25: Talking Data Privacy with Jeff Bermant

We talk with Michel Tricot, who is the Founder and CEO of Airbyte, which is an open source data integration Y Combinator startup. It has raised over $30M in capital and has been growing quite fast. It was a great conversation and I think you will also enjoy it. 🎉 We cover lots of things in the podcast including:  1. Technical aspects of what Airbyte does, how it sits in the ETL/ ELT landscape, how it differs from other tools such as Fivetran, Stich etc.  2. Data Warehouses being a canonical source of data and how Airbyte helps with bringing the data into the warehouse.  3. How Airbyte works as an open source data tool.  4. Life aspects of running a fast growing start-up including raising capital, hiring etc.  Links to the tools/ services mentioned:  1. Airbyte: airbyte.io 2. Airbyte Slack where you can talk with the team: slack.airbyte.io  3. Dbt for transformation in ELT: getdbt.com  4. Airflow which is a data orchestration tool: https://airflow.apache.org/ 5. Astronomer which can host Airflow: https://astronomer.io/  Pay as you use data warehouses:  6. Snowflake Data Warehouse: https://www.snowflake.com/ 7. BigQuery Data Warehouse: https://cloud.google.com/bigquery  Set up your own infrastructure:  8. Redshift Data Warehouse: https://aws.amazon.com/redshift/ 

Imagine you are at a beach and you are hanging out and seeing all the waves come and go and all the shells on the beach. And you get an idea. How about you collect these shells and make necklaces to sell? Well how would you go about doing this? Maybe you’d collect a few shells and make a small necklace and try to show to your friend. This is where we begin our journey on learning about data engineering pipelines.  Using an example of running a necklace business from shells - we learn about the following data engineering concepts:  1. ETL - Extract Transform Load vs ELT - Extract Load Transform concepts. Why Data Warehouses are great for analytics.  2. Spark for large data processing and hosting / running 3. Data orchestration using Airflow My blog on Towards Data Science about moving from Pandas to Spark: https://towardsdatascience.com/moving-from-pandas-to-spark-7b0b7d956adb  Great book to learn about Spark: https://www.amazon.com/dp/1492050040/?tag=omnilence-20  Tools covered in the episode:  dbt: https://www.getdbt.com/  Databricks: https://databricks.com/ EMR: https://aws.amazon.com/emr/ AWS Redshift: https://aws.amazon.com/redshift/ Snowflake: https://www.snowflake.com/ Delta Lake: https://databricks.com/product/delta-lake-on-databricks 

In this episode, I'm excited to be talking with Jeff Bermant, who is the founder and CEO of Cocoon Mydata Rewards browser. It is a browser based off Chrome and it pays people to use it! ✨  In this episode we talk about data ethics and privacy, and how Jeff believes that users should be paid for their data. We talk about GDPR and similar laws in US, future of data privacy and more!  Go to https://getcocoon.com to download and use Cocoon Rewards Browser.  ~Thanks for listening~

In this episode, we are talking about women in tech with Rupal Gupta. Rupal, a recent graduate from Online MS in CS from Georgia Tech, is a data engineer in the industry and is passionate to help promote women in tech. She also has some great tips and resources for anyone trying to break into data science and tech!  In this episode we talk about things that can help promote women in tech, women in tech conferences such as Grace Hopper, looking for jobs, resources to prepare for the interviews etc.  If you want to reach out to Rupal for any help or to collaborate with her project womenmentors.co, here is her LinkedIn: https://www.linkedin.com/in/rupalgupta15/  FREE Women in Tech Conference by Manning Publications on Oct 13th at 12pm ET on Twitch: https://freecontent.manning.com/livemanning-conferences-women-in-tech/ 🎉 There will be women in tech speakers from Dropbox, Microsoft, Warby Parker and more. 🌟 Programs and conferences covered in the episode: OMSCS program at Georgia Tech: https://omscs.gatech.edu/ Grace Hopper conference: https://ghc.anitab.org/ Anita Borg Institute: https://anitab.org/ 🌟 Interviewing resources: 1. Pramp: https://www.pramp.com/#/ 2. Interviewing.io: https://interviewing.io/ 3. Educative "Grokking the System Design Interview": https://www.educative.io/courses/grokking-the-system-design-interview 4. AWS Certifications: https://aws.amazon.com/certification/ Disclaimer: All opinions on this podcast are our own and not the views of our employers or organizations. ~Thanks for listening~

In this episode, we talk about Amazon SageMaker and how it can help with ML model development including model building, training and deployment. We cover 3 advantages in each of these 3 areas.  We cover points such as: 1. Host ML endpoints for deploying models to thousands or millions of users. 2. Saving costs for model training using SageMaker. 3. Use CloudWatch logs with SageMaker endpoints to debug ML models.  4. Use preconfigured environments or models provided by AWS. 5. Automatically save model artifacts in AWS S3 as you train in SageMaker.  6. Use of version control for SageMaker notebooks with Github. and more…  Please rate, subscribe and share this episode with anyone who might find SageMaker useful in their work. I feel that SageMaker is a great tool and want to share about it with data scientists.  For comments/feedback/questions or if you think I have missed something in the episode, please reach out to me at LinkedIn: https://www.linkedin.com/in/sanketgupta107/

In this episode, we are talking with Paul Azunre. Paul is one of the world’s experts in the area of Transfer Learning for NLP and is also an author of the upcoming book Transfer Learning for NLP published by Manning Publications. In this episode we talk about things such as:  1) Paul’s background and how his background in maths and optimization as well as fake news detection got him started in transfer learning in NLP. 2) How Paul got started with the book, book writing process as well as tips to the listeners for writing a technical book. 3) High level summary of transfer learning in both computer vision and NLP and why this is the ImageNet moment of NLP. 4) Why ML and NLP practitioners today should be excited about transfer learning (such as how students in Ghana are able to build their own Google Translate using transfer learning) 5) How BERT, ELMo and ALBERT work at the high level and how they differ from traditional techniques like Word2Vec or FastText. 6) Differences between BERT, ELMo and ALBERT. 7) What makes Paul’s new book a must-read for anyone interested in this field.  ✨Paul's Info👇 Paul’s Website: azunre.com (with all social media handles) Please reach out to Paul if you have any questions about transfer learning in NLP or the book. ✨Chance for one of 2 free copies of Transfer Learning for NLP 🎉 Get a chance to win the free copy of Paul's book! Please share this episode on Twitter and add my Twitter handle "sanket107" to it, you will get a chance to win one of 2 free books. My Twitter: https://twitter.com/sanket107 ✨Discount Code for all Manning Publications books! 🎊🤩 Special Link to get extra discount for Paul’s book: https://www.manning.com/books/transfer-learning-for-natural-language-processing?a_aid=Omnilence&a_bid=d53fed17 As The Data Life Podcast listeners, you can also go to this link http://www.manning.com/?a_aid=Omnilence to get any Manning book with 40% discount with the code: poddlife20 This will help support this show as well and is much appreciated. Thank you Manning Publications and Paul as well as sponsors to make this show a reality.  ~Thanks for listening~

In this episode, we talk about why the two libraries Scikit-Learn and Keras are great for machine learning. These two libraries combined with Pandas form the 3 core libraries in Python for a data scientist today.  We cover things like: 1)  Data Exploration and data cleaning - how Pandas and Jupyter notebooks provide a good way to get started here. 2) Data Transformation - how Scikit-Learn provides many useful functions like train_test_split, Scalers, PCA etc. 3) Data Fitting - how Scikit-Learn provides good shallow models and Keras provides great support to quickly get started with neural networks. We also cover various tidbits on things to take note in building ML pipelines and preparing models to be deployed in production, so tune into the episode to find out! Fantastic Resources: 1) Book by head of Youtube DS team Aurelien Geron: https://www.amazon.com/dp/1492032646/?tag=omnilence-20 This is one of the best book I have read on this topic as it covers practical tips incl. Scikit-Learn API etc. 2) Developing Scikit-Learn estimators: https://scikit-learn.org/stable/developers/develop.html 3) Guide to Keras Sequential API: https://keras.io/getting-started/sequential-model-guide/ 4) Guide to Keras Functional API: https://keras.io/getting-started/functional-api-guide/ 5) My previous episode on Pandas: https://podcasts.apple.com/us/podcast/17-why-pandas-is-the-new-excel/id1453716761?i=1000454831790 Thanks for listening! Please consider supporting this podcast from the link in the end.

In this episode, we talk with Akshay Kanade. He is a business analyst working in New York City who likes taking a big view of data, and has very interesting spiritual views on data analytics and life in general, he is also a handwriting expert- he can read people’s handwriting and can recognize a lot about their personalities. In this interview we will cover several things such as:  - How has been an analyst influenced Akshay's life?  - Introspection about data and analytics - Taking high level view of data - connecting deep learning with deep thinking - People who don’t have background in analytics- how they can use their unique backgrounds for decisions  - Power of consciousness and spirituality at work  - Hand-writing analysis and whether it is a science or an art It was a fascinating conversation, and I took a lot away talking with Akshay's view points. This interview is a must-listen if you deal with data and analytics in your work.  Akshay's hand-writing analysis and mentorship website: www.pradnyatantra.com (will be live soon) Reach Akshay on LinkedIn at https://www.linkedin.com/in/akshaykanade06/ Some of Akshay's favorite books: 1. Autobiography of a Yogi https://www.amazon.com/dp/8120725247/?tag=omnilence-20 2. The Monk Who Sold His Ferrari https://www.amazon.com/dp/0062515675/?tag=omnilence-20 3. Mastery https://www.amazon.com/dp/B00A6G9CGG/?tag=omnilence-20 To add to this list, one of my favorite books is: The Power of Now https://www.amazon.com/dp/B00A6G9CGG/?tag=omnilence-20 If you have any feedback drop me a note at [email protected] or reach me on LinkedIn at https://www.linkedin.com/in/sanketgupta107/ ~ Thanks for listening~ 

In this podcast episode, we do an interview! We talk with Patrick McClory, who is the founder and CEO of IntrospectData. He is an expert working in areas of data science consulting, large machine learning projects, math, statistics and more. In this episode we cover several interesting topics such as: 1) What makes a good data scientist? 2) The different roles in the industry such as data engineer, machine learning engineer, data analyst etc. 3) The first mile problem: Data ownership and ethics of data collection. Patrick can be reached at [email protected] and you can read more about IntrospectData's projects at https://introspectdata.com/  Some books discussed in the episode:  1. The Field Guide to Understanding Human Error  2. Information Theory: A Tutorial Introduction  If you enjoyed this episode or have any feedback drop me a note at [email protected] ~ Thanks for listening ~

What should you consider for pursuing MS in US? There might be several questions in your mind as you explore this question. In this episode we cover some of the main things to consider before you make the decision. I also go into details about things which I wish I knew before coming to US for MS.  The things I cover in the podcast are to consider for MS in US are:  1)  Location matter more than rankings. 2) Talk to professors before applying. 3) Culture of hard work, and advantage of having prior work experience. 4) Cost is High and low cost alternates. 5) Visa situation is uncertain. Hope you enjoy this episode, this was an episode that I wish I listened to before flying to US. Reach out with your questions/feedback at [email protected] Resources: Although I did not cover GRE or TOEFL topics in detail, I am linking to some great resources for their preparation. 1) Essential Words for the GRE https://www.amazon.com/dp/1438007493/?tag=omnilence-20 2) GRE Prep Guide by Kaplan https://www.amazon.com/dp/150624890X/?tag=omnilence-20 3) GRE Guide by Barrons https://www.amazon.com/dp/1438009151/?tag=omnilence-20 4) TOEFL Guide by Barrons https://www.amazon.com/dp/1438076258/?tag=omnilence-20 5) Blog version of this podcast episode https://medium.com/the-data-life/ms-in-us-for-data-science-57079509ded9 Thanks for listening. Please support us via the link in the end for Anchor Payments. It would allow us to build more of this content!