You’ll probably have heard the term many times before. Nowadays it’s hard to come by an article on trending technologies (particularly information-related ones) without some reference to a Machine Learning in it. It seems that suddenly the world has become aware of the immense practical aspects of this evergreen field of Artificial Intelligence that constitutes the heart of data science. Machine learning is a computer’s way of learning from data and examples. It’s a type of machine intelligence, and will be among one of the technological disruptions of the coming years.
Many people see Machine Learning as a high-tech field that only the selected few can understand and practice, while others see it as merely glorified programming. As in many other cases, the truth lies somewhere in between. Machine Learning is not an esoteric discipline as it once was, in its earlier stages of development. It has grown very popular and therefore accessible, with a variety of open-source libraries in R, Python, and other programming languages. Also, its theory has become more structured and easier to understand, while most of the methods it entails have been tested over many years in a variety of datasets. Still, Machine Learning is not trivial and involves more than just writing code. It requires a lot of work to learn, though doing a specialized degree on it is unnecessary, unless you are really into research. Yet, despite the variety of literature out there, it is very hard to learn it properly on your own.
Machine Learning is used widely today for all kinds of tasks, from churn prediction in large companies, to web search, to medical diagnostics, to robotics (this in particular would have been next to impossible without Machine Learning). It’s hard to find a field that cannot benefit from Machine Learning in one way or another. The reason is simple: data abundance. With all kinds of data floating around, it is natural to gather meaningful combinations of it (creating what is known in Machine Learning as “features”), and use them to make useful predictions about the world, particularly aspects of it that pose some value to us. You can think of it as cooking skills in an environment where there is easy access to a large variety of cooking ingredients and everyone there has quite an appetite. What’s more, Machine Learning is getting better all the time, so it is quite unlikely that it will run out of methods that can turn data into valuable information more efficiently and more effectively.
But don’t take anyone’s word for all this. Look around at Machine Learning practitioners and their lives. Few of them are sitting idle. Most of them, particularly the more adept ones, earn a decent living and often win prizes at Machine Learning competitions. What’s even more important is that they usually have a good time doing what they do, because this is a line of work which is both manageable and challenging at the same time. If you are into programming, it makes it so much more interesting as it allows for the development of better quality applications, some of which can be marketed as intelligent or predictive applications.
As mentioned earlier, Machine Learning takes some effort to learn, but the whole process becomes much easier when it is done in a systematic and engaging way, with an experienced professional as your guide. This is why School of Data Science has created a series of courses, the Machine Learning Smackdown, that provide you all the help you need to learn Machine Learning properly, gaining some hands-on experience in the process. Completing the Machine Learning Smackdown will turn you into a competent Machine Learning practitioner, able to tackle real-world challenges, turning big data into big insights and big opportunities. Are you ready?
Register now for the 2nd Round, a five day bootcamp starting on the 21st of July to learn basic building blocks of practical Machine Learning using Python Scikit-Learn.
Machine Learning is the best way to exploit the opportunity presented by Big Data.read more
By Ali Syed and Dr. Zacharias Voulgaris
Big data is declared as the next big thing – one of the strategic resource to remain relevant, value focused and competitive in the digital economy. To truly take advantage of this opportunity organizations must become connected enterprises, rapidly obtaining and reacting to the intelligent predictions and relevant insights obtained by continuous exploration and analysis of data. The increasing importance of data and everyone’s desire to become a data driven organization has boosted the demand for people with the skills to analyze, interpret and predict from data.
As an executive you fully appreciate the significance of data to compete in the digital age, value it can bring to your organization and you are actively exploring opportunities to leverage the value of big data. You are aware of this fact that there are certain professionals, called data scientists, that can help make this happen. However, you are also cognizant of the fact that data scientists are a rare resource and this data scientist shortage won’t go away soon, because organizations need them more than ever to deal with the complex and massively data driven digital world.
“Data by itself is meaningless. It’s the skill of the data scientist that makes the difference”
Dr. Josh Sullivan
Some additional research may help you realize that this isn’t a game of musical chairs for employers, as data scientists also come about through talent development and following a team based approach to address data science skills shortage. This option, that is not so well-known, is definitely worth exploring more, which is what we shall attempt in this article.
Organizations are struggling to find individuals who possess all of the skills and abilities to think and work like data scientists. Developing your own people by helping them learn data science is an excellent strategy, considering that the most effective set-up for tackling big data problems is through a team of data scientists. Data Science is a team sport so why not use a team based approach to address the problem of Data Scientist shortage? Contrary to what many people think (or fear, rather) training your employees reduces the chances of them jumping ship. According to recent market research, 92% of employees who learned new skills in an organization decided to maintain their position. And why wouldn’t they? If the person who hired you shows that he/she cares about your skill-set and makes sure that you become a more valuable asset, then wouldn’t you want to stay close to that person and express your gratitude towards them through your work?
Whether your employees will be willing to learn or not about data science is something that you may find concerning. However, most of them are bound to be quite keen to learn new things and data science is one of the most appealing things out there nowadays, so you’ll find little resistance from them. Of course not everyone will be interested in learning the ropes of data science but you don’t need all of them take up this role anyway. If your approach to data science is team based then just a few data scientists are enough to successfully solve a complex business challenge or to create new business opportunity. To this end, this newfound team of data scientists can then collaborate with business analysts, project managers, systems architects, developers, web designers, product and subject matter experts engineers, etc.
Training your own people (particularly cross-training them) is a win-win situation that can have clear advantages for data science endeavors. This strategy brings about more agility in your workforce and enables your projects to be more flexible in their execution. By cross-training your employees you allow them to understand each other better as they have a wider frame of reference, enabling them to have a better synergy in their work. All this is very useful for data science projects in particular, as the problems being tackled by data scientists involve a more inter-disciplinary approach, making collaboration more challenging. Cross-training resolves all that, plus you get more flexibility in your project as a bonus, since you no longer need to rely on a few experts who may not always be available.
“As an executive recruiter specializing in quantitative recruiting, I work with clients continuously looking to find the unicorn that can do it all – the algorithm development, the data munging, building visualization and BI tools, scaling, and turning all this into enterprise wide adaptation. They are out there, but it could turn into a long and frustrating search. It takes a team and a solid commitment from the top.” Linda Burtch
Naturally, seasoned data scientists are also worthwhile as an investment, albeit a risky one. If you have a large organization, in particular, hiring a more experienced data scientist may be a big boost in your dealing with big data. Also, their more in-depth understanding of the data science field may bring about more useful insights about the value that can be derived from the available data. Of course, his/her positive effect will be maximized if you have some people dedicated to working on the same projects to facilitate the development of the data products involved. Regardless of all this, however, a good data scientist is hard to find and you’ll have to rely mainly on recruiters for this task. Also, even if she/he is the best data scientist around, some training to get acquainted with your domain will be inevitable. Finally, he/she may leave at any time (especially if he/she is quite experienced) as there is bound to be some other organization out there that’s willing to offer a more appealing package for data science expertise.
There are several reasons why having a mix of in-house trained and hired data scientists is the best way to go about it. Having both types in your organization will allow for a stronger data science team, combining the experience and know-how of the hired data scientist with the versatility and other merits of the in-house trained data scientists. Moreover, a hired data scientist in your ranks can help your employees learn the practical aspects of data science faster and more effectively.
Training your employees to be like the aforementioned seasoned data scientists is not an easy task, however, although it’s not too challenging either, especially today. The reason for that is that there is a large variety of resources that your employees can use to learn the ins and outs of the field. The most efficient of these resources are without a doubt data science courses, practical use case based project work and mentoring. One great place that offers data science education and talent development services is Persontyle.
Summing up, the data available is a great asset but it’s completely useless without the right people, working together as a team, turning this data into actionable information and insight. A healthy mix of in-house trained data scientists and hiring external ones is probably the most effective strategy. This can allow the formation of a flexible data science team that can benefit from both the experience of the seasoned data scientists and the various benefits that the in-house trained data scientists yield . One way to make the latter a feasible and effective option is through the use of Persontyle’s “Data Science Talent Strategy” workshop.read more