Meet Gino Parages, a former sales and IT business analyst with no coding skills who decided it was time to learn coding to give his career a boost. He chose Dataquest to help him achieve his learning goals and land the job he wanted.Here’s his story...Q: First, what are your preferred pronouns?A: He/himQ: All right, […] The post Learner Spotlight: Gino Parages appeared first on Dataquest ..read more

Data engineer, data analyst, and data scientist — these are job titles you'll often hear mentioned together when people are talking about the fast-growing field of data science. There are plenty of other job titles in data science and data analytics too. But here, we're going to talk about: 1 The "big three" roles (data analyst, data scientist, and data engineer) 2 How they differ from each other 3 Which role is best for you Although precisely how these roles are defined can vary from company to company, there are big differences between what you might be doing each day as a data an ..read more

Python programming is a critical skill for data engineers. When it comes to working with data, there's a powerful library that can increase your code's efficiency dramatically, especially when you're working with large datasets: NumPy. That's why we've added a NumPy for Data Engineers course to our Data Engineering path! At present, this is the ninth of 14 courses in our Data Engineering path — we recently added a course on Algorithm Complexity as well. Completing the Data Engineering path requires a Premium subscription, but you can try out the first mission of this new course, or any oth ..read more

Algorithms are at the center of almost any programming job. And particularly in the world of data engineering, using efficient algorithms is important enough that it's a common topic to be quizzed about in job interviews. That's why we've just launched a new course! Algorithm Complexity is the latest course in our Data Engineer career path. It adds five all-new missions and a completely new guided project aimed at helping you master the assessment and implementation of efficient algorithms to fit your use case. This course requires a Dataquest Premium subscription (which is currently avail ..read more

Today, Stacey Ustian is a data engineer. But the path that led her here wasn’t always easy, and there were a few bumps and twists along the way. Her journey to data science started in a rather unusual place: the law library. After earning her Master’s degree in Library and Information Science, Stacey had taken a job working in the library of a law firm. But she discovered she liked the working-with-information bits of the job more than she liked shelving books, and after a few years she transitioned into a role as a research analyst at another firm. I came across Dataquest and checke ..read more

If you’ve ever wanted to learn Python online with streaming data, or data that changes quickly, you may be familiar with the concept of a data pipeline. Data pipelines allow you transform data from one representation to another through a series of steps. Data pipelines are a key part of data engineering, which we teach in our new Data Engineer Path. In this tutorial, we’re going to walk through building a data pipeline using Python and SQL. A common use case for a data pipeline is figuring out information about the visitors to your web site. If you’re familiar with Google Analytics, you know ..read more

We've got some really exciting news: we've just launched a total revamp of our Data Engineering learning path! This revamped path is designed to be more like our other course paths. You can start it even if you have no prior experience with coding, and it'll take you from total beginner to experienced practitioner with all of the core skills needed to become a data engineer. If you've checked out the path before, you'll notice we've changed a lot. We've added some new courses. We've created and added custom data engineering versions of existing courses to this path. And we've optimized all ..read more

Exciting news: we just launched a totally revamped Data Engineering path that offers from-scratch training for anyone who wants to become a data engineer or learn some data engineering skills. Looks cool, right? But it begs the question: why learn data engineering in the first place? Typically, data science teams are comprised of data analysts, data scientists, and data engineers. In a previous post, we’ve talked about the differences between these roles, but here let’s dive deeper into some of the advantages of being a data engineer. Data engineers are the people who connec ..read more

The data science life cycle is generally comprised of the following components: data retrieval data cleaning data exploration and visualization statistical or predictive modeling While these components are helpful for understanding the different phases, they don’t help us think about our programming workflow. Often, the entire data science life cycle ends up as an arbitrary mess of notebook cells in either a Jupyter Notebook or a single messy script. In addition, most data science problems require us to switch between data retrieval, data cleaning, data exploration, data visualization, and ..read more

In previous blog posts, we have described the Postgres database and ways to interact with it using Python. Those posts provided the basics, but if you want to work with databases in production systems, then it is necessary to know how to make your queries faster and more efficient. To understand what efficiency means in Postgres, it’s important to learn how Postgres works under the hood. In this post, we will focus on the more advanced concepts of Postgres and relational databases. To begin, we will learn how Postgres stores its own internal data for describing, debugging, and identifying the ..read more