Deep Data Dive

Deep Data Dive
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  • NOTÍCIAS DE TECNOLOGIA
  • DUAS VEZES/SEMANA

We're two data enthusiasts taking weekly deep dives in the world of big data. Follow us on Twitter & watch us on YouTube!

Episódios

  1. 12/12/2021

    How To Get Into Data Science, Part 2 w/ Guest, Jonathan Bechtel

    This episode is part 2 of our discussion with Jonathan Bechtel, a data science instructor at General Assembly. In this episode we  tackle the following topics:   • The job application/hiring process for data science       - Do bootcamps and graduate programs offer a career services office?       - Training for technical screenings       - How long does a job search take?   • How is applying into a data science job different now vs 3 or 4 years ago?  😎ABOUT US: Laura and Vijay are data scientists who enjoy talking about data and sharing their experiences, which is why they started this podcast. Laura worked in credit risk modeling and experimental design. She is also an amateur silversmith. Vijay worked on Deep Learning NLP models and recommender systems. He also loves learning new things, like French. On Deep Data Dive, they will share their work experiences, discuss data science fundamentals, and chat with guests, and more. 👍🏼 PODCAST MISSION STATEMENT: Welcome to Deep Data Dive! Data is the new oil and data scientist is the sexist job of the 21st century (at least according to Harvard Business Review…). If you want to learn more, you’re in the right place! Follow us as we discuss our work experiences, review key data science concepts, learn new concepts, and chat with guests. We will tell you everything they know about the field, getting a job as a data scientist, and what the job is like. We hope you will follow our journey, have some fun, laugh, and learn some data science along the way. 🎙PODCAST (Streaming FREE on 17+ platforms!)    • Amazon Music: https://bit.ly/DDD-Amazon    • Apple Podcast: https://bit.ly/DDD-Apple    • Spotify: https://bit.ly/DDD-Spotify    • iHeart Radio:https://bit.ly/DDD-iHeartRadio    • Google Podcast: https://bit.ly/DDD-Google 🗣 LET'S CONNECT: Twitter: @DeepDataDive (www.Twitter.com/DeepDataDive)  *Tweet us your questions and comments! We want to engage with our audience. 🌐 VISIT OUR WEBSITE:  www.DeepDataDive.live 👥 HOSTS: Laura & Vijay 🎥 VIDEO PRODUCTION: Dana Donovick, Passion Possible, LLC. dana@passionpossible.com • 206-222-0740 https://twitter.com/PassionPossible https://twitter.com/DanaDonovick

    38 min

  2. 12/12/2021

    How To Get Into Data Science, Part 1 w/ Guest, Jonathan Bechtel

    Special Guest: Jonathan Bechtel, data science instructor at General Assembly (Worked with multiple companies in NYC and other cities) (Lots of work with programmatic data extraction and pipelining) How did we all get into data science?   • Jonathan, Vijay, Laura Different ways to get into data science   • Bootcamp programs   • Traditional masters   • Self Taught 😎ABOUT US: Laura and Vijay are data scientists who enjoy talking about data and sharing their experiences, which is why they started this podcast. Laura worked in credit risk modeling and experimental design. She is also an amateur silversmith. Vijay worked on Deep Learning NLP models and recommender systems. He also loves learning new things, like French. On Deep Data Dive, they will share their work experiences, discuss data science fundamentals, and chat with guests, and more. 👍🏼 PODCAST MISSION STATEMENT: Welcome to Deep Data Dive! Data is the new oil and data scientist is the sexist job of the 21st century (at least according to Harvard Business Review…). If you want to learn more, you’re in the right place! Follow us as we discuss our work experiences, review key data science concepts, learn new concepts, and chat with guests. We will tell you everything they know about the field, getting a job as a data scientist, and what the job is like. We hope you will follow our journey, have some fun, laugh, and learn some data science along the way. 🎙PODCAST (Streaming FREE on 17+ platforms!)    • Amazon Music: https://bit.ly/DDD-Amazon    • Apple Podcast: https://bit.ly/DDD-Apple    • Spotify: https://bit.ly/DDD-Spotify    • iHeart Radio:https://bit.ly/DDD-iHeartRadio    • Google Podcast: https://bit.ly/DDD-Google 🗣 LET'S CONNECT: Twitter: @DeepDataDive (www.Twitter.com/DeepDataDive)  *Tweet us your questions and comments! We want to engage with our audience. 🌐 VISIT OUR WEBSITE:  www.DeepDataDive.live 👥 HOSTS: Laura & Vijay 🎥 VIDEO PRODUCTION: Dana Donovick, Passion Possible, LLC. dana@passionpossible.com • 206-222-0740 https://twitter.com/PassionPossible https://twitter.com/DanaDonovick

    52 min
  3. Episode 3: Machine Learning

    15/08/2021

    Episode 3: Machine Learning

    Episode 3 : Data science and Machine Learning In this episode, we juxtapose the different types of Machine Learning: supervised, unsupervised, semi-supervised, and reinforcement. What is the difference between data science and machine learning? Machine learning is a sub-discipline within data science Machine learning is an application of artificial intelligence Machine learning is predictive analytics Prescriptive analytics and recommending a path forward The different types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning Supervised machine learning: data is labeled and we know the outcome, using a predictive model and comparing your predicted results to the labeled outcomes. Classic examples of churn and spam vs non-spam Classification vs regression problems and how they differ Unsupervised machine learning: data is not labeled and we don’t know the outcome. Clustering using a distance metric is involved, and we can get the attributes of different clusters Latent dich allocation (LDA) for topic modeling (categories not defined beforehand) Semi-supervised machine learning Turning an unsupervised program into a supervised learning problem How do we determine accuracy and precision of semi-supervised machine learning? Reinforcement learning Self-driving cars examples A car will make mistakes, a penalty system will prevent those mistakes from re-emerging in the future The AI agent continuously learns based on a set of penalties that are imposed There will be a growth in reinforcement learning in the future 🎙PODCAST: Listen on the go, free! Spotify  •  Amazon Music  •  Google Podcast  •  iHeart Radio 👥ABOUT US: Laura and Vijay are data scientists who enjoy talking about data and  sharing their experiences, which is why they started this podcast. Laura  worked in credit risk modeling and experimental design. She is also an  amateur silversmith. Vijay worked on Deep Learning NLP models and  recommender systems. He also loves learning new things, like French. On  Deep Data Dive, they will share their work experiences, discuss data  science fundamentals, and chat with guests, and more. 📱LET'S CONNECT: Twitter: @DeepDataDive (www.Twitter.com/DeepDataDive) 🎥VIDEO PRODUCTION & DESIGN: Dana Donovick, Passion Possible, LLC. dana@passionpossible.com • 206-222-0740

    22 min
  4. Episode 2: What is/isn't data? + Debate: Skills vs Degree

    15/08/2021

    Episode 2: What is/isn't data? + Debate: Skills vs Degree

    📺 WATCH THIS EPISODE: https://youtu.be/8yGucN6FUTI What looks like data science but isn't? Data science is interdisciplinary and one needs to master multiple skills such as math, statistics, programming, some domain knowledge These skills in isolation are not data science, and can be used for other fields in different ways The need to derive insights and act on them You need good data to get good insights (garbage in, garbage out) Real-world data is not perfectly ready for data science and a data scientist’s job will involve data cleaning What skills does one need to be a data scientist and is there only one way to get there? There are multiple paths into data science but statistics, math, programming, and domain knowledge are all needed The interview process for becoming a data scientist is designed to assess a candidate’s skills On-site and take-home data challenges are part of the interview process for all candidates regardless of background What are the general job requirements for a data scientist? Most job listings will require at least a Bachelors or Masters in a quantitative discipline Need to have proficiency in Python, Java, or C++ Familiarity with cloud services is a nice-to-have What ways are there of getting into data science? Traditional masters or doctoral programs Programming bootcamps Self-learning via free online courses Will Massive Open Online Courses (MOOCs) change anything? Learning and staying updated Keeping up with the newest tools and techniques as part of a data scientist job Vijay describes his master’s experience Laura reflects on her educational experience 🎙PODCAST: Listen on the go, free! Spotify  •  Amazon Music  •  Google Podcast  •  iHeart Radio 👥ABOUT US: Laura and Vijay are data scientists who enjoy talking about data and  sharing their experiences, which is why they started this podcast. Laura  worked in credit risk modeling and experimental design. She is also an  amateur silversmith. Vijay worked on Deep Learning NLP models and  recommender systems. He also loves learning new things, like French. On  Deep Data Dive, they will share their work experiences, discuss data  science fundamentals, and chat with guests, and more. 📱LET'S CONNECT: Twitter: @DeepDataDive (www.Twitter.com/DeepDataDive) 🎥VIDEO PRODUCTION & DESIGN: Dana Donovick, Passion Possible, LLC. dana@passionpossible.com • 206-222-0740

    27 min
  5. Episode 1: Introduction + What is Data Science?

    15/08/2021

    Episode 1: Introduction + What is Data Science?

    EPISODE SUMMARY: What is Data Science? Episode 1: What is Data Science? In this episode We discuss the definition of Data and how it relates to Data Science. We also touch upon the ethics of Data Science and the possible futures for it. Brief introductions What is data? Kid’s height in doorway example Number of steps in a fitness tracker example Measured record Importance of metadata How does it relate to Data Science? Making sense of data Taking action based on data It’s an emerging field in its infancy Data science in the news HBR calling it the sexiest job in the 21st century in 2012 Number of data science job listings per year have exploded Training programs for data science Data and data science are tools used by people 📺 WATCH THIS EPISODE: https://youtu.be/rH9pgq2XHSw 🎙PODCAST: Listen on the go, free! Apple Podcast  •  Spotify  •  Amazon Music  •  Google Podcast  •  iHeart Radio 👥ABOUT US:  Laura and Vijay are data scientists who enjoy talking about data and  sharing their experiences, which is why they started this podcast. Laura  worked in credit risk modeling and experimental design. She is also an  amateur silversmith. Vijay worked on Deep Learning NLP models and  recommender systems. He also loves learning new things, like French. On  Deep Data Dive, they will share their work experiences, discuss data  science fundamentals, and chat with guests, and more.   🎙PODCAST MISSION STATEMENT:  Welcome to Deep Data Dive! Data is the new oil and data scientist is the  sexist job of the 21st century (at least according to Harvard Business  Review…). If you want to learn more, you’re in the right place! Follow  us as we discuss our work experiences, review key data science concepts,  learn new concepts, and chat with guests. We will tell you everything  they know about the field, getting a job as a data scientist, and what  the job is like. We hope you will follow our journey, have some fun,  laugh, and learn some data science along the way. 📱LET'S CONNECT: Twitter: @DeepDataDive (www.Twitter.com/DeepDataDive)  *Tweet us your questions and comments! We want to engage with our audience. 🙅🏾‍♂️🙅🏻‍♀️HOSTS: Laura & Vijay 🎥VIDEO PRODUCTION & DESIGN: Dana Donovick, Passion Possible, LLC. dana@passionpossible.com • 206-222-0740 Twitter: @PassionPossible @DanaDonovick

    21 min
  • 5 episódios

Sobre

We're two data enthusiasts taking weekly deep dives in the world of big data. Follow us on Twitter & watch us on YouTube!

Informações

  • Criado por
    Deep Data Dive
  • Anos de atividade
    2 mil
  • Episódios
    5
  • Classificação
    Livre
  • Copyright
    © Deep Data Dive
  • Site do podcast
    Deep Data Dive