Introduction
Although Data Science has been around us ever since the 1960s, it has only gained traction in the last few decades. This is one of the main reasons why budding data scientists find it quite challenging to find the right mentors. However, this scenario is drastically changing now. With the right approach and by looking at the right corners, you can find data scientist mentors who can help you bridge the gap between theoretical and practical applications of data science.
In this article, we will be looking at why there is even a need for individuals to have mentors in data science and how can we find them.
Why does one need a mentor in Data Science?
Earlier for data science jobs if you had a technical grad degree, you could brush up on your Python skills, fill a small portfolio with scikit-learn projects, and more or less watch the offers roll in. But this is not the case anymore. Data science industry has made a lot of advancements in a small span of time. These advancements have basically done two things here. First, they have resulted in organizations looking for more than basic skills from the data scientists. Secondly, it has created a huge demand for data scientists which have resulted in a lot of competition between different job seekers
- Take you under their wing and help you to stay motivated and discover the path that you may need to take.
- Understand what it takes to get to the top and be a valuable resource by answering your career or work related questions and providing good advice.
- Help you to be passionate about your success and brand.
- Provide you with a wealth of knowledge and resources and help you to connect with various Subject Matter Experts (SMEs).
- Be your own personal cheerleader and help you discover new opportunities.
Where to find mentors?
Available Online Courses
Dimensionless Technologies provides best online data science training that provides in-depth course coverage, case study based learning, entirely Hands-on driven sessions with personalized attention to every participant. We provide only instructor-led LIVE online training sessions and not classroom training.
These programs are led by Kushagra (IIT, Delhi – 10+ years experience in Data Science) who is a machine-learning practitioner, fascinated by the numerous application of Artificial Intelligence in the day to day life. He enjoys applying my quantitative skills to new large-scale, data-intensive problems. Also, he is an avid learner, keen to be the frontrunner in the field of AI.
Learning data science will never be easy without any help from the community or from someone who is willing to help beginners. These someones are the ones that are making up our amazing LinkedIn Data Science Community.
Kate Strachnyi ♕ – If you’re in pursuit of having a data science career and learning data visualization. You must follow her in a heartbeat. Her hashtag#makerovermonday posts are amazing and she shares a lot of information that can help you in your journey.
Randy Lao ☁️ – He has been serving the community from as long as I know there was one for data science in LinkedIn. He shares the best resources for everything in data science. Starting from libraries in Python to courses on machine learning. You will find a lot of useful resources in his posts. Also, check out his collaboration with Kyle.
Kyle McKiou – Top-notch data science influencer. Can’t afford to not follow him if you want to learn from books to pick for data science to interview tips and tricks. Constantly helping beginners with his experience and content.
Mentors at work/college
Experienced working with professional developers can make or break your ability to land a data science position.
The best strategy we’ve found is called income share: basically, aspiring data scientists work with an expert mentor on an industry-level project, and they pay their mentor a small share of their future income in exchange (but only if they actually get hired as a result).
Income share has two benefits: first, it means that you can get expert instruction at no upfront cost. You only pay when you can afford to, and if you don’t get a data science job within a certain time limit (usually 24 months) you don’t pay anything at all.
Second, income share aligns the incentives of the mentors and mentees. Even after the formal mentorship period ends, mentors still have a stake in your future success, which means they’ll look out for opportunities for you by default.
Income share mentorships make new opportunities accessible to people who can’t afford the expert time or find professional data scientists to learn from.
Paid mentorship available at different websites
If you have a disposable income to spend then I’d highly recommend hiring a mentor who can walk you through your problems. When I was just starting to learn data science, I found having a paid mentor (via Thinkful) was incredibly helpful as it allowed me to ask all the dumb questions that I otherwise would’ve been too embarrassed to ask on a community forum.
Learn code & data science 1-on-1 with a mentor
Instant hands-on programming help available 24/7
Clarity – On Demand Business Advice
What if you are not able to find one?
Take ownership of your career
People who think a mentor is a key to success may lack confidence in their own ability to take initiative. Carreau advises taking control of your own growth. “Create your own plan to take control of your career and your life, and rise above the average,” she recommends.”Howard Schultz is a great example of someone from humble beginnings who took control of his destiny early. He has famously said, ‘I had no mentor, no role model, no special teacher to help me sort out my options.
Get value from peers
Even though a mentor is not necessary to success does not mean that you should ignore the opportunity of turning to your peers for guidance. In fact, Carreau advises joining a peer-mentoring group. “The combination of great networking and feedback makes participation in these kinds of circles invaluable, rather than the standard networking coffee meetings between two people,” she stated. “Getting feedback from not just one, but many people is much more valuable, and leads to better solutions and ideas. The dialogue in these groups consistently impresses me. And the business outcomes I see from promotions to career changes to overcoming major career challenges are substantial.
Learn from the youth
As technology continues to accelerate the rate of change, many would-be mentors are finding their approach outdated and obsolete. They find they actually have a lot to learn from younger generations. Instead of using experienced senior leaders as mentors to younger colleagues, some companies are reversing roles. “The younger person becomes the mentor, and the senior professional becomes the mentee,” explains Carreau. “In India, they have found that this concept has re-energized senior professionals and showed them a lot about technology and the social media world we live in today.
Combine activities to maximize time
For real value to take place, mentorship requires focused time, which is a valuable commodity. Carreau recommends looking for ways to combine learning activities to save time and be productive all at once. “Instead of going for a jog and then meeting a friend for coffee, why not go for a jog with your friend?” notes Carreau. “Instead of letting your commute be wasted time, listen to a podcast, relevant news or language tapes. Leveraging the power of multipliers lets you accomplish more by overlaying tasks that make sense together.”
Become a better networker
Building a network is not an intuitive skill for most people. It is also an iterative process; you are never finished, and the way you develop your network will change as your career progresses. When you begin networking, you are still figuring out your interests and career goals,” elaborates Carreau. Because of this, you must cast as large a net as possible among the people you can. Hence you should contact-family friends, school alumni and more. As you understand your ambitions better, you will become a better networker as well. Thus you are able to quickly spot the diamond in the rough among your contacts. You should not focus on networking and wasting your time on superficial contacts. Rather, make ensure you are engaging in an authentic and helpful way with them both online and in person.
Conclusion
Data science is one of the areas where this idea is starting to take off. Data scientists remain rare, and students may find it hard to get access to information. Mentoring bridges that gap and enables students to improve their skills and understanding of using data science in business.