Balancing AI Courses and Real-World Projects, Mindset, and Securing an NVIDIA Internship
By: Michael Burkhardt
Tags: Kaggle, Data Science, Machine Learning, Omdena, Deep Learning

Balancing AI Courses and Real-World Projects, Mindset, and Securing an NVIDIA Internship

Data Scientist Kennedy K. Wangari shares his learnings as a community leader and incoming data science intern at NVIDIA. Michael Burkhardt Follow Oct 3, 2020 · 5 min read

Can you describe yourself in 50 words or less?

Kennedy Wangari is a Data Scientist, based in Kenya. An AI Community Leader and Innovator, Kennedy is passionate about tech communities, and harnessing the power of data and innovative AI technology to make a better and easier tomorrow. He’s a firm believer in the power of open source: words, knowledge, and ideas should be accessible to everyone.

Why are you into AI and not something else?

I believe that we can apply AI to tackle, and solve complex problems, in numerous domains, thus providing fundamentally new approaches to every problem and situation in this data-driven world.

The AI-powered future looks promising, and I would love to be part of this radical transformation changing the face of humanity.

To improve your skills, how do you complement course work and real-world projects?

Read

Cultivate the culture, and habit of reading research papers, domain academic literature, famous case studies, and articles. They will greatly help to deepen and solidify your craft, and domain understanding.

Reproduce

Next, attempt to reproduce and implement the research papers in projects.

Network

Listen, ask, talk, and build meaningful relationships and collaborations with people involved in your field of specialization (domain experts, customers) for feedback, mentorship, and guidance. This will improve your understanding of the domain, and gain a familiar taste with their challenges and potential data science/ analytics use cases.

To take things to the next level, attend conferences, events, and hackathons, you will find mentors and possible collaborators to partner.

Be involved in an online/ physical domain-related reading group or community with colleagues/ students, to study academic literature, and help one another.

What contributed the most to get your internship offer at NVIDIA?

I could say that the proper data science and machine learning experience, and expertise gained from taking up real-world AI projects at Omdena, winning hackathons, building, and working with AI communities like deeplearning.ai, previous rigorous data science internships, and my current role provided an impressive background and skillset that interested recruiters from some top tech companies such as Microsoft Applied ML Software Engineering team, NVIDIA amongst others.

Throughout the rigorous rounds of interviews, I would probably say what contributed greatly to receiving the offer was my unique problem-solving mindset, analytical thinking, and approaches, based on how I responded, and tackled the various questions: technical, non-technical, use case related scenarios, and projects.

Most importantly, I thoroughly researched, read, and inquired internally on ongoing data science projects, research resources, articles, and ongoing work related to the role I was interviewing for.

One of the recruiters was quite impressed that I went beyond to greatly interact, utilize some of their products, and shared valuable feedback from my experience.

All these activities improved my understanding of the ongoing projects at NVIDIA, how they operate, function, are utilized, provided insights for potential data analytic use cases, and challenges from the perspective of the teams and people involved.

This provided a great baseline for our engagements and discussions with the recruiters. I gained insights, and intuition on contextual understanding of the data the teams work with, and how to utilize different data science technologies to solve related business problems that would later come up during the rounds of interviews.

Of course, it’s important to write good quality code, be technically competent, having a full understanding of what you are doing, and communicate effectively your work, and results, but there is more than that, and that’s what made me bag the offer.

What are your most important mindset related tips (as mental conditions like impostor syndrome are becoming a problem)?

The learning mindset: become a life-long learner, to stay relevant, adapt to changes in the AI field, agile, adaptable to tap into opportunities and prospects, and future proof your career. Constantly be building up your knowledge and expertise.

The focused mindset: remain disciplined and focused as you build your craft in the AI Space. Make that real progress in ML, don’t be swayed by exciting ideas, projects springing up daily, and by latest development trends. Stay motivated, focused, and build up skills.

The self-trust mindset: you’ve got to trust yourself, be courageous enough to follow your passion aggressively, and believe in your capabilities. The AI space is vast, with lots of information, difficult concepts to master, a sea pool of learners, and its challenges. Don’t give up on the things you believe in and want to achieve.

When it comes to Imposter Syndrome, the voice in your head can get very loud about how big a fraud you are and how little you know. It will focus on your shortcomings, ignoring your success. Shift it by getting louder about your achievements. For every new skill, you gain, celebrate in grand. Always remember this quote by Albert Einstein: “The moment you stop learning, you start dying”. That feeling will always be there, get better at overcoming, and dealing with it, using it as a motivation to improve yourself.

Often value comes from flipping a perceived weakness on its head and figuring out its opposite. Use imposter syndrome as your greatest strength, act on it in the right way, and improve.

In this highly evolving, dynamic field with a sea pool of learners, and career people transitioning into the field, What will separate you from the rest in the field is your problem-solving skills; work towards improving your creativity, concrete problem solving, and critical analytical thinking abilities through different ways, and by practice. Know how to find out answers, develop the solutions yourself, and how to derive the answers.

A book or course, that you most recommend to your fellow AI enthusiasts?

Ultralearning: Master Hand Skills, Outsmart the Competition, and Accelerate Your Career by Scott Young:

In a field that is highly dynamic, and evolving, where you’ve to be a lifelong learner. This is such a great read that will help us to become more pragmatic, relevant, re-invent ourselves, maximize our competitive advantage, adapt to changes in the AI space, and future proof of our careers in the data world.

Practical Natural Language Processing [Book] by Oreilly:

A very straightforward from the go book that does a great job in bridging the gap between Natural Language Processing (NLP) Research and practical applications. Covering from e-commerce, healthcare, finance, and other sought after domains where NLP is put into use, it’s such a great manual recommended for machine learning practitioners, data scientists, or anyone interested in the NLP field.

The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists:

A great read for aspiring and current data scientists to learn from the best. It’s a reference book packed full of strategies, suggestions, and recipes to launch and grow your own data science career.