prominent name companies form comprehensive data teams to manage, clean and visualize raw data into consumable reports. Of course, what we’ve mentioned is but the tip of the iceberg, but today we’ll be entertaining a different question; what you need to build a data science career.
Why Is Data Science Growing In Popularity?
Let’s first understand the popularity behind data science and why it’s becoming ever-so-important in the modern-day. Data science might appear to be a boring job surrounded by numbers and blocs of data from an uninformed point of view, but it’s much more than that. In fact, it’s hard to pin down a one-size-fits-all description for a data scientist’s job because of its multi-faceted nature that covers a wide range of processes. However, a cornerstone of its advantages is in smart decision-making and its ability to condense vast amounts of information.
Interdisciplinary Field
Another significant advantage of building a career in data science is that it’s an interdisciplinary field, meaning that you can effectively work anywhere. All businesses make decisions, and each of those companies has data in dire need of attention.
- Identify Opportunities: Data science can involve identifying opportunities and taking the best possible course of action to capitalize on them. For example, a home insurance business might be unsure of where to advertise. Still, a data scientist can narrow down their choices according to averages, values, and available data to make the best decision.
- Mitigate Risks: On the opposite side of the spectrum, data science can also help mitigate risks by pinpointing the least profitable products or errors in the system. From a stock trader and fund manager’s point of view, a data scientist can analyze the markets and pinpoint the financial market risk of sudden bearish movement.
First, You Need A Foundation of Skills
Before anything else, your first order of business is building a foundation of essential skills in a data science career. As the name suggests, a career in data sciences features the relative need for technical knowledge and skills, which can be daunting for some. However, if you’re up for the challenge and enjoy the thrill of making interesting solutions through data, here’s what you need to get started:
#1 Mathematics and Statistics
Number one, you need a firm grasp of mathematics and statistics as these sciences are responsible for building and honing your data literacy. At its core, these two make up the fundamentals of data science, and not having a background in them will leave you with zero clues on how to proceed.
From understanding the basics of variance, clustering, weighted deviations, and quantitative analysis to knowing when and where these methods are best applied will ultimately decide whether you cut for the job or not. So, for those coming with these backgrounds, you might find it much easier to transition.
#2 Programming Knowledge
You will need to be skilled in programming and coding as this career entails the same level of expertise as a software developer. However, you won’t be producing code that results in pre-defined outputs but rather an open-ended analysis of data that can be cleaned, visualized, and presented to key stakeholders.
Of course, some would argue that Excel and other business intelligence tools can be used for data science; however, these applications are minimal and don’t cover the full extent of what data science can do. So, we strongly suggest that you start diving into open-source code like R and Python to give you a baseline.
#3 Business Communication
And number three, you need to know the basics of business communication because you can’t depend on your employer to understand the full depth of data science. Over the years, the career has been sensationalized, and many businesses set unrealistic expectations for their data scientists. Thus, causing many in the field to juggle more work than they could have ever bargained.
You need to ask essential questions like understanding the current data infrastructure of the business, their processes, and the existing responsibility of their data team. Likewise, you will also need to communicate your data and analyses into understandable reports and presentations.
However, Tread Lightly
But before you jump into data science thinking it’s the right career path for you, please heed our warning to tread lightly. Many other people are in the same position as you looking to land a position in this sustainable career, and applications could reach upwards of hundreds for an entry-level role. Thus, leaving only those positions looking for experienced and senior roles.
Still, don’t let this thought stop you, and if you’re serious about getting started, then be prepared to consume webinars, workshops, and maybe even consider getting back into the academe.
