Data science uses various technologies and tools to understand, interpret, and generalize data to provide business-oriented and relevant decisions.
But even though data science professionals and technologies have improved a lot, there is still a talent gap. While getting additional talent can be a solution, so is upskilling your in-house team members.
Upskilling should include all levels of the organization – with a focus on each role and function. There are a number of skills that typically are involved in data science-related projects: business knowledge, IT skills, industry expertise, and analytical skills.
It is important to include different levels of upskilling to ensure proper progress depending on different professionals’ preferences.
Read on to find out how to lead upskilling initiatives.
Build awareness about applications and relevance
Show success stories and use cases. People are often skeptical, especially about technology, but these opinions are not substantiated. You need to educate people on the benefits and use cases by using upskilling initiatives. Every department in the organization – from marketing and business units to the forecasting department – need to understand how they will benefit from data science talent.
Also, publicize success stories to ensure that the employees understand the substantial benefits of data science applications. Use informal methods such as weekly meetings or learning activities to do so.
Encourage people to participate
You have to encourage people from different departments to get the basic knowledge about regression models, data wrangling, clustering and classification, and so on.
- Create a learning repository and include online learning courses and other resources available.
- Provide a list with free tools, paid tools, products, and everything else needed to learn.
- Schedule assessment sessions and include technical skills assessment at every stage to ensure tangible results.
Specify the skills and assess them regularly
You need to know how effective was the training, how tangible were the results, and whether the participants will be able to use the new knowledge in their work. To be able to answer these questions, you need to assess the results.
Use IT skill assessment tests to determine the success but make sure to create checkpoints for each learning level. You can also use online data science tests to understand the proficiency level of the employees.
Build an example of the ideal profile for each data science role
Most promising candidates come from different backgrounds such as finance, economics, computer science, mathematics, physics. Map these and use them depending on the roles you need in your organization.
Also, create a list with all the needed skills that your existing talent need to upskill. Include factors such as domain expertise, awareness of your IT needs, knowledge of core business needs.
Then, map these core competencies to the industry-specific skillset. Some of these skills include R, SQL, Python, Java, C/C++, data integration tools, and data discovery tools.
Once you have gauged the skills your organization lacks in the existing talent, discuss with your employees, and create your upskilling strategy based on their areas of interests and backgrounds. You can use tailored assessments for data science skill set to understand the growth and measure the results.
