5 Steps to launch your Data Science Career in 2022

By Vahid Haghzare, Director Silicon Valley Associates Recruitment &
Armae Garcia, Marketing Associate, Silicon Valley Associates Recruitment

In recent years, Silicon Valley Associates Recruitment has noted various industries across Hong Kong have relied on Data Science as their charger for development. Companies now use it for large amounts of data and augment business operations, project management, and IT recruitment.

IT Recruitment Agencies in Hong Kong now recognize Data Science as a propitious career because of its demand in the job market and the career advancement that it offers.

The recent Covid-19 outbreak has made Data Science a vital instrument in taking on the different industries’ actual complications, including the banking, medical, retail, and transport sectors. Since it has developed, Data Science provided scientific expertise for better decisions and predicting results from previous data.

More career opportunities offered by employers and IT Recruiters can be expected in this field as it continues to emerge over the coming years and grow more and more in importance.

Tech Recruiters at SVA Recruitment reported that last year, Data Science ranked second out of the Top 10 most sought-after skills, with thousands of new job opportunities for data analysts, data scientists, and machine learning engineers.

The escalating need for Data Scientists is yet to be fulfilled as there is an insufficient number of candidates who are not skilled enough for the role. The younger generation of IT pros is highly encouraged to refine their data science skills to be able to succeed in this field.

Here are 6 advice to take note of if you are pursuing a Data Science career

1. Align your skills with the right position

Data scientists work in many different roles, from analysts to visualization experts and machine learning experts.

To succeed in your career, it is important to choose the right position for your skill set, experience and education, and personal interests

2. Upskill

Once you settle on a role; the next step is to study further. The best choice is, to begin with, a mainstream tool/language and then expand your knowledge as you progress further into the realm of data science.

There are a variety of certificate and degree programs in data science. Certificate programs include basics such as tool/language selection, while advanced post-graduate degrees provide a deeper analysis of data-driven decision-making.

There is a trove of programs and courses on topics with class materials available online, as well as live classes, which are held at various locations throughout the country.

There are offers of blended learning, where learners can take part in both traditional classroom instruction and online learning simultaneously. Students have the opportunity to gain job-ready skills through projects, assignments, and real-life scenarios.

It can be hard for young IT pros who are pursuing the Data Science career path to steer clear of mistakes along the journey. This involves not prioritizing the fundamentals, focusing on too many languages or tools, not giving enough attention to learning data visualization, and not giving time to know the latest trends in Data Science.

3. Connect with the Community

Connecting with peers and colleagues will help keep you up to date and immersed in your field. Peers can provide a fresh point of view to your problem-solving efforts and facilitate networking.

4. Start Practicing

Practical and applied learning are more essential than just theory and knowledge. Practical applications allow you to gain real-world experience and portfolio, which can be beneficial and applied to your career. Recruiters and employers assess you on what you have actually done, not what you have studied or theorized.

5. Improve your Communication Skills

A data scientist may perform a variety of roles. They may explore the data, gain insights, and model predictions or patterns. Critical thinking is a major part of their job. But so too is communicating your findings later.

The most significant challenges for data scientists include collaboration, as well as time management. A data scientist's job requires that they work with multiple individuals, constantly analyze large amounts of existing data, and sometimes reach a point where they are jammed.

Companies often have different stored data structures and inconsistent data formats, which makes it difficult for data scientists to validate the information.

The ability to communicate insights to key stakeholders so that they can make informed decisions is an important part of becoming a well-rounded Data professional. Soft skills, such as critical thinking, listening, persuasive communication, and problem-solving also help you develop these important abilities.