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Our Top 10 Most-Read Blog Posts of 2019

By Emily Wilson • December 20, 2019

Photo by NordWood Themes on Unsplash

Throughout the year, we post blog content including alumni stories, data science insights from our Sr. Data Scientists, guest posts, and much more. Below, check out our top 10 most-read posts of 2019. We hope you enjoy them again or for the first time, and we look forward to producing much more data science content in 2020.

1. How to Learn Data Science Quickly and Effectively
There’s no shortage of discussion in the data science community about where to best learn data science. However, there’s precious little discussion of a topic that’s arguably more important: how to learn data science. In this guest post from Dataquest, read the three crucial things you need to do to learn data science quickly and effectively. 

2. Three Ways to Improve Your Data Science Communication Skills
Data scientists are often described as hybrids: part statistician, part computer scientist; part analyst, part strategist. But while we focus on the myriad technical skills that a data scientist should possess, we often overlook one of the foundational skills (without which the whole edifice falls apart): communication skills. In this post, read three tips for improving your professional communication skills.

3. Financial Applications of Natural Language Processing
In an increasingly competitive financial landscape, gaining an informational edge is vital to constructing and maintaining a superior equity portfolio. Alternative data sources including satellite images, sensor data from IoT devices, text, and video are all becoming increasingly important sources of insight for active equity strategies. In this post, explore some of the common Natural Language Processing (NLP) techniques that are used in asset management.

4. Scoping a Data Science Project
Earlier this year, we published a blog post on how to foster a data literate and empowered workforce, which allows your data science team to then work on projects rather than ad hoc analyses. In this follow-up post, learn how to carefully scope those data science projects for maximum impact and benefit.

5. Via Non-Traditional Path, Grad Alexandra Fox Sets & Meets Data Science Goals
If such a thing as a “traditional path” to data science exists, bootcamp graduate Alexandra Fox didn’t take it. Instead, she forged her own route, complete with the goal of getting a job as a data scientist immediately after graduation. Find out how she did it and what she's up to now. 

6. A Beginner's Guide to Object Detection
In this post, Metis Sr. Data Scientist Kimberly Fessell covers the basics of object detection: what it is, various approaches to it, the measurements used to judge its results, along with a few important considerations of modern object detection.

7. How a Former Software Engineer's Dream of Working in Machine Learning a Reality
After 20+ years of working as a senior-level software engineer for companies like Goldman Sachs and Bank of America, Emy Parparita was looking for a change. Read how the bootcamp helped him transition to his current role as a Machine Learning Engineer at Quora.

8. The Impact Hypothesis: The Key to Transformative Data Science
Too often we assume that good data science translates to effective data science, even though it's untrue. This assumption has killed many would-be successful projects. In this post, Sr. Data Scientist Kerstin Frailey introduces the impact hypothesis, or, how to critically scope and communicate how a project will drive impact. Doing this will transform the way data science drives your business.

9. Finance, Technology, and Human Behavior: The Story of One Grad's Ideal Role
Bootcamp graduate Laura Chen has always been deeply curious about human behavior. Read how she landed a job that allows her to explore that interest while also working closely with her passions for finance and technology.

10. Playbook: Hiring Data Scientists
Data scientists are in high demand, particularly as data changes the way many companies do business. But it can be difficult to attract (and retain) data science talent in a job market that is growing at an unprecedented rate. In this post, former Metis Sr. Data Scientist Brendan Herger draws on years of experience to share how to effectively and efficiently hire data scientists and build out your team.

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