3 Ways You Can Break Into the Big Data Industry
According to a profile by Quantium, by 2020, 1.7 megabytes of data will be created every second, for every person on Earth. The amount of data that is currently available, not to mention the data we can anticipate in the future, is exponentially increasing. So, what happens with all that data? What exactly do companies do with the billions of searches and information conducted on a daily basis? This is where Data Analysts, Data Scientists, and Business Analysts come into play.
How can you break into the big data industry? We’ve put together three key steps you might find helpful for fueling your data-driven career.
Learn basic coding skills
Big data is still globally evolving, meaning there are no set structures or exact methodologies to go about capturing value. Computers support data processing by allowing experts to create sophisticated and complex algorithms- rules to facilitate problem-solving and huge bulks of calculations. A proficient data analyst needs to be comfortable coding certain languages.
To start off, begin with programming languages, like R, Python, Java, C++, and SQL/NoSQL. The first three are usually the minimum necessary requirement for most entry-level jobs. Get comfortable with these, as they will be the backbones for many of your assignments. It also doesn’t hurt to add supplementary skills to your coding capabilities; familiarize your way around Excel, read up on Data Visualization tools (Tableau, Power BI, D3.js), Machine Learning, Data Structures (Hash Tables, Arrays, Trees), and Algorithms (Quick Sort, Merge Sort, Heap Sort).
Build a complete portfolio
In any industry, you will find that having a diverse portfolio, showcasing your work is crucial. The same goes for big data. Once you feel competent with the skills mentioned above, put them into practice. Test and run experiments with the programs and languages you learn to use.
These kinds of personal projects will provide insight into fields of interest you like more so than others, as well as how implementations and results are generated in real-world scenarios. Keep in mind, your idea does not have to be perfect. Don’t wait to start a project because you are looking to achieve a perfect result; building and analyzing a piece of data (even if doesn’t work), shows employers your determination and adaptability to whatever comes your way.
Follow relevant blogs or threads
How you learn to use data is paramount, but when you’re first starting out this can be difficult to grasp. A great way to see how other prominent data influencers are thinking about and applying concepts and skills is to stay-up-date on their publications. Here is a collection of six big data blogs and speakers you should bookmark:
- Fast Forwards Labs: A machine intelligence research company, founded and led by Hilary Mason.
- Edwin Chen: Founder/CEO at Hybrid. Edwin works on AI, human computation, and data. His blog entries are great resources with practical examples.
- Hunch.net: John Langford, a machine learning research scientist and principal developer of Vowpal Wabbit, authors this blog where he touches upon data theory and practice.
- R-bloggers: Referring to the R programming language, this blog delivers daily news and tutorials by over 750 different bloggers.
- KDnuggets™: A leading site on Business Analytics, Big Data, Data Science, and Machine Learning, who also posts job listings from around the world.
- FiveThirtyEight: A blog by Nate Silver, a statistician, and writer, who correctly predicted the 2012 United States presidential election
The University of Minnesota Data Analytics and Visualization Boot Camp provides students with on-site career services and dedicated in-classroom instruction, to help you stand out and build a meaningful portfolio. Working with our career services team can make all the difference in landing the data science job you’ve dreamed about. To learn more about the U of M Data Analytics and Visualization Boot Camp, check out our data curriculum or give us a call directly at (612) 284-5883.