Mar 24 2021

Maximising the ROI on your Data Science employee learning programs

“Post-training, if employees are not provided project-based lessons, ROI on learning programs (specifically for Data Science) can be significantly low.”

by  Jackie Tan , Co-Founder, UpLevel

Data is the new oil

With an increasing amount of data generated by organizations, there is a need for employees who can extract business value out of the data through data analytics or data science.

However, hiring data analysts and data scientists is a very expensive and time consuming affair. According to research by Wanted Analytics, it takes around 43 days and around $60,000 to hire technical staff in a company. The reason for the high costs is on account of the indirect costs such as time spent by the HR team to shortlist and interview candidates, in addition to the direct recruitment costs.

Image Attribution: Freepik

As such, one strategy that can be adopted by organisations is to train employees from within the company in Python and Data Science and empower them to harness data in their everyday work.

What are the benefits of Internal Reskilling & Upskilling?

There are numerous advantages to this, such as the obvious savings in time and money required to recruit, and the fact that the upskilled employee is already familiar with the business activities of the company and can identify areas that benefit from data analysis. Not just that, internal training & talent mobility helps in improving employee retention as well.

This Learning is usually administered in two ways:

  • Engage vendors and trainers to organize short courses on introductory Python and data science, usually spread over a period of 3 days three days.
  • Allow employees to enrol in short & long online courses, e.g., Coursera, Udemy, and reimburse them for the cost incurred. a vendor to provide a learning platform for the employees, e.g., DataCamp.

Maximize coding ROI with follow-through training

In theory, any of these three ways will lead to the employees acquiring coding and data skills. However, what really happens after the learning session? Usually, the upskilled employees are left to their own devices to follow through with the training. This is not an ideal situation because there is no continuity to build on the learning.

More specifically, without follow-through on introductory coding lessons, there is not much an employee can do at the workplace. This leads to a poor ROI of the coding training and wasted time and resources spent on upskilling them. To address this, L&D teams should consider a few things:

  1. Work with training vendors for project-based follow through trainings with business units
  2. Enrol upskilled employees in deeper learning programs to develop intermediate skills to achieve business impact
  3. Provide employees with additional resources for self-paced learning


Image Attribution: Freepik

Option 1 is hard to execute, as it requires buy-in from stakeholders in the learner's department. This may not be feasible in organisations where a strong learning culture has yet to be established. On the other hand, Option 2 is slightly more achievable but would require the employee to be away from work again. Option 3 is useful for employees that want to learn at their own pace, but would require self-discipline for completion.

Regardless of the options, there is a need to follow through basic Python and Data Science education with project-based training. With project-based training, your employees get to

practice problem-solving skills in a structured manner similar to problems encountered at the workplace

  • execute crucial well-defined parts of a project, from data collection to creating a data product
  • develop on-the-project soft skills such as thinking through problems and a data scientist mindset

Address the training gap: Introducing UpLevel

To address this training gap, UpLevel has developed a catalog of end-to-end data projects that employees can work on. The projects are developed after training thousands of Python and data science learners across skill levels and experience.

The end-to-end data modelling process mirrors actual data science cycles in a company, from defining business requirements and collecting data, to data cleaning and engineering, and finally to data product creation. UpLevel’s training pedagogy is based on scaffolded experiential training, which means learners are guided and not spoon-fed during the projects.

UpLevel has 400 hours of project content in its catalog, ranging from beginner projects for those who just completed short courses, intermediate projects for those who want to broaden the breadth of skills not covered by usual courses, and advanced projects for those who want to apply techniques from cutting edge research.

Click here to schedule a demo on how UpLevel can supercharge your employees’ Python and data science skills through projects, or drop Jackie an email at

UpLevel can be found on the marketplace under the "Talent Development" category. 

Jackie Tan is currently the co-founder of UpLevel, a data science training startup where he has empowered over 1000 students in project-based data science training. A serial entrepreneur, he previously co-founded a Q&A insurtech startup fundMyLife in 2016 which was acquired in early 2019.

Shortly before UpLevel Jackie was the Chief Academic Officer of UpCode Academy, a coding academy for Python and data science where he created coding curricula, and taught and mentored close to 300 students. He then started UpLevel because he observed that there was a lack of resources for a novice learner to gain experience and build experience.

Jackie is passionate about hands-on learning and is obsessed with creating enjoyable learning experiences that have tangible outcomes. He is also an avid educator, holding large workshops on data science in collaboration with partners such as Shopee, and NUS. In his free time, he lectures on data science in Nanyang Technological University and lectures on entrepreneurship in the Singapore University of Social Sciences.

Jackie holds a PhD from the Nanyang Technological University, where his thesis combined data science and biology. He is a Forbes 30 Under 30 honoree and was also the National Youth Entrepreneurship Award awardee.  Linkedin


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