The Covid-19 crisis has upended business infrastructure and continuity. With advancements being made in the field of data and analytics, businesses can use data to determine the skills and methods that are necessary to train and employ workers and equip them with new capabilities needed for what lies ahead.

Data scientists are crucial to business growth and innovation, and they must be prepared to adapt to a continually evolving digital landscape. Upskilling involves growing and diversifying the skills and knowledge that are necessary for future success and employment. And as a data scientist, it's essential to remain competitive and excel—and help businesses continue to thrive with a future that may continue to utilize new IT architectures and remote infrastructures.

Leadership should take steps to develop new upskilling strategies so their employees can continue to be an asset during the ongoing global disruption of the current pandemic—and beyond.

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A Deep Dive into Four Helpful Upskilling Strategies for Data Science

As a business executive, manager, or leader, it's your responsibility to be tasked with upskilling employees in data science. But how do you start? Let's take a closer look.

Predict Future Talent Needs

Business leaders need to adopt a selective approach to upskilling, and to that end, should look to forecasting to obtain a reliable idea of the type of skills and the number of employees that will be required to accelerate future business goals.

Think about overall company needs and the new skill potential that would fill existing talent gaps, as well as likely future investments in new technologies and tools that will require new capabilities and talent, such as artificial intelligence (AI).

Talent Analysis to Guide Skill Priorities

It's more efficient to improve existing employee skill sets than to hire new talent, especially given the current data scientist talent shortage.

Consider conducting a workforce talent analysis to evaluate unique employment needs within the company, which qualifies individual employee skills and enables the organization to build employee profiles that can inform upskilling decisions.

Talent development is critical in increasing productivity in a growing work environment, and businesses should look internally for the right solution.

Encourage Strategic Skill Procurement

As a business leader, you have to position your employees to succeed and innovate in new working environments and conditions.

Continuous learning and skill development and interactive training programs in specific data science fields guide professional development and improvements towards unique goals, such as effectively working with new technological infrastructures that thrive on technologies like machine learning (ML) and cloud computing.

Encouraging new skill procurement in an ML programming language like Python or Julia, or risk management would be highly appropriate in this regard.

Help Employees Navigate New Dynamics

Your workforce needs to be diverse in skills, education, and culture, and able to keep up with ongoing and changing job role dynamics—a truth in any work situation.

Consider the value of an existing employee with advanced knowledge of the business, such as a business analyst. This employee would be well-worth the upskilling investment of acquiring specialized data science skills, such as SQL, Tensorflow, or Tableau.

With this strategy, hard-to-acquire business knowledge is combined with the mathematical and statistical-based skills that drive data science, and your original investment is returned several times over with talent that benefits your unique business in myriad ways.

Are you are en route to becoming a certified Data Scientist? Try answering these Data science with R practice questions and assess your level of understanding.

Equip Yourself with New Skills for Ongoing Disruption

Organizations need to be prepared to upskill employees in a world of disruptive uncertainty created by the acceleration of technological advancements and unforeseen circumstances.

Online education certification company Simplilearn helps organizations with their upskilling strategies with the PG in Data Science, the Data Scientist Course and the Data Science Bootcamp so that they can enable their employees to meet the rising demands of new technology, desired talent across industries, and unpredictable events.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Post Graduate Program in Data Engineering

Cohort Starts: 16 Jul, 2024

8 Months$ 3,850
Caltech Post Graduate Program in Data Science

Cohort Starts: 23 Jul, 2024

11 Months$ 4,500
Data Analytics Bootcamp

Cohort Starts: 23 Jul, 2024

6 Months$ 8,500
Post Graduate Program in Data Science

Cohort Starts: 7 Aug, 2024

11 Months$ 3,800
Applied AI & Data Science

Cohort Starts: 20 Aug, 2024

3 Months$ 2,624
Post Graduate Program in Data Analytics

Cohort Starts: 11 Sep, 2024

8 Months$ 3,500
Data Scientist11 Months$ 1,449
Data Analyst11 Months$ 1,449

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