What is Data Science?
Data Science is an interdisciplinary field that uses a combination of code, statistical analysis, and algorithms to gain insights from structured and unstructured data.
Let’s break this down.
We’re all kind of familiar with data. It’s stored information. Anything we read online is data. Anything we do that is recorded can be a data point. So a “data scientist” is someone who works with data and uses a structured approach to find insight from a set of data. They do this in any number of fields, from healthcare, to marketing, to medical sciences. The focus of a data scientist is on mathematical models — statistics and algorithms. An algorithm can be defined as “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.” You can think about an algorithm as a set of steps to follow in order to solve a problem, like a Rubik’s cube. If you think back to high school algebra, you might remember the formula for a line on a graph:
y = mx + b
You can determine the slope of a line based on data points and this basic algebraic equation. If you start with two data points, you can predict what a “y” value would be, given an “x” value.
From this we can use the equation to extrapolate an equation.
Which will indicate that if we have an “x” value of 1, the algorithm provides a “y” value of 2.1.
This is basically the kind of problem that a data scientist tries to solve, but with things like what will make a customer purchase a product and how a stock portfolio will perform over time, which are much more complicated and involve way more factors than a simple algebra. They use code and other technologies to build these models, and are constantly working to improve their predictions. They are working for companies like Spotify, Yelp, and Google.
The thing about Data Science, though, is that it is a new field that is still getting defined. While every company seems to want a Senior Data Scientist, the job descriptions can vary incredibly. It’s also a weird field where some companies want a super experienced person with a PhD and others are excited to employ someone at an entry level, someone who may have completed a Boot Camp. One thing I like about this field, is that if you study Data Science, you learn a bunch of skills that can be used in other, similar, roles. For example, a Data Analyst might need to know about statistics, data cleaning, Big Data, and APIs. A Data Engineer should understand the same things, and what a Data Scientist needs to do in order to support them, as well as be able to code efficiently in multiple languages (I use Python and SQL), understand Amazon Web Services, or another Cloud based platform, and other basic data related things.
Needless to say, there are a lot of opportunities and directions you can go in if you choose to learn Data Science. As a person working in data, you have the ability to provide insight to complex information about customers, you can help define how ethical your companies analytics or machine learning models are, you hold a lot of unique and interesting power. You are required to constantly be learning new things, solving new problems and troubleshooting odd inconsistencies.
If this is something you are interested in learning more about, you can check out TechCultivator on LinkedIn and Instagram. They are a company dedicated to helping underrepresented folks get rewarding data science and software development jobs through skill building, mentorship, networking and community.
