Beginner's Guide To Data Science
It’s important to ask before diving in, what exactly does it take to be great in the illustrious field of data science? A lot, it turns out. Data Scientists are expected to have a strong foundation in mathematical and statistical theory, from linear algebra to regression analysis and beyond. Additionally, computer programming proficiency is essential for the everyday functions of someone in a Data Science role. While this typically means a solid grasp on either Python or R, many industries highly value candidates with a great understanding of SQL, and additional languages (Java, C, and more). Perhaps most forgotten about the data science toolbox is the need for good written and visual communication skills. Because Data Scientists cover the full process of gathering, cleaning, analyzing, and presenting the data, it is essential that a good data scientist can explain their findings in a way that is digestible, actionable, and can create business value. Because of this, it is highly advantageous for a Data Scientist to have general business acumen as well.
If this sounds at all daunting, then you are not alone — it certainly was to me before I started. Thankfully, NYC Data Science Academy strives to make all graduates not just comfortable with these skills, but excellent at them. Here, you will find a curriculum that gradually builds upon itself to provide you the groundwork needed to master these skills.
But what exactly is the purpose of the Data Scientist? While it may be a little less straightforward than a doctor or lawyer, the basic function is actually quite simple. Data Scientists identify a business problem or question, and use data to help improve or answer it! This involves a couple of basic steps:
- Identify the question: Which factors contribute to ad clicks? What internal factors are preventing us from maximizing efficiency? What demographic of users prefer product X over product Y?
- Collect and analyze the data: This can include researching different accessible databases or writing a program to “scrape” the data from the internet. This step also includes the “cleaning” of data to make it more workable, as well as the creation of new variables from our existing ones! This process, called as ‘feature generation’ often requires some creativity. From our dataset, we begin to offer a hypothesis.
- Creating Models for visualization: Finally, data scientists create some sort of model or algorithm that can be applied in order to derive insights. Generally speaking, this allows us to derive connections, correlations, and causations within our data, more broadly allowing us to better understand the full story of what is occurring. From there, we present and deploy the model in a way that can create business value, often by using the model to make some sort of prediction about the impact of future data.
Some of the techniques used to accomplish this include Regularization, Parameter Tuning, Dimension Reduction, and Feature Engineering. When deploying models, you’ll likely use platforms like Amazon Web Services (AWS), Kafka, or Docker. All of these topics will be covered in depth in the bootcamp.
Because of the diverse applications of data science technology, the types of jobs you can pursue after graduation span far beyond “Data Scientist.” Many alumni have gone on to take jobs as Data Engineers, Data Analysts, Machine Learning Engineers, Software Engineers, Product Managers, Research Analysts, and beyond. Some of our most popular hiring partners include IBM, Google, Spotify, PwC, Amazon, Facebook, and JPMorgan.
As I stated before, this may seem like a fairly daunting amount of information to take on, let alone the challenge of actually getting started. However, rest assured, we always strive to make sure every student can conceptualize and truly understand these concepts in a way that is tailored to them. If you have any questions about the information covered here, or just a general question on all things Data Science, don’t hesitate to contact someone from our school for more information.
This article is contributed by Sammy Dolgin, a graduate of the NYC Data Science Academy and Loyola University Chicago's Quinlan School of Business.