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The Top Skills You'll Need to Become a Data Scientist

The online world isn’t as simple as we’ve thought it to be. Behind the seemingly quiet and vast space of nothingness, huge amounts of data are uploaded and downloaded in fractions of a second. Data science does not only keeps track of these numbers, but also attempts to analyze and organize them. Algorithms are created to keep tabs on searches in search engines, analyze user data preference, and so on and so forth.

The demand for qualified data scientists have become very high. A recent Bloomberg article  predicted that there will be a shortage of data scientists in the US by the year 2018. Current demand is so high that qualified data scientists can command extremely high salaries, and virtually choose the highest bidder. There is also a big need for solid data analysis skills from countries like Switzerland, UK, Canada, UK, India and China, just to name a few.  The most coveted skills include research, business analytics, data mining, statistics, design knowledge as well as machine learning.

A Shortage of Talent

According to research, around two-thirds of data science job openings will remain open due to a serious shortage of qualified data scientists. While not everyone can all be experienced and skilled in all aspects, there is a way for an ordinary IT person to acquire more skills and become a data scientist. The advantage of doing this is that someone with an IT background will already be familiar with the basic skills and knowledge base needed in data science. Higher learning and adding new skills and experience is imperative; however, this is not impossible and can even be done in as little as a few short months depending on the person’s background.

If you’re someone who’s interested in becoming a data scientist, here are the top skills that you will need to acquire:

1. A Firm Grasp of Algorithms

The world wide web runs on algorithms. An efficient data scientist needs to know when and how to use the right machine-learning algorithms. This is not an easy skill to learn and master. To learn the basics of algorithms you must become familiar with its classification, clustering, and regression, among other things.  There’s no shortcut to hitting the books and attending classes or lectures if you are given the opportunity to learn in a classroom set up.  Learning the Python language will allow you to conduct experiments by yourself. Alternatively, you can also check out the website Kaggle.

Joining forum and discussion groups around the topic of algorithms, coding competitions and big data can help you immensely if you want to become a data scientist. Ultimately, what matters is to get the job done in the fastest and most economical way possible. The concept of big data associated with data scientists not only imply that the large amounts of data exist, but that the data needs to be manipulated in a different way because normal algorithms, programs, and even languages do not scale well with the huge amount of today’s data.

Kaggle, and competitions there, offer a democratization of the computer science, as well as data science. The notion that what works best is what should be used is nothing new. What is new is that novel approaches are tested with huge data and compared with existing approaches. Whatever works or whatever works better is the mantra of these competitions. Like all skills, following the competitions online for 6 to 8 months will give you a lot of learning experiences.

2. Improving on the Algorithms

Understanding algorithms is one thing. Using them in the real world is another. Again, the problem with algorithms is that they might not scale well. Getting your algorithm in use with huge amounts of data will show where the envelope lies. Pushing that envelope with the same algorithm does not always work. It’s like the problem with moving between A and B in the dark with a wall in between. What works is improved further until you can get it optimized along the dark path. However, if the wall goes higher or wider, the old method might no longer yield the best results. You have to find a better solution. Machine learning or even hard coded AI might not scale well when the wall gets bigger.

3. Show and Tell

The art of mastering data science does not lie in the theory, but in its practical application. The real world has no practical use for a whiteboard fill of impressive equations that blow away people’s minds. What the industry needs is hard reason codes that should first and foremost be solution based. Great features plus an algorithm that sums up to reason codes are essential for interpreting data accurately.

Operational considerations should also carry some weight. False positives should be taken into account and also included on the metrics. Both of these scenarios can upset the standard model metrics. Finally, your codes should be applicable to real life. It should work online and offline, even in seemingly non-related industries such as schools and hospitals. Algorithms can and should be applicable to everyday life.


4. Go for the Win-Win Solution

Even if you are armed with the best algorithm and produce the greatest features, it must be applicable to real-life businesses or problems. This is something that experts at are focused on. They make sure that data scientists and other IT experts work hand in hand with the end users or stakeholders. These are the people who will either directly benefit from the data gathered and analyzed, or it is their data that needs to be sorted and made sense of. 

To make sure that you arrive at a solution that’s win-win, you must be able to interact and get the opinions of a core team, which is made up of: technical experts, upper management, and the business stakeholders.  The first one will undoubtedly be able to offer a lot of input and experience as you develop the features of your algorithm. They will also be the ones interpreting the data later on, so it is best to get them involved even in the early stages.

The stakeholders, on the other hand, should be made aware of what the algorithm will entail in terms of manpower and cost. Finally, the upper management will be there to support the implementation of the algorithms as well as other improvements to it in the future. They must be able to understand how it works, and what it can and cannot do.

Working as a data scientist means long hours of work and perseverance. It also means working one’s way to the top. No matter how high the demand is, there is no compensating for skill and experience. The four skills outlined above will help an IT or tech person make the transition to become a data scientist almost seamlessly.  

Author bio: Anna Garland is community manager at offshore development company called ignite outsourcing. In this article she has explained about the most important skills that are needed to become a data scientist.

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