Getting Started with a Career in Data
Whether you work in tech now or have considered working in a technical field, you’ve likely been encouraged to pursue a career in data. Data is everywhere and being collected at rapid rates, so it seems like a great choice, but where do you start? What job titles should you be looking for? And, likely the most important question, what skill set do you need to get started?
Over the last few years alone, many great resources have popped up for pursuing these kinds of roles. The best part is you don’t necessarily need to decide which type of data role you’re most interested in before you start learning! That said, it’s good to know what the core paths are as you begin your journey. The specific titles and responsibilities within roles have shifted over the years, but here are a few that are relatively standard throughout the industry:
- Data Analyst: focused on querying and reporting data (typically using SQL), building dashboards and visualizations (using tools like Tableau or PowerBI) and communicating key insights to the business. Other titles can include: Business Intelligence Developer, Report Developer, Analytics Engineer
- Data Engineer: builds and maintains data pipelines (ex. moving data from source systems to analytical databases used by data analysts), cleans and manages data and data models. Core coding languages: Python, Java, SQL. Other titles include: Software Engineer (with data specialization).
- Data Scientist: uses statistics and sometimes machine learning to identify patterns and find opportunities in large structured or unstructured data sets. Core coding languages: Python, R, SQL.
This list is certainly not exhaustive and is just meant as a starting point. The definitions of these roles are malleable and continue to evolve, but can also overlap depending on the organization. For example, a data engineer could be responsible for building dashboards and conducting data analysis. It all depends on the individual, the needs of the team, and the structure of the organization!
You might notice one key similarity between all of these roles: SQL.
SQL (or Structured Query Language) allows you to directly interact with databases. Let’s say you work for a company, XYZ, that owns a chain of hardware stores. These stores likely sell a variety of products like tools, paint, etc. In all likelihood, customer purchases and store inventory are stored in some type of operational database (meaning the database is updated when a purchase is made or items are added to inventory). While we’d want to replicate this data into an analytical database as our first task (replicating the data ensures that any querying we do on the database won’t impact the performance of our sales system since our sales system is built on top of the operational database), our next step in following the data development lifecycle for company XYZ is interacting with the database using SQL.
Not only can we answer core business questions about company XYZ with SQL (ex. How many customers visited our stores last week? What is our best selling product for the month of January?), SQL will be core to cleaning and managing the data in our analytical database. These types of tasks would likely be completed by data analysts and engineers and are a core part of any data development workflow.
Since SQL is so ingrained in every step of the process, it’s a core skill for anyone looking to work in data, regardless of the specific discipline you ultimately choose. This isn’t to say that getting started with an object-oriented language like Python isn’t also a great choice, but SQL is considered one of the easier languages to learn initially and allows you to start working with databases on day one.
Now that you’ve learned about possible roles and a language to start with, it’s time to dive in! Codecademy, Udemy, and many other online sources have free or low-cost offerings for learning SQL. I’ve provided some links at the end of this post!
Most importantly, reach out to data professionals in your network — in my experience, we love to geek out about data and help others break into the industry! Happy coding!
