Eight most in-demand big data skills in 2019
By Kelly Dent
It’s no secret that today’s tech market is dominated by all things big data and cloud computing—two areas that often go hand in hand and see a crossover in skill sets. Whether you’re taking the first steps in your tech career or just want to sharpen your skills, here’s a quick look at the most important big data skills to have on your resume when you’re looking to build your career in AWS.
Most in-demand big data skills
Core languages worth investing your time and money in include Python, Java, and C++. Of course, it’s not necessary (or possible) to learn every programming language out there, but the more relevant ones you know, the better your career prospects get.
Most open-source big data offerings are written in Java, so if you know that language well you’ll already have the kind of technical mindset necessary to tackle Hadoop For Spark. Spark uses Scala because the language was created with functional programming and immutability in mind, making it compatible with behavior-oriented Spark APIs and its RDDs.
Python is hugely popular for text analytics and creates a solid foundation for big data support. If you work with or develop big data platforms, you’ll no doubt be involved with scripting, so it’s useful to have at least one major scripting language in your back pocket, and Python is one of the strongest contenders.
Machine learning and AI
The widening digital skills gaps means that organizations all over the world are in a never-ending race to snap up big data professionals with machine learning and AI skills. Neural networks, reinforcement learning, adversarial learning, decision trees, logistic regression, supervised machine learning—the list goes on and on, and the more you can offer, the more valuable an asset you’ll be to any progressive tech-focused employer today.
Quantitative analysis is a huge part of day-to-day life in big data because it’s all about the numbers. A strong background in math and statistics will put you ahead of the pack, and getting acquainted with powerful tools like SPSS and R will make you an even more attractive hire.
Having a background in mathematics—especially calculus and linear algebra—will give you a great foundation for understanding the probability, statistics, and algorithms involved in a big data job.
Advances in tech over the last five years have taken data mining to staggering new heights. Big data pros with serious data mining experience are in high demand across the tech landscape, so invest some time in building out your data mining kit with industry favorites like Rapid Miner, KNIME, or Apache Mahout.
Having a naturally analytical mind will take you a long way in this line of work. Whether you’re a naturally gifted analyst or not, it’ll take continuous practice to hone those skills and become a big data bigshot. There are countless ways to sharpen your analytical thinking, like solving puzzles, playing chess, or enjoying videogames that challenge your problem-solving skills. The key is consistency.
SQL and NoSQL databases
SQL forms the bedrock of the big data movement and is central to Hadoop Scala warehouses.
Distributed NoSQL databases like MongoDB are fast-replacing their more traditional SQL counterparts, including the likes of DB2 and Oracle, allowing for far more efficient storage and access capabilities.
NoSQL works in perfect harmony with Hadoop in terms of its data processing abilities, and having NoSQL skills gives you access to a whole range of job opportunities anywhere in the world. Simply put: don’t just get to know NoSQL databases, master them.
Data Structure and Algorithms
These are fundamental skills that you’ll build your career on when it comes to big data or data science, so make sure you’ve got them polished to perfection as early in the game as possible. By learning about data structures and algorithms you’ll become familiar with data types (stack, queues, and bags), sorting algorithms (quicksort, merge shot, heapsort), and data structures (binary search trees, red-back trees, hash tables)—essentially the bread and butter of any big data role today.
Even as a junior data scientist, you’ll need to know your way around unstructured data. This is undefined content which doesn’t have a place in your database tables, for example, videos, blogs, customer feedback, audio, and social media posts. Because this data isn’t streamlined, sorting it is notoriously complex, earning it the nickname ‘dark analytics’.
Unstructured data is pretty valuable because it reveals insights that can be vital to the decision-making process. Any data scientist worth their salt needs to be able not only to understand but manipulate this kind of data across various platforms.
Interpretation and data visualization
Without analyzing data and deriving insights, a business can’t function effectively. As a big data professional, it’s essential that you have a strong understanding of the business environment and domain your employer operates in.
The ability to visualize and interpret data is an essential big data skill that brings creativity and science together. Data visualization and analysis requires a lot of precise science and mathematics but also calls for inventiveness, imagination, and a natural curiosity.
AWS Big Data certification at a glance
AWS Certified Big Data – Specialty is the certification to have if you’re a cloud professional looking to earn your big data stripes. The exam is designed to test your ability to carry out high-level analysis and confirms your capacity to use the provider’s core big data offering to design and maintain data structures, and employ a range of tools for data analysis automation.
- Multiple choice, multiple answer
- 170 minutes
- $300 registration fee
- AWS Certified Cloud Practitioner or a current Associate-level certification: AWS Certified Solutions Architect (Associate), AWS Certified Developer (Associate) or AWS Certified SysOps Administrator (Associate) recommended
- At least five years’ experience in data analytics
- Experience using AWS Big Data services and detailed knowledge of their place across the data life cycle
- Experience designing scalable, cost-effective architecture for data processing