Without the professional expertise of data scientists, it is difficult to gain actionable insights through machine learning algorithms. As many organizations are now opening up their doors to machine learning and unlocking its power, this has increased the value of a data scientist. Data scientists know how to accurately drive the value of a large amount of information that already exists inside an institution. But let me ask you the burning question: Do you need data scientists now? Can you do machine learning without hiring data scientists?

Why is it difficult to perform machine learning without hiring data scientists?

At the moment, data scientists are definitely in short supply, and trained data scientists are aware of this scarcity; they, therefore, judge companies from the compensation packages they are offering. Several organizations feel that without experienced data scientists, venturing into machine learning and data science is difficult. But in reality, this is not true. Many organizations, still in the early phases of their data science journey, struggle to understand what machine learning and data science can do for them. They do not completely understand which skill sets are needed for implementing machine learning and are required to depend on data scientists.

Let’s take a look at how to do machine learning without hiring data scientists.

Train existing employees into data scientists

In several organizations, there are mathematically skilled employees having quantitative skills. Organizations can gather such individuals with different skill sets and form a data science team. For doing this, organizations must focus on the following skills:

• Communicator: A person who conveys complex information to others in an easy to understand manner.
• Data Wrangler: A trained person who can manipulate data into the required format for data analysis.
• Modeler: A person who can apply statistical models to data for gaining insights.
• Visualizer: A person who can produce informative and comprehensible visualizations.

Partner with academic institutions

Several universities and colleges are now offering data science related degrees. You can partner with these universities for specific projects. This helps in serving a dual purpose for the organization; firstly, it gets skilled resources, and secondly it also provides students with real-world learning experiences. Partnering with academia can take four main forms: internships, class projects, or innovation labs. It is best to use students trained in data science who know about quantitative methods in machine learning really well.

Hire third-party professionals

As there is an immense shortage of machine learning skills, you can resort to third-party professionals. These professionals can easily accelerate and kick start the success of data science programs. There are many consultancies that provide a spectrum of assistance, ranging from creating project ideas, coaching, and early piloting to providing a complete package of managed services.

Thus, you can follow these guidelines for implementing machine learning in your organization without hiring data scientists.