13 Jan 2022
You have decided to go for a data science career or you might have thought to transition from the academic world to handsomely paying jobs in data science. But how could you make sure you have embarked on the right career path? How do you know you will end up with the job you have ever hoped for? Who would tell you precisely what skills/projects it takes to be the chosen one among other applicants? Should you adopt a Python course or turn to a well-structured data science program that also focuses on analytical and mathematical techniques?
The answer is a good mentor – an experienced Senior data scientist.
To excel at data science job interviews, it is important to have a guiding light that prepares you well. Mentorship is therefore fruitful in making you industry-ready, preparing for real-world business scenarios and eventually landing you on a promising career landscape. Let’s dive a little deeper into what data science mentorship looks like and why you need a veteran industry mentor for your career.
Read Also: Data Scientist Salary in Hong Kong
In its super-basic meaning, mentorship is meant to prepare learners for real-world challenges and equip them with enough practical knowledge to sustain the relevant market. Data science workplace mentoring has become popular among graduates and entry-level professionals. The main motive of formal organizational mentorship in data science is to build skills in a specific subject and be able to apply hands-on knowledge to build business-oriented solutions.
Data science aspirants going through a healthy data science mentorship program can work efficiently for companies that demand more than basics and modest portfolio. Data science mentors – who are industry veterans – may collaborate with educational startups to conduct interactive guidance about the latest career choices, projects, and courses to opt for. Aspiring candidates can participate in 30-minute Q&A sessions with senior experts to:
⦁ Test their industry readiness
⦁ Get constructive feedback on projects
⦁ Set learning targets
⦁ Get career advices
⦁ Improve coding standard
⦁ Boost awareness about interview challenges
⦁ Know about modern industry practices
The one good area where mentorship works best for junior data scientists is an obvious knowledge gap. Being fresh and inexperienced, grads carry insufficient industry knowledge and need to develop an understanding of business-oriented data science solutions. This is where mentors aid. Mentorship and internship share a lot in common as good mentor shares words of wisdom and insight and makes candidates industry-ready.
Newbies are not sure of the role and responsibilities they ought to perform at a company, so mentors train them on exactly how analytics works for an organization. Also, in this increasingly competitive field, it takes more than plain basic skills for you to take off and become highly qualified for data science positions. The mentorship program plays a solid role in landing a promising job position in data science.
Even though you are a novice in the data-centric domain, you will avert from choosing ‘any’ data science job that comes your way. This is because stepping into a right data science career is not about settling for anything as it may ruin your later career prospects. The first step in entering the right data science career is to have real passion and interest in data-focused activities and the next is to know what area of the industry you could best fit and discovering what jobs are available and suitable for you – what jobs you could expect to land. Mentorship from a keen, seasoned data science pro can empower your acumen and drive your ultimate decision about the right career choice.
Trying to brush up your Python skills and add some good Python programming projects to your portfolio is no longer enough for beginners to land a great data science job. When the word data science was first heard around 2013, it was still in its emerging, even incubatory phase. Employers were happy to hire a bunch of inexperienced technical grads with some Python programming skills mainly because they had limited options. Back then, if you wanted to end up with a good-paying employer, you could just brush up your skills in Python and flaunt a portfolio filled with scikit-learn projects and land a great offer. However, the scenario has turned around significantly over the years and now they have much advantage and freedom to choose only the best polished talent suited for their respective business processes.
If you have a great mentor in your league, it can work in so many ways and potentially impact several aspects of your professional mind set including your:
⦁ Goals and vision ⦁ Competency level ⦁ Confidence and Attitude ⦁ Overall Polish in skills ⦁ Job Approach
Since a senior data scientist has gained large-scale practical exposure in the industry and garnered substantial expertise, they are able to introduce you to:
⦁ Real industry practices ⦁ Actual work environment ⦁ Specific Skills in demand ⦁ Industry/business processes ⦁ New updates and innovations ⦁ DevOps approach
By receiving enough awareness from your respective senior data scientist mentor, you can go from nowhere to somewhere in your familiarity and rise above the mediocre. Mentorship of this level is designed to prime, prepare and harness you, create a smart, polished professional and enable you to score high in follow-up interviews and successive stages.
If you are, as a beginner, striving to expand your career horizon and launch a big career move, it doesn’t nowadays look probable without some strong guidelines from experienced field experts. A good mentorship session from well-established data science professionals can, therefore, help you dial up a notch especially if you are trying to figure out effective ways to switch from low-paying, somewhat vapid field to interesting, high-paying data science opportunities.
It is apparent, especially to those who are fairly acquainted with the data science industry that getting noticed by bighead employers is no longer as easy as it used to be before. Landing a place at a big company with a right profile is a rustic road ridden with unpleasant challenges and odds. This is especially true when you are starting out in data science.
Even though the latest job market is exploding with high demand in data science tech jobs, it is quite hard to find promising job offers that do more than just pay the bills for you. As the field has evolved, companies expect to hire candidates with specific skills for a specific role. Modern employer expectations include more than plain, basic algorithms like Decision Tree. Now you must be able to use leading Python libraries and tools like scikit-learn and TensorFlow to develop and deploy mature data science models.
From raw to ripe: Mentors better your portfolio
As companies are growing picky and prefer to choose only the mature, ripe data science talents, it is crucial to think of all the ways to strengthen your portfolio and be highly employable in 2021. Because the current job market is flooded with grads offering entry-level skills and a humble portfolio, you need to develop that rare, outstanding edge. One way to achieve this objective is to go from raw to ripe with the help of impactful mentorship. When you have data science industry experts working with you on your ongoing industry projects, you have a far better chance to get up to speed on the data science platform.
Since quality projects are the best way to judge one’s grasp and potential, make sure you have met the right mentor who not only helps you with projects but also guides you to good project finder platforms and volunteering opportunities.
Mentors can touch your coding abilities
You might think data science models written in Python or R are easy to code once you learn it all. However, the competition is soaring high, bringing you down to the point where only elite and sophisticated edge can help you stand out and win a great job position at a big company. To master such high-level, strong and competitive skills, you need reliable support and directions from someone who has already won the battle and can show how to land on a bright career path.
Mentors stand like a guide between the completion of a micro grad degree and your final career destination. For instance, it is better to know how to write the clean app production code than entering the industry and then learning about it. You can leverage the mentor’s bright inputs that come from years of experience and learn to correct your coding style. You will be told what approach and practices to follow when producing a robust industry-ready production code.
Read Also: 10 Ways to Activate a Data Science Career
A common question that often plagues young data science aspirants is whether they should join a self-paced Python course or go for full-time data science Bootcamp. To answer this, it depends on your career goals. If you're looking for career change then full-time data science & machine learning bootcamp would be suited for you. The full-time Bootcamp curriculum is more towards industry-focused training and preparing you to be job-ready. Whereas, if you're looking to just upskill yourself then you can opt for part-time courses.
To learn more about the data science industry within Hong Kong market, check out Xccelerate's job ticker for some insightful data. And if you want to know companies that are hiring for data science job roles, visit Xccelerate's job board.
(https://xccelerate.co/en/blog/know-your-data-science-and-machine-learning-instructor) with experience of more than 10 years in Data Science and analytics across different industry verticals - including telecommunications, cybersecurity, insurance, e-commerce and financial services.
If you are at a choice point in your career and need someone to help you navigate professional challenges. You can make an appointment to our complimentary 1-on-1 Career Consultation and receive personalised career advice.