In the fast-growing field of data, the “big three” job roles are data engineer, data analyst, and data scientist. Figure out which is the best fit for you.
Learn how Dataquest’s philosophy sets our platform apart from other data science learning tools, and what we’ve learned from years of teaching data science.
Being a jack-of-all-trades may not be the best approach when it comes to getting a data science job and building your data science career.
Dataquest’s learning platform is user-friendly enough that if you’d like to, you can simply dive right in. But if you’re the type who likes to flip through the user manual first, this article is for you. In it, we’re going to cover the basic features of the Dataquest platform, and pass along some helpful tips […]
Deep learning is a type of machine learning that’s growing at an almost frightening pace. Nearly every projection has the deep learning industry expanding massively over the next decade. This market research report, for example, expects deep learning to grow 71x in the US and more than that globally over the next ten years. There’s […]
One of the reasons that R is a top language for data science is that it’s great for data visualization. R users can take advantage of the wildly popular ggplot2 package to turn massive data sets into easily-readable charts in just a few lines of code. That can be incredibly valuable for presenting your data, […]
Editor’s note: This post is the result of a collaboration with PredictX, a decision automation platform. Author Joni Lindes is a content writer at PredictX. AI is set to disrupt our current society on a major scale. According to Indeed, the number of roles in AI has risen by 485% in the UK since 2014, […]
Learn 12 useful command line/terminal commands for data science work.
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 […]
Advancing your skills is an important part of being a data scientist. When starting out, you mostly focus on learning a programming language, proper use of third party tools, displaying visualizations, and the theoretical understanding of statistical algorithms. The next step is to test your skills on more difficult data sets. Sometimes these data sets […]
This post is partly for myself and based on various peoples conversations – it is also inspired by On Being a Senior Engineer. I’m trying to answer questions like ‘what do we expect from a Senior Data Scientist’. My job title is ‘Senior Data Scientist’ and I often joke I’ve no idea what that means. […]
This post looks at the World Bank World Development Indicators (WDI). This massive collection has data in several categories: demographic, education, work, poverty, health. It includes both country-level data and various aggregates by different criteria: geographical regions, income levels, etc. The UK Data Service has a useful guide as well as access to the data. […]
Today I want to go on an excursion in “catalogues as data“. The UK National Archives’ Discovery catalogue is an excellent resource for this activity, because a) it has a lot of records that have document descriptions at ‘item’ or ‘piece’ level in the catalogue, containing quite structured information (like dates, places, occupations) that can […]
Women are underrepresented in STEM fields – science, technology, engineering, and math. For instance, women made up 27% of people employed in computer and mathematical occupations in 1960. But instead of growing over several decades, as many more women participated in the workforce overall, that number had declined to 26% by 2013, according to a […]
Getting started in data science can be overwhelming, especially when you consider the variety of concepts and techniques a data scienctist needs to master in order to do her job effectively. Even the term “data science” can be somewhat nebulous, and as the field gains popularity it seems to lose definition. To help those new […]
Editor’s note: This post was written as part of a collaboration with data.world, a site for sharing and hosting data. Authors Shannon Peifer and Gabriela Swider are on the data.world team. Finding the right data can be difficult. And even once you have it, how do you collaborate with others to make sense of it? […]
Editor’s note: This post was written as part of a collaboration with SwitchUp, an online platform for researching and reviewing technology learning programs. Erica Freedman is a Content and Client Services Specialist at SwitchUp. Data Science is a rapidly growing industry. From university programs to week-long cohorts, it can be difficult to decide where to […]