Avsnitt
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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
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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
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Saknas det avsnitt?
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There are many ways to get a data science job and on this episode we hear a very interesting one!
Khuyen Tran is a student and a data scientist who got her first job before even graduating. On top of other things, we learn how this was possible. She shares her tips on how to stay engaged in the data science field and how to improve oneself by doing hands-on projects.
She tells me all about how she got her first job as a data scientist while still studying, how her writing on Medium brought a lot of opportunities to her foot, what resources she used to gain new skills and more!
Khuyen's is a great story to understand what to focus on when aiming to become a data scientist. She has some great advice on how to approach the whole data science field and how to think about learning new concepts.
Do not miss this episode!
Khuyen's medium account - khuyentran1476.medium.com/
Hands-on Data Science: Complete your first portfolio project course (previously Master the Data Science Method) - soyouwanttobeadatascientist.com/hods
Free Pandas cheat sheet - soyouwanttobeadatascientist.com/pandas-cheat-sheet
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Communities all over the world are on the rise. People want to connect with others that they have shared values with. People in these communities help their fellow members with hard times, follow each other's journeys and are there for each other through challenges. One such great community is Women in Data. Its goal is to increase diversity in data careers. But it’s more than a community. It “provides awareness, education and advancement to women in the tech industry - specifically in analytics, data science, machine learning and artificial intelligence” in their words.
Behind this organization is Sadie St. Lawrence. And she has a very interesting story on how this all got started. Being the only woman in her studies, Sadie felt the need for a community and built one from the ground up to help others who felt like her.
Now, in just 5 years, Women in Data is active in 11 countries.
Sadie is a data practitioner herself who has a background in data science and consultancy. In this episode, we talk about: Her journey into data science, how to approach getting into a data career, her advice for aspiring data scientists, what programs Women in Data has for aspiring data scientists, how to get in touch and become a part of the organization and much more!
Don’t miss this awesome episode!
Women in data website - https://www.womenindata.org/
Women in data YouTube channel - https://www.youtube.com/channel/UCwMkDNVrU2Vzc0dlJSdl7ww
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
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Yes, government agencies use AI too! When you think about it, with the gigantic amount of data they have, no wonder they need machine learning solutions to deal with their problems. And if you're passionate about working for the government or at least supporting it, this could be a great alternative for you!
This week on the show I have Nicole Janeway Bills. She works for Atlas Research, a consultancy firm that specializes in providing technical support to accomplish strategic transformations, improved performance, and enhanced service delivery for federal organizations in the US.
After realizing her passion in data analytics, Nicole followed her heart and started the journey that brought her to where she is today. Currently, Nicole's work involves Natural Language Processing and the latest technologies in AI which she admits to really enjoy.
We talk about how she decided to become a data scientist, what qualifications she went through, how she landed her first job, the future of AI and much more. Nicole has some great advice for anyone who is thinking of jumping into a career switch.
Don't miss it!
Here is Nicole's medium page where she writes about her learnings and current state of AI: https://nicolejaneway.medium.com/
Nicole's article on Python (from 30:07): 10 Underrated Python Skills
Hands-on Data Science: Complete Your First Portfolio Project course: https://www.soyouwanttobeadatascientist.com/mdsm
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This week on the show I have Mikiko Bazeley. She is currently busy co-founding Sidewalk.ai, an AI based solution for residential real-estate.
Her path to founding a company hasn't been very straightforward though. She has worked in many different companies, in many different domains before she got to where she is today. Her story is a great example of trying on different things and learning from your experiences to build yourself a life and a career that fulfills you.
We talk about sidewalk.ai, her story of becoming a data scientist, the projects she's involved with and the future of AI. Mikiko also shares her advice for aspiring data scientists on how they can turn their career around in the direction they want.
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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
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Data Scientists are in demand and that's why becoming a data scientist is also in demand. Though many times what people have in mind when they think of becoming a data scientist is working at a company, with a boss.
This doesn't have to be the case for everyone though. Ways you can do data science and in general, careers in data are too many to count. If you're really passionate about what you're doing and you value freedom above all else, like Susan, you might even start your own business and become your own boss.
Susan, also known as, The Classification Guru started her business after she realised that there is a gap in the market for people who would tidy up your data for you and make it ready for analysis. And now, her business is flourishing. She gets to work with fun use cases, have fun making marketing content for her work and make money on top of it.
In this episode we talk about her journey building her business, how she came up with an idea, what kind of services she offers to her clients, how she likes her job and many more things!
Tune in to learn more about the possibility of starting your own business and to get some inspiration!
The Classification Guru website - https://www.theclassificationguru.com/
The Classification Guru YouTube channel - https://www.youtube.com/channel/UCfrWRbL-zeEZsHwjYhj3v_A/videos
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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
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A consultant’s life is an exciting one. You get to experience many different industries, domains and technologies. It tends to be high-paced and at times stressful but if you like the kind of work you do, it is rewarding. Rossy is one such consultant. She works for a very big multinational tech consultancy firm. She specializes in finance and banking industries but has become part of other types of projects too.
Rossy joins me this week to discuss how she got to her position as a consultant data scientist, what she does day to day and what kind of projects she participates in. We also talk about why she chose data science, what she likes most about her job and much more.
Tune in to learn more about what you can expect from a consultancy position!
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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
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As a person who looked for jobs in the remote workspace before the pandemic, I can say, it is not easy. Fully remote companies tend to be, simply, super cool and have great culture. That’s why they get a lot of applications. This in turn makes the space super competitive.
When I, “re-met” Ceren 6-7 months ago, she was also looking for remote work. In the meantime, she actually found a remote position at an awesome company called Data Revenue and has been with them for the past couple of months.
In this episode, we hear how Ceren changed her career from being a logistics consultant to a machine learning engineer, which resources she used to learn data science, how she got her current position, her projects and more.
It's fair to note that with the latest developments in the world, as in a global pandemic, companies started seeing that remote work is actually possible. A lot of businesses have been temporarily or some even permanently moved to a remote working style. As a result, a lot of new opportunities for remote work opened up! This will make remote positions more accessible to a wider array of workers in the coming years. If you're interested in getting a remote position, let's hear it from Ceren who has been through it all!
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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
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This week I welcome Jayeeta Putatunda on the show! Jayeeta is a senior data scientist specialising in Natural Language Processing and Machine Learning solutions at Indellient Inc. in New York. She is equipped with all the skills a data scientist can have and works on very interesting projects with Fortune 100 companies. But of course, this doesn't happen overnight.
We talk about why she decided to pursue data science, how she got to where she is and how she studied to become a data scientist. She has some great advice for anyone who is looking to get into data science.
Take a listen to see what the path to data science looks like for someone whose starting point is economy!
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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
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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
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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.
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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.
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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!
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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!
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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!
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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
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In this episode, we look into one of the many data-related titles: product analyst. Kasia Rachuta is a product analyst at Square, a financial services company in San Francisco. She also held data science positions before and is able to compare the two easily.
We talk about how being a product analyst differs from being a data scientist, what her daily responsibilities are, what she loves about her job and what she did to get where she is right now.
All of this and more in this week's episode!
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Samantha joins me to talk about her career in the fourth episode of So you want to be a data scientist. She is a senior data scientist at Sentry.io in San Francisco. We talk about her journey from academia to business. We also discuss her daily responsibilities and what being a senior data scientist changed for her. Samantha has really helpful insights for aspiring data scientists and great advice for anyone transitioning careers to data science. We talk about all this and more, in this episode.
- Visa fler
