Data science forms one of the most lucrative yet least understood professions today. SAS notes that a data scientist uses their technical skills to solve complex problems, requiring them to be part computer scientists, part mathematicians, and part trend-spotters. For the non-specialist, a data scientist might sound like the coolest job ever, or something boring to do with numbers and graphs. The reality is quite different.
Tech Republic called data science the most promising job position in 2019, citing 56% growth in the volume of job openings within the field. In this article, we’ll try to outline how to join the army of data scientists that are staking their claim on the world of technology.
Why Become a Data Scientist?
If you’re a student or someone looking to switch careers, data science looks like a great chance to do important things. It’s a versatile and lucrative field, and there’s no shortage of job openings. Most importantly, data science is an in-demand industry, meaning that you may have good job security. However, mastering data science can take most of your life, and many employees in the field haven’t done so, despite working with data science for over a decade. If you’re tenacious and want to experience the cutting edge of technology (and push that boundary yourself), a career in data science may be worth it. Some data scientists like the ones at Black Fin Marketing focus on a single industry, such as Tradition Company, to provide services to. This single-mindedness keeps them focused on solving particular problems and doing so efficiently.
How to Become a Data Scientist
There’s no simple path to becoming a data scientist because there are several categories that you could potentially specialize in. There are a few learning paths that make life easier to gain the skills necessary to become skillful in data science. We’ll start from the prerequisites and then break down the things that all data scientists should know before they choose a specialty.
Data science is a mathematically dense field. You will need to know calculus, algebra and basic statistics to get along, including probability and inference. You will also need to know a programming language. The most popular language to learn among data scientists is Python since it’s versatile and relatively simple. Within Python, your focus should be on learning how to use the built-in data types, and standard programming patterns such as iterations, conditionals, and functions.
In the essential knowledge section, you should get familiar with understanding how to analyze data with Pandas. Additional knowledge in the field of statistics will include hypothesis testing, variable association, and variance analysis. You will also need to understand SQL and how to use it to query Advanced Programming Interfaces (APIs) in Python. It would help if you also learned the basics of both supervised and unsupervised machine learning.
This point is where most people get to and stop because learning becomes slow and difficult. Statistical intermediate knowledge should include Bayesian statistics, causal experiments, data munging, and econometrics for use in situations where experimentation isn’t possible. You’ll also have to get a grasp of data ingestion, web scraping, and big data environments, including how to utilize unstructured data in your development. You may also need to understand and use transformation pipelines and containers to create APIs. Finally, you should be able to use self-correlated data in machine learning models.
While we call this area advanced knowledge, a lot of the experience here is only necessary in certain specific cases. However, knowing this information does impart knowledge on how a machine learning system functions on a fundamental level. An advanced data scientist should be aware of deep learning models, including natural language processing (NLP), and deep reinforcement learning (DRL). Within the statistics field, a professional should know instrumental variables and use them with causal modeling and score matching.
Markov Chain Monte Carlo (MCMC) and synthetic counterfactuals are also crucial to understanding the development of a pseudorandom system that makes sense. In the data realm, the advanced data scientist may delve into graph-oriented data, Kubernetes, low-latency APIs, and streaming data for use with their programs.
Areas of Specialization
When you’ve gotten the hang of the basic knowledge, your intermediate, and advanced knowledge might be different if you have a particular direction you want to head in. Specialization in data science can range between teaching machines how to speak to humans and understanding language to teaching them how to drive. Your experience as a data scientist will vary depending on where your area of expertise lies.
Where Does That Leave You?
The road to becoming an advanced data scientist sounds like a long and arduous one. In the interest of honesty, to get to that level may take decades of work. However, getting started as a data scientist takes a fraction of that time. Forbes mentions that there are more direct ways to become a data scientist by limiting your scope to what you’re interested in. Streamlining your path to what you want to work with will narrow your opportunities, but make your goals far more achievable.