Data science, in itself, is an interdisciplinary field that uses various tools and technologies to interpret, understand, and extrapolate with a goal to discover hidden patterns in data and make relevant, business-oriented decisions.
However, despite upskilling data science technology and professionals, the talent gap remains one of the biggest concerns for organizations. And while getting additional talent and resources may seem like a reasonable decision, upskilling your in-house talent is also of equal importance.
Upskilling should be part of the work culture. It should trickle down from the top management and c-suite to all the levels in an organization (with a targeted focus on each role and function). In a similar vein, you must prepare your data science talent to identify and nurture candidates along with charting their developmental journey. Furthermore, you must also prepare them to guide the rest of the organization in making data-driven decisions and explain how to use the in-house resources to do so.
A number of skills are typically involved in any data science-related project: there’s business knowledge, industry expertise, IT skills, and analytical skills. Check out the leading upskilling software offering the best upskilling programs in the marketplace today.
But there will be some who will be more comfortable with a more hands-on experience with the tools designed to automate data science upskilling. For this, you’ll have to create robust mentoring programs, wherein leaders are involved at every step of the way. However, it is crucial to add a layer of technical screening after each level of upskilling to ensure progress is being mapped appropriately.
So how exactly does one lead these upskilling initiatives?
Step 1: Create awareness about its applications and relevance
Show use cases and success stories. And this isn’t just limited to your data science talent.
Often, people are skeptical about technology, which is mostly not substantiated and merely fueled by hearsay and popular opinions about the growing use of technology. As an L&D professional, you must lead your upskilling initiatives by educating people about its benefits and use cases. At the very least, each dependent department in your organization, be it the business units, marketing, or forecasting department, should understand how they can benefit from the data science talent.
Moreover, you must publicize all data science success stories, be it internal or external, so that all employees can understand the tangible benefits of data science applications. You can do so using the informal methods of meetings as well, such as weekly town hall meetings or lunch and learn activities.
Step 2: Encourage people from all functions to participate
You must also encourage other learners from different departments to understand the basics, such as data wrangling, regression models, clustering and classification, etc. To do this, you can take some quick steps:
- Make a centralized learning repository with a number of e-learning courses and documents available. Ask your employees to go through them in groups, which will ensure maximum participation.
- Create a list of all the free tools, paid licensed tools, products, and other technology at their disposal to learn.
- Schedule periodical assessment and feedback sessions. It is necessary to include technical skills assessment at every stage to ensure the learning has tangible, mappable results.
Step 3: Benchmark the skills and assess them periodically
How effective was the training? How tangible are the results — from when they began with the program to now? Were the participants of the program able to use their new knowledge in the work environment?
These are the questions you need to ask yourself as an L&D leader. The results have to be tangible, and the only way to do so is to assess them.
Furthermore, the IT skill assessment test, to decide the success of each L&D program, will look different for each function. So make sure you create apt checkpoints at every level of learning. You can also deploy our online data science test to your employees to understand their proficiency level.
Step 4: Create an ideal employee profile for each data science role
According to our research, the most promising candidates come from a variety of backgrounds like physics, actuarial sciences, finance, computer science, economics, and mathematics. You need to map these out on the kind of roles you have and need in your organization.
Moreover, you need to create a gist of the skills in which the existing talent needs to upskill. This includes, but isn’t limited to, the following:
- Domain expertise
- Familiarity with core business issues
- Cognizance of what your IT needs are
After you take out the above core competencies, you can, then, map them to the industry focussed data science skillset. This can vary from open-source programming languages to data discovery tools. Some of these skills include:
- R
- SQL
- Python
- Java
- C/C++
- Data integration tools
- Data discovery tools (Microsoft Power BI, Tableau, etc.)
- Advanced spreadsheet skills
After gauging the skills your organizations need and the gaps in the existing talent, talk to your employees to decide their areas of interest and create a focussed L&D plan. However, their backgrounds are equally important here. For instance, a business analyst with a non-coding background will not be a good fit for a code-focused course, but their expertise can lie in visualization and communication. On the other hand, an individual with strong SQL skills will perform well in a Python course and understand data science programming skills better.
How can iMocha help?
Using iMocha, you can create a tailored assessment for the data science skillset for each learning course you create as part of your L&D process. We’ve ready-made data science tests that you can deploy to your talent at each stage of their L&D process to understand their growth. Moreover, you can work with our team and Subject Matter Experts to create customized assessments that are more in line with your current and future requirements. You can explore our skills library for data science here.
