This blog post originally appeared on UpScored.com, a job search platform that combines sophisticated technology and human intelligence. UpScored gets to know your background, preferences, and career interests to match you to your best job prospects.
Our guest bloggers this week are CEO Elise Runde Voss and CTO Dan Elbaz, co-founders of UpScored.
What are the biggest skill gaps among technical roles?
We’ve been fielding a lot of questions from our users on what skills are in highest demand. Luckily, we’re not short on data. So, we decided to explore the topic in more depth.
Before we dig into our findings, we’ll tell you how we got there. We based our research on approximately 500,000 resumes and over 13,000 open job descriptions with the goal of analyzing the top "skill gaps" among technical roles. To offer more clarity, we recognize “skill gaps” as skill keywords that appear proportionally more frequently in job descriptions than in candidate resumes. The data was scaled across 2,500 keys to identify the skills with the biggest differences between demand (jobs) and supply (resumes).
Okay, really, what is the trend?
Demand for skills associated with NoSQL technologies has clearly outpaced supply. "NoSQL,” "Redis," "Hadoop," "Cassandra," and "MongoDB" were among the top 15 scarcest skills. Interestingly, relational database SQL technologies such as “Oracle,” “MySQL,” and “SQL Server” are at a slight surplus. For example, we’re seeing skills like “MySQL” appear more frequently in resumes than in actual job descriptions. Intuitively, this may not come as a huge surprise: as companies are collecting more and more data, it’s likely many are switching to newer database technologies over relational database systems. They will need employees who can manage these systems.
Times are changing - and quickly! As Business Insider tells us, digital data is growing at an exponential rate, doubling every two years. Thus, there's also a major skill gap in the data science and analytics fields - where the objective is to make sense out of all this data. As data science becomes integral to all industries, we expect all departments within all companies to start requiring skills like "Machine Learning," "Data Science," "Statistical Modeling," and "Data Mining." We’re already seeing the trend...
As candidates, how do we keep up?
1. Degrees and Bootcamps: An increasing number of universities are offering degrees and specializations that focus on the relatively nascent field of data science and analytics. For programs with shorter commitments, data bootcamps are a great way to get a solid foundation and practical experience in data science. We recommend:
2. Online Courses: MOOC's (Massive Open Online Courses) are becoming popular. There's no shortage of courses focusing on topics such as big data, data mining, and machine learning. We recommend:
- Coursera: Introduction to Data Science
- Udacity: Intro to Hadoop and MapReduce
- edX: Data Science and Machine Learning Essentials
3. Just Do It: If you already have some technical background, the best way to learn is to get your hands dirty. There are plenty of open data sources and packages with thorough documentation out there to get you started. Whether it’s forecasting real estate prices or building a recommendation engine for movies, choose a project that interests you. We recommend:
This blog post is from our friends at UpScored.