So you want to be a data scientist? – Details, episodes & analysis

Chanin Nantasenamat is behind the very successful YouTube channel Data Professor. He is an actual professor with a background in working in academia in the field of bioinformatics. Since the beginning of the pandemic he has started his brand Data Professor and has been taking the data science scene by storm.

We talked about his background, how he decided to start his YouTube channel, how he built his YouTube channel to the size it is now, what future plans he has, his role at Streamlit and advice he can give to aspiring data science practitioners.

Data Professor YouTube channel: https://www.youtube.com/c/DataProfessor

Data Professor on Twitter: https://twitter.com/thedataprof

Chanin's Data Science Landscape: https://twitter.com/thedataprof/status/1557407932060663808

Amazon has many services and one of the biggest products they have is AWS. It stands for Amazon Web Services and is a cloud services platform. As part of their cloud services platform, Amazon also has skilled data scientists helping their clients implement data science solutions effectively and efficiently.

In this episode, I talk to one such data scientist: Naz Levent. Naz has been with Amazon for more than two years and has been part of some exciting projects in fashion, energy and entertainment industries. She even worked for F1 on a prominent project whose results were broadcast to hundreds of thousands of F1 followers.

She shares with us how she got into data science, how she got her first job, what her days look like and why she loves her job. She has some unusual and great advice on how to get ready for a data science career.

Take a listen!

And don't forget to go check out my courses at soyouwanttobeadatascientist.com/roadmap

One of the biggest worries before changing to data science is inexperience. It's natural to think that what you're doing right now is not relevant and that it's too late for you to switch to data science. But what you work on now does not stop you from pursuing a career in data science. In this episode, we learn about Meg's transition into data science after years in academia.

Meg is a senior data scientist at Spin, an e-scooter sharing company. She has started her work-life in ecology research. After working in academia for a while, she decided to change the course of her career and took the leap towards data science.

We talk about her journey, the resources she used, her work life, daily challenges she faces and much more in this episode. Don't miss it!

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Hands-on Data Science: Complete your first portfolio project course - https://www.soyouwanttobeadatascientist.com/hods

Data Science Kick-starter mini-course - https://www.soyouwanttobeadatascientist.com/courses/data-science-kick-starter-mini-course

Doing a PhD is a great way to dive very deeply into a topic you're passionate about. When it comes to AI there is no shortage of interesting topics. If you don't want to have the limitation of a company that needs to make profits and want to satisfy your curiosity about your passion, doing a PhD might be just the thing for you. This week I'm interviewing Selene Báez Santamaría.

Selene is a PhD student working on interaction between humans and robots. We discuss her journey towards starting a PhD, how she acquired her knowledge, what she did before starting her PhD and the details of her research. We also talk about how PhDs work, who it is for and how can you steer your career towards academia.

Curious about how data science and machine learning contributes to renewable energy research? In this episode, Sakshi Mishra from the National Renewable Energy Laboratory (NREL) joins me to talk about her journey and her work. NREL is part of the United States Department of Energy and its main goal is to innovate on renewable energy and energy efficiency.

We talk about how the lab comes up with research projects to work on, how she uses ML in these projects and how these projects are used in the real world. Apart from her work, we also talk about how she ended up where she is and what qualification she needed to get this job that quite honestly, sounds very dreamy.

Give it a listen to get inspired about applications of ML in research!

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Sometimes it’s hard to imagine a vast variety of jobs where data science skills and machine learning are used. So, we end up defaulting to a data scientist position. Shivali Goel’s job is an excellent example of a non-data scientist title where data science skills are highly used.

Shivali is a globalisation engineer at Adobe. She works on making sure that Adobe products are applicable and acceptable across the globe to different countries, cultures and languages. While doing so, she uses NLP and other ML techniques as her main tool.

This goes to show that you don’t only have to be a data scientist by title to be practicing data science and using the “cool” ML techniques in your work. 

Shivali says that her passion for languages, different cultures and NLP is combined in her position. Let’s hear it from her in this episode and go into the details of how she works.

Yaakov is a data scientist and theatre producer. He has experience working in start-ups and more recently as a free data scientist ready to make trouble. He has worked on some very interesting projects bringing art and data science together. 

We talk about his experience working in start-ups, how he comes up with his projects now and what he works on currently. Yaakov has great examples of bringing multiple disciplines together. This episode surely will widen your horizons on what kind of projects are possible in data science. 

Don’t miss it!

Nikola and Petr both work for the same company in Prague. DataSentics, where they work, has a wide selection of projects and a very interesting portfolio. As part of their jobs as data scientists, Nikola and Petr not only implement machine learning models for their clients but also act as consultants and help them from start to end of creating a whole product. That's a great example of the ideal case of being a data scientist.

We talk about how they ended up where they are, what kind of responsibilities they have day-to-day and how they like their work right now. This episode is special in the sense that we hear the experience of being a data scientist from two different perspectives!

Don't miss it!

On this week's episode, I welcome Katia Stambolieva, proud co-founder of the space tech company NinaSpace and a freelance data scientist. In NinaSpace, Katia and the team work on detecting wildfires from space and warn governments on time to mitigate the negative effect of these unexpected events. Apart from her work at her company, she also does freelance work primarily to help amplify the impact of social, civic or environmental projects. She truly is an inspiration for anyone who dreams of starting their own business and has a lot to teach about being a freelancer. 

Katia shares her journey to data science, business and space tech, we discuss the position of women in tech, talk about what it takes to be a data scientist and much more. Don't miss it!

In this week's episode, Jigyasa Grover of Twitter joins me to talk about her career and machine learning engineering. 

She shares her journey of becoming a machine learning engineer and the steps she took throughout the years. We dive deeper into the types of projects she works on, how she works with her team at Twitter and what she loves about her job. 

All this and more is on this week's episode. Don't miss it!

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Hands-on Data Science: Complete your first portfolio project course - https://www.soyouwanttobeadatascientist.com/hods

Data Science Kick-starter mini-course - https://www.soyouwanttobeadatascientist.com/courses/data-science-kick-starter-mini-course