The feeling of missing out can be a bad source of anxiety. As Ellen explains it, it means that everyone is having fun without you. If you apply it to businesses, it has become a real fear in itself that your competitors are doing better than you.

“For business managers, FOMO is not just the impending feeling that a competitor is reaping benefits that you could be doing, but it is also sensing that you are missing out and not capitalising on something that you should be doing.”

Not only can FOMO cause significant stress, it’s a perception that you are not doing enough as a business. So how do businesses get through FOMO? With Data Science. Luckily, we have Data Science to help us through career FOMO.

If you haven’t heard of Data Science before, then welcome to the dark side. It is one of the technologies that is at the frontier of IT. It is one of the only aspects that can generate quick revenue with just a few insights. Data Science has become the new norm for identifying precious data with analytics and implementing them in workflows to make more money.

“Your Job is being automated away from you.” — Andrew Yang

One of the many parts of Data Science includes something called automation. Some students we have, after seeing an automation or two in their company, immediately feel that they are at risk to lose their current job. If you can’t beat them, then join them. Like many others before you. Maybe it’s time to make the switch?

The shortage of Data Scientists is becoming a serious constraint in some sectors’ because all companies are now leveraging the use of Big Data. If any business is not headed in this direction, they will lose out big time. The rising demand for analytics is giving rise to an exponential growth of data science vacancies.

If you Google Data Science, it seems hard, as with learning anything should be. But once you learn all the fancy words (matrix, dimension reduction etc…) You’re halfway there.

There are a lot of things to consider when switching careers. Here is a list of the Top 10 current careers that had successfully changed into the Data Science sector in Hong Kong.

1. Software Engineers

Some programmers or software engineers find themselves wanting to be more communicative or not wanting to just stare at a screen 100% of the job. If engineers want to go out more often and speak business with people; share their thoughts and theories about…everything.

Since they have experience in deploying code to productions and working with developer teams already, this can be an easy transition. Especially for software engineers that find out after they graduate, they do not like to just write code, they like to use their application and make an impact on what they do.

The most common way for programmers to get recognized for a data science position and not development is that they build their own data science project to show to potential employers. They leverage their programming skills by showing off their technical skills. This is extremely effective because it leaves no guessing for the potential employer. What you do in your project is your potential as a data scientist. Programmers can often let their imagination go wild and have the ability to make interesting use of data or collection methods. In turn, it gives an idea to employers that not only are you skilled at programming, but you have a sense of using data as well.

Data Science bootcamps are increasingly familiar with this method and are actively using it to attract all levels of employers. You can find out more at this Data Science trial Class: Venturenix LAB

2. Underwriters

Underwriters have come a long way from the day when everything was done on paper. With new tech advancements, a vast amount of customer data is available and readily accessible. Some of it even in real-time. They operate under a strict philosophy of risk selection using the tools that Data Scientists make to refine and analyze risk.

Underwriters are mostly familiar with the main data. The data includes credit score, credit line utilization, delinquency status, credit inquiries, etc… All the traditional forms of analyzing risk have been revolutionized with machine learning and AI.

The idea that data science might be a good career switch comes when the underwriter starts to enjoy customizing the tools they use and enjoy coming up with innovative ways to use the data. On occasion, they make the switch to become a rare and in-demand, domain-focused data scientist.

3. Accountants

Accountants, especially those in the industry for many years, have been noticing that parts of their job is being automated more and more as time goes by. They are typically well-versed in excel and put formulas that calculate most things.

However, technology has become a master at redundant and repetitive tasks to generate data; something that accountants have been doing manually for many decades. With all these advances in technology, gone will soon be human error and risks. As a CPA, the fewer errors, the more happy everyone is.

Machine learning especially has made auditing more fun and doable. Being up to date with new policies and storing data is making number-crunching more doable. Accountants that make the switch into Data Science often find themselves dominating as Domain-focused Data Scientists.

4. Sales/Business Development Managers

Data Generates Leads. In the modern age, especially in the 4th Data revolution, we are at an increasingly saturated point of sales where every aspect of selling can be leveraged to make a sale.

Have you ever received that birthday card for 50% off? How about a whole package congratulating you on your pregnancy that no one had known about except you? [Link here to Supermarket] Congratulations, you are now in their marketing pipeline because one day, you’ll bite.

Business Dev Managers and Sales managers find themselves often working with a business intelligence people or Data Analysts people. To see where marketing trends are or to get updated on market patterns. They don’t want to miss out on any potential lead. Like I said, FOMO is a big keyword in 2020. Now, we finally have a tool to battle it out.

Managers find themselves looking at Predictive Analytics. If they are not using any form of machine learning or Data Science, they might be stuck not meeting targets. Some managers like processing and making use of Data, they find it making the switch to a business-focused data scientist to be quite easy.

5. Economy/Maths/Physics/Stats Majors

There have been many successful career switches in any major that included some form of math. Data naturally includes lots of numbers and any person of these majors has to have some sort of sense of math. As math is used in algorithms that sort data or predictive tools that make patterns in data, the successful switch depends on their ability to digest data and make it readable to a normal person.

During their university work, all of these majors find themselves working with calculus or linear algebra. To have a technical understanding of this applied with real-life data usage, Data Science is becoming more natural to them for a career switch.

6. Business / Financial Analysts

With an increasing need for Data Scientists, businesses are actually shared between the two. The tools that are used extensively in business analytics such as Excel, Tableau, SQL, and Python.

Both positions share similar techniques to help the business whether it’s Statistical Methods and Forecasting. Being a BA that transitions to Data Science is a huge advantage as their domain knowledge is already existent. As they build up their technical skills in predictive modeling and machine learning, their career path can easily change into Data Science.

7. Marketing Managers

There is a rise of a new kind of data scientist called, ‘The Marketing Data Scientist’. You guessed it. It is a combination of marketing and data science. They do both with managing data and play a part in the execution of it. In a start-up, with limited resources, the Marketing Data Scientist can be quite valuable in terms of saving money from a head-count, and having someone who is familiar with the business organizing the data gathered. A kind of two birds with one stones thing which is becoming quite common in businesses now. Unique specializations are becoming more merged.

Having a Data Scientist who has a marketing background is immensely beneficial. Since marketing is engaged with the product or service, they can spot opportunities and trends from different channels much faster and more targeted than a data scientist without domain knowledge. People with a marketing mindset can easily set up a defined structure to make use of data. They can provide more valuable insight on what is effective and what is not.

Growth managers especially are now required to use some form of Data Science to show patterns, behaviors, and predictions on their client/consumer behavior. If they cannot, then they are put aside for someone who can. It is a must in the new digital age.

8. Office Managers/Admins

Office managers have come a long way from just ordering lunch and bringing people coffee. A lot of times, in any position, companies just want to hire a persona based on if they can work with each other.

In 2020, the officer managers are now a supportive part of the business with administrative duties. As the company grows, they have to adapt and adjust to the ever-changing workflows and organizational structure. Not only for data but accounting, tax reporting, legal compliance, and even auditing.

Office managers are finding themselves learning excel as a starting point with formulas and basic functions. As accounting and auditing best practices are evolving’ automaton has become a must. If a company has the tools and standards in place to analyze data and put it to good use, all they need to do is find someone to help them run through the data science workflow. The career path for office managers can be a great entry point into IT.

9. HR Administrators

Administrators in the Human resource department have found strong success when changing into a field in data science. Especially at a startup level, HR administrative positions have evolved past the normal attendance and accounting duties.

More and more CEOs are incorporating machine learning to evaluate well0being, how to hire and maintain employees. The HR sector is usually one of the first sectors to be the guinea pigs in any digital transformation, with all the readily available application tools.

With all the customization or workflow and KPI integrations, HR helps achieve. Some HR administrators might find their hidden talent in IT.

10. Project Managers

Project managers know that being a PM isn’t a straight-line process. You have to be versatile and evolve along with the project needs. PM also has to fill the gaps where needed, either in hiring or doing it themselves. In recent years, they have been turning to Data Science to help them in all aspects of project management.

Today, project success depends on the efficiency and the effectiveness of the invested work but also how to grow that work exponentially where 1 + 1 = more than 2. It’s a constant race to maintain a competitive edge in the market, so businesses rely on PMs to make a correct decisions. In return, PMs need to care about the impact and be in the know.

Data Science comes in where they can determine certain patterns and avoid time-consuming and previous mistakes. They can reduce risk and more projects can be streamlined with smart insights from interpreting gathered data. Project Managers can truly enhance their productivity and performance.

When needing to make tough decisions under tough situations, they turn to data models or predictive analytics to help them stay rational and keep the project going.

More Project Managers are constantly finding themselves in bootcamps or online courses to help them effectively use the data that they have. For younger companies or startups, they have limited resources to have a headcount allocated to data science, so project managers have to do this on their own. It is not uncommon to see PMs fall in love with data and switch entirely to a business-domain focused data scientist.