Planning to build your tech career in 2017? You may want to consider a few high-demand fields that are positioned to take off this year. We’re profiling careers to watch, along with everything you need to get started.
If you are comparing tech careers, you’ve probably heard some of the hype surrounding Data Science jobs. Glassdoor ranked Data Scientist as the best job in America in 2016, and Harvard Business Review went so far as to name Data Scientist “The Sexiest Job of the 21st Century”.
Sure, the hype might be a bit over-the-top, but there’s no question that data science job growth isn’t slowing down anytime soon. Companies are now working with more data than ever, and need employees with the training necessary to make sense of the numbers. Thanks to demand, Data Scientists enjoy high earning potential, a wealth of career opportunities, and the large number of job openings.
It’s sometimes hard to know the best way to learn data science because the term varies widely. Many companies are seeking different skillsets, expertise, and experience levels. For example, you could be working for a B2C company that is looking to better understand their customer base, or you might be working for a company that offers data as the product. When you begin your data science training, it’s important to have a clear idea of how you would like to use your skills.
Regardless of your path, you will likely need to demonstrate the following skills to land a job:
Programming Languages: A statistical programming language like R or Python, and a database querying language like SQL.
Basic Statistics: At least a basic understanding of statistics is crucial. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc.
Machine Learning: This is especially important if you plan to work at a large company, or a company that offers data as a product. You can use R or Python libraries for many machine learning techniques, but it’s still an important concept to understand at a high level.
1. Learn The Basics. Before you dive into a bootcamp, make sure you’ve mastered the basics. Check out one of these free online courses to get started:
2. Research Data Science Bootcamps. Once you’ve learned the basics, a Data Science bootcamp can help you fill any gaps in your knowledge and get you ready for an entry-level data science job.
3. Choose A Career Path.A Data Science Team consist of multiple roles with slightly different skillsets, that all work together to tackle a problem. Common roles are:
Data Scientist The data scientist uses a range of tools to take a project from start to finish. As a Data Scientist, you’ll need to master the ability to manage and analyze raw data, and share insights in a compelling way. Skills include predictive modeling, Python, SQL, R, and distributed computing. Medium Data Scientist Salary: $113,436.
Data Engineer: A great career transition for someone with a background in software engineering. The Data Engineer’s secret weapon is fluency in both statistical programming languages and languages used in web development. Skills include database systems, database modeling, and languages like SQL, R, Matlab, and Python. Medium Data Engineer Salary: $95,526.
Data Analyst: The Data Analyst is the key liaison between the data team and the rest of the company. During your Data Analyst training, you’ll need both technical data science chops as well as business and communications experience. Skills include programs like R. Python, and SQL, statistics and business communications. Medium Data Analyst Salary: $60,476
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