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How to create a portfolio of data science projects that will actually help you get a job as a data analyst or data scientist.

Hezekiah was looking for specialized data science skills his university didn't offer. Dataquest fit the bill, and offered courses that felt as high-quality as Tufts'.

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 […]

What can you do to make your data science job applications stand out and get hired as a data scientist? Here are some high- and low-risk strategies.

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, […]

In this post, we’ll learn to create an online survey and how to prevent some common mistakes made in surveys. We’ll cover all steps of the survey process, including: Selecting a population Sampling methods Making a data analysis plan Writing good questions Distribution options Data Scientists know that even the slickest code, the best data […]

When it comes to relative newcomers in the Data Science field, there aren’t many out there doing better than Alyssa Columbus. Although she just graduated from college earlier this year, she already has a full-time data scientist role at Pacific Life, a laundry list of conference and symposium speaking engagements, and has founded a local […]

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 […]

Tips for finding unique, interesting data sets that haven't already been analyzed by tons of other data scientists.