Thanks to Samuel Nwobodo for contributing this article!

Data Science is a buzzword right now. It’s literally for everyone – what makes Data Scientists unique is not the fact that they are in the field but that they understand the need, purpose, and incredible impact it can make on our daily lives. But how can you tell if Data Science is for you? This article will explain what data science is and provide an overview of the field.

What is Data Science?

Data science is the study of data and involves developing methods of recording, storing, and analyzing data to extract useful information effectively. The primary purpose of Data Science is to help us find and understand unique patterns in data, using various techniques or tools to analyze and draw insights. Processes range from data wrangling to data pre-processing as well as making useful predictions from the data. From these predictions, solution-oriented conclusions are then derived.

Let’s take an example: Consider the COVID-19 case. This pandemic is taking its toll on us, and one of the major ways we have been fighting back – aside from our wonderful healthcare workers on the frontlines of the battle – is by using the data collected from the hospitals or isolation centers. We then interpret this data to know the trend, to figure out the locations that need testing the most, and to understand which people might be most vulnerable to the virus. From the data analysis that’s already been completed, we know that the elderly are more likely to die from COVID and this is just one example detailing the importance of data science.

Without the expertise of professionals who specialize in delivering insights from data, the data itself would honestly be useless. It is becoming more evident each day that there is an extraordinary amount of value from processing and analyzing data; this is where data scientists come in to perform their magic.

What do Data Scientists do?

The goal of a Data Scientist is to derive solution-oriented conclusions from data for companies and organizations to make better decisions.

According to Career Explorer, a typical day for a data scientist involves extracting data from various sources, running it through an analytics platform, and then creating visualizations of the data. Data Scientists will then proceed to spend hours sifting and analyzing the data from multiple angles, looking for trends that may uncover problems or opportunities. Any insights are then communicated to business and technology leaders with recommendations to adapt existing business strategies.

What Values do Data Scientists Add to a Business?

The values that come with data science to businesses are numerous. Some of the common ways are:

  1. Decision-Making with Data-Driven Evidence.

Businesses are trying as much as possible to reduce their risk in decision-making, and they need to be sure of every move they are about to make. With Data Science, they can make decisions based on data not unevidenced opinions.

  1. Interpreting Data into Actionable Insights.

Data Scientists play a vital role in fishing out unique insights from raw or messy data, which can then be transformed into action-oriented solutions.

  1. Identifying Target Audiences.

One of the biggest reasons why customer data is collected is to identify demographics. Data is often collected using Google Analytics or customer surveys. Data Scientists take this existing data that is not necessarily useful on its own and merge it with other data points to generate insights an organization can use to learn more about its customers and audience. With this in-depth knowledge, organizations can then tailor services and products to customer groups and help profit margin flourish.

  1. Testing Decision Options.

In an organization, half of the battle involves making certain decisions and implementing those changes. The other half involves the effect of those changes on the organization. It is crucial to know how these changes affect the organization; this is where Data Scientists jump in to do their thing – to measure the key metrics that are related to essential changes and quantify their success.

  1. Managing Company Strategies.

Due to the importance of the position, a Data Scientist is likely to be a trusted advisor and a strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytical capabilities. 

Advantages of Data Science as a Career

With the way the Data Science field is massive and still proliferating, it has its fair share of advantages and disadvantages, which are important for people to know what they’re getting into. 

At first, I was interested in the field because I googled the average salary of a Data Scientist and the pay was good. But to be honest, there are so many other advantages of Data Science. Some of these benefits are as follows:

  1. High Demand.

With the enormous amount of data created every minute via websites, social platforms, mobile, or desktop apps, organizations need people who can make sense out of this data; if not – the data will be useless. This makes Data Science a highly employable job sector. It is one of the fastest-growing jobs on LinkedIn and predicted to create 11.5 million jobs by 2026.

  1. Good Salary.

According to Glassdoor, the average salary of a data scientist in the United States is $113,309/yr. This places Data Science amongst the most highly lucrative careers.

  1. Versatility.

Data science has numerous applications. It is widely used in healthcare, banking, e-commerce, and tech industries. This gives you the opportunity of choosing to work in any data-driven field, as the case of COVID-19 highlighted at the beginning of this article.

  1. Constant Advancements.

Organizations require skilled Data Scientists to analyze and interpret their data in order to make better-informed decisions.  Through this process, they not only analyze the data but also improve the quality of data and direct the organization towards the kind of data they need in order to make better decisions.

  1. Beneficial Products.

Data Science has enabled industries to create better products tailored specifically for customer experiences by making predictions from the existing customer data. For example, the recommendation systems used by online shopping and streaming platforms provide personalized insights to users or customers based on their historical data on purchases, site visits, etc. Let’s say you streamed ten hip-hop music videos on YouTube on Monday. When you log into YouTube again on Wednesday, you’ll see like a thousand recommended hip-hop music videos for you, similar to the ones you saw on Monday. This is thanks to Data Scientists.

Disadvantages of Data Science as a Career

While there are several positives to Data Science, we must also know the limitations of Data Science. Some of them are:

  1. Blurry Field.

Data science is a very general term, with so many definitions attached to it and this makes it hard to pinpoint a clear definition. A Data Scientist’s role depends on the field that the organization is specializing in. Some people have described Data Science as the fourth paradigm of Science. At the same time, critics call it a mere rebranding of statistics.

  1. Data Privacy Problem.

Data is the fuel for so many organizations, and Data Scientists are in charge of managing and making use of this fuel for the organization’s success. However, the data utilized in the process may breach the privacy of customers. The ethical issues regarding the preservation of data-privacy and its usage have been a concern for many industries.

  1. Mastering Data Science is Near to Impossible.

Data Science involves many other fields: Computer Science, Statistics, Software Engineering, and Mathematics. Mastering each of these fields is far from possible task. While many online courses try to summarize the whole field, there will always be a skill-gap considering the immensity of the field. Data Science is a very dynamic field that requires constant learning.

With the values, advantages, and disadvantages of Data Science stated above, it is safe to say that Data Science is an incredibly diverse field. I believe to succeed in anything that you do, you have to understand at least every aspect of that field, so you do not get surprised when you hit bumps on your way. Data Science is an ever-evolving field with many sub-fields and techniques/tools to master. This involves a step-by-step process, which requires consistency and patience. While the pros will motivate you, the cons exist to help you make careful decisions.

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