It’s no secret that big data is now a big hit. And that means that the rush is on to find talented individuals able to work with this new data paradigm. Over the last five years, the industry has seen demand for data analysts more than quadruple. Big data is one of the most lucrative sectors of the entire economy. And that means that there are significant rewards to be had by job seekers with the right skills for the field.
But hold on a sec. What are those skills? Most of us have a rough idea about what goes into becoming a doctor or an accountant. But what about somebody who wants to succeed in the big data industry? What do they need to do to succeed? Let’s find
Creativity And Problem Solving
Before diving into the specifics, let’s start with a general skill that big data analysts need. You might think that people working in big data would need to be clued up on technical procedures. And that’s true. But big data is not something that is static. The technologies surrounding it are changing all the time. And that means that people in the sector need to be able to think creatively and address new problems over time. The people who do best in the industry are those who have the determination and can find solutions to new challenges. Rarely are companies looking for people with off the shelf skills. They want individuals who can address their specific problems and do something creative.
Apache Hadoop
Apache Hadoop has now been around for about two decades. But thanks to big data, it’s never been more relevant. Over the last few years, Hadoop has gone from strength to strength, thanks to its importance to big data. Knowing how to use Hadoop is essential if you’re going to have a successful career in big data. Knowledge of Hadoop is one of the first things that employers look for on a CV. They want to know whether you can cajole Hadoop into doing things that are relevant to business.
Hadoop has been important in the area of data analytics for some time now. But it’s thanks to the fact that it allows fast data transfer between nodes that makes it so valuable today. Big data relies on stability. And Hadoop allows data transfers to continue, even if nodes go down. As you can imagine, Hadoop is a complicated beast. And that means that companies need technicians who really understand how it works. And they’re willing to pay a lot for them.
Machine Learning
If you want to succeed in the big data industry, you have to have an understanding of deep learning. Deep learning is one of the most significant applications of big data today. Machine intelligence, as it stands, requires that you feed it enormous amounts of data. Machines, in essence, are able to learn, just from the raw data input. How they use these data are driving a whole new class of potential applications. Already, some of the biggest companies in the world are experimenting with deep learning. Just look at what Apple is trying to achieve with their chatbot, Viv and what Tesla is doing with its cars. It should be noted that Tesla’s autonomous systems don’t rely on expensive scanning lasers. They rely on dirt cheap cameras, backed up by deep learning algorithms. The machine learning program in a Tesla is actually interpreting its environment from the raw pixels. In other words, it’s doing what we do.
Big data analysts need to know about deep learning applications like these. But they also need to know the applications that are relevant to other businesses. Take the ability to interpret a document, for instance. Right now, teams of junior lawyers spend hours going through court papers and classifying them. It’s a hugely laborious task. And junior lawyers tend to make a lot of mistakes. Deep learning can organize documents in a fraction of the time that it takes a junior lawyer. And that puts it in high demand.
Another application is in personalisation. Retailers and advertisers want to be able to make their marketing customer-specific. So right now there’s demand for people who can use machine learning to learn about customer habits and preferences. Once an algorithm knows a person, it’s possible to personalize their experience with a company.
Hence the opportunities in big data going forward will be huge. Many expect this to be the primary driver of growth in the economy over the next couple of decades. And that’s why big data analysts should have an in-depth knowledge of machine learning and AI.
Data Visualization
If there’s one thing that’s true about big data, it’s that it can be difficult to interpret. There are thousands of different techniques that you can apply to the data to gather meaning. But sometimes the best option is just to eyeball it and see what meaning you can extract.
Data analysts who are able to look at data, form hypotheses and test them are in demand. There’s always something hidden in the data that is useful to a firm. Tools like Tableau help users identify patterns in samples of data. And so having some visualization skills under your belt is a must if you want to succeed. There is some great info by Simplilearn on the subject.
Statistical Analytical Skills
Statistics is the crux of big data analytics and machine learning. And so having quantitative skills is essential. Machine learning works by using data to find the most likely outcomes. And that means that it relies on the sort of statistical tools many of us learn in college. Having an understanding of how regression works is a good start. But also understanding how binary choice models work is also important. So much of machine learning uses methods similar to those in logistic regression.
It used to be the case that the only people really bothered about this sort of analysis were the financial firms. But now that big data is everywhere, companies in other industries are seeking to use it to their advantage. If you have a quantitative background, it’s going to put you streets ahead of the competition. And it’s going to make the industry a whole lot more accessible. With statistics, you’ll be more adaptable and won’t have to fit into any particular niche.
Data Mining Skills
Finding little gems in the data is what data mining is all about. Companies want people with the skills to find these critical tidbits and exploit them. Perhaps they’re looking for a pattern of behavior in their customers. Or maybe they’re trying to optimize their website to get more conversions. Whatever it is, the truth can usually be found in data mining. Again, you’ll be using statistical tools to interpret the data. And that means that you’ll need an understanding of how regression works. And which type of regression is most appropriate for the data mining task at hand.
General Programming Language Expertise
This is another one of those skills that just makes your life easier as a person looking for a career in big data. Right now, there are a lot of people in the industry who have programming skills. And there are a lot of people who have analytic skills. But there’s aren’t an awful lot who have both. Knowing how to use statistical programs and programming languages will make you particularly valuable. With knowledge of both of these, you’ll be able to bridge the gap between analysis and programming. And that puts you in the right place to be able to do things like app development.
According to industry, there’s been a bigger increase in demand for people with these skills than general analytics. And the result has been that analyst-programmers can charge pretty much whatever they want. They can also work wherever they want. They can flip between working at established end user companies and startups. They have the skills to work in both environments.
NoSQL
NoSQL and Hadoop work in close cooperation together. NoSQL is a repository for all the big datasets you need to crunch. It stores data in a different way to tradition databases in the sense that it is not tabular. Hadoop is the program that interprets this non-tabular data. And so to be a success in the big data industry, it’s essential that you have knowledge of both. In today’s world of big data, very little data is in traditional tabular format. Large companies like Amazon, Facebook, and Google, realized this early on. They recognized that things like clicks on an ad were hard to store in a two-dimensional grid. That means that they had to improve their data storage tools.
NoSQL isn’t new. But people who know how to use it are in big demand today, simply because of the expansion of big data applications. These applications rely on large datasets using NoSQL. And so a knowledge of these databases is essential for future success in the industry.
