In a world where important decisions are increasingly data driven, the demand for data scientists is higher than ever. A large number of information technology (IT) professionals and enthusiasts are switching careers and choosing to advance as data scientists. The phenomenon is easily explicable: Data scientists enjoy high salaries while doing work that has a major impact on business outcomes.

Some data scientists chose their career path early during their studies, and others had to complement their IT and software development skills with statistics, mathematics and linear algebra. Either way, training data scientists to be capable of handling complex problems and datasets is tricky, and it’s crucial to provide adequate help in terms of preparing them for the real world. It’s time to dive into the challenge of training data scientists and advancing their skills within their companies.

What Is the Core of Data Science?

While many standardized frameworks and libraries might hide the dirty work from newbie data scientists, the core of machine learning and deep learning models is highly mathematical. In other words, everyone can apply certain artificial intelligence (AI) models to their problem without diving in too deep, but making adjustments to improve performance won’t be as easy.

In general, data science can be understood as a mix of mathematics, computer science, statistics and computer engineering. On top of all that, data scientists need to be able to fundamentally understand the problem they are resolving in order to translate it into code. Training for data science roles might include additional skills, such as high-performance computing, data mining or back-end engineering.

How to Train For Data Science Roles

Data science roles differ from company to company, and the company’s business will dictate the skills you will need to succeed. Additionally, companies generally work with different types of data. While data science is sometimes traditionally referred to as making predictions from tables and databases, deep learning advancements lead to an explosion of solutions where insights and information are extracted from audio, video and text.

Here are several ways to train employees for data science roles:

  1. Learn to enjoy data:

Without motivation, employees might be overwhelmed with mathematical terms, different algorithms and metrics that sound confusing for newbies. However, employees need to understand that their work has the potential to make a huge impact. Data science models they develop will be used to make decisions, improve the company’s financials or automate repetitive processes to save time and money. Data tells a story, and it’s important to fall in love in reading and interpreting what it has to say.

  1. Learn by doing:

As mentioned before, data science is popular, and many people are switching to this field. There are numerous courses from commercial sites and universities you can take to get started, or even specialize in some of the more advanced topics. However, these courses hide something from you: 90% of data science is not about the models themselves. That’s right, actual data science work is, of data scientists by CrowdFlower, a data enrichment platform provider for data scientists, more like this:

  • 19% – collecting the actual data
  • 60% – cleaning, organizing, and transforming the dataset
  • 9% – mining the data to spot patterns
  • 3% – building the actual training sets
  • 4% – refining the AI models
  • 5% – miscellaneous

As can be observed, there’s plenty of work involved before getting to the models and algorithms. However, when you take an online course, most data you’ll be using is already neatly organized and ready to be used for training.

The best way to get past this is by coming up with a project and doing all the heavy lifting yourself. That way, data science trainees get to combine the useful knowledge learned from courses with the dirty work that precedes it.

  1. Learn from others:

Data science employees and trainees need to understand that they are not alone in this. There is no single way of doing something right, and it’s important to stay up-to-date on everything going on in the scientific community. There are so many platforms dedicated to knowledge sharing where people are eager to help, share their experiences and connect for future conversations.

There are a few ways that data science employees can learn from others, without even having to leave their computer:

  1. Join social collaboration platforms where people share their discoveries, questions and new models/tools they’ve built: You never know if their creations might help one day.
  2. Attend data science webinars: Webinars on data science are taking place more often than ever, and many of them are free. There is no better way to learn than through live discussion on a topic of interest.
  3. Ask senior employees at your company: Seriously, people love sharing knowledge and helping others, especially in things close to their expertise. While you definitely won’t make the same mistake twice after spending the entire day debugging it, it’s still better to use your time more efficiently and have someone resolve it in a minute.

There are numerous ways data science employees can train and improve their skills. Not everything is applicable across the board, but there is potential in pursuing any of the above-mentioned points for employees to be ready for real-world, data-driven solutions.

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