Data Science Imposters Podcast

Latest Episodes:

Are you with me? The journey of a startup Aug 21, 2017

Show notes

This week we interview a technology and data company startup entrepreneur. Dwaine explains how a company is born out of an idea, evolves to meet the demands of customers, and survives through the hard work, sacrifice, and dedication of its team. We discuss how the financial crisis of 2007 and 2008, when the world seemed close to collapse, led to some people questioning their futures in the industry and others seeing opportunities. One of our listeners said : “….[I] thought I’d be a deer in headlights listening to you guys. However, I thought it was not only informative, but really engaging & reliable even to someone like me, who isn’t familiar with the lingo and industry.”

Becoming a Data Scientist with Renee Teate (@BecomingDataSci) Aug 14, 2017

Show notes

Renee Teate – an accomplished data scientist, creator of the ‘Becoming A Data Scientist’ podcast and website, and the voice behind @BecomingDataSci twitter name – joins us to discuss her own journey to become a data scientist, her quest to get others to join the field of Data Science, and what excites her about the future. Renee was an exceptional guest with great stories, good insight, and the stamina to keep Jordy and Antonio focused for an hour. Here are few hashtags to describe moments in this episode including #HarrisonburgNotHarrison #JMU #RosettaStone #Skynet #AI Check out these resources if you’re interested in more …

Becoming a Data Scientist Website
http://becomingadatascientist.com
- Here’s the talk Renee referenced, and powerpoint to go with it
- http://www.becomingadatascientist.com/2016/10/11/pydata-dc-2016-talk/
Becoming a Data Scientist Podcast (iTunes)
https://itunes.apple.com/us/podcast/becoming-a-data-scientist-podcast/id1076448558?mt=2 (audio only) and other podcast players
Becoming a Data Scientist Podcast (YouTube)
https://www.youtube.com/playlist?list=PLTnOXzOljuWZJo1IlaBcGM74P9vE0Uauc
Data Science Learning Club!
http://www.becomingadatascientist.com/learningclub/
Data Science Guide
http://www.datasciguide.com/
Beginner References:
http://www.datasciguide.com/recommended-resources-for-beginners/
Jobs For New Data Scientists
http://jobsfornewdatascientists.com

If you tell me who your closest friends are, I can tell you who you are Aug 09, 2017

Show notes

We’ve heard sayings like ‘if you tell me who your closest friends are, I can tell you who you are’ – the idea is that you and your friends have such similarities allowing you to form a group or cluster that may be different than strangers.

In this episode, we explore how we would group similar items together (clustering) using similarities (or distances). The unsupervised clustering algorithms (i.e. we do not provide test data indicating an existing relationship) that we discuss are k-Means and DBSCAN.

If Only I Had A Brain – Artificial Neural Networks Aug 07, 2017

Show notes

Antonio and Jordy talk about artificial neural networks; these are algorithms that today seem synonymous with Artificial Intelligence. These algorithms, first introduced in the late 1950s, which mimic how the brain works are now being used extensively. We casually explore the history of ANNs, how these algorithms work, and what they can do. We expect that this will be one of many conversations about artificial neural networks.

Keep the conversation going on Twitter @dsimposters.

**Ep. 0 – Who? What? Why? The Data Science Imposters Introduction** Aug 02, 2017
Show notes

We have danced around an introduction of ourselves and the show. Now that we have published 10 episodes, have a few more being edited, and have some great guests lined up we want to introduce ourselves and our thoughts about the show.

@BecomingDataSci said it best. If you haven’t already,check out her Twitter page and site.

**Ep. 10 – 1 way to lose $1,000 in a week: Cryptocurrencies** Jul 30, 2017
Show notes

Antonio plunges into the world of cryptocurrencies and buys enough Ether and Litecoin to lose $1,000 in a week. Bitcoin, Ether, and Litecoin cryptocurrencies are built on blockchain technology. Blockchain technology is like a decentralized ledger. While this is an exciting new frontier for the technology and these cryptocurrencies, there is still room for improvement. Hacks and thefts have threatened these cryptocurrencies and has created some skepticism for the underlying technology. Financial companies are optimistic and betting on blockchain technology’s success to improve their own processes.

Listen to our podcast, read these articles, and let us know what you think about the technology.

Block Chain Technology – http://www.businessinsider.com/what-is-blockchain-2016-3

Ethereum – https://blockgeeks.com/guides/what-is-ethereum/

Ethereum hack – https://medium.freecodecamp.org/a-hacker-stole-31m-of-ether-how-it-happened-and-what-it-means-for-ethereum-9e5dc29e33ce

7 Cryptocurrency Predictions From the Experts – http://fortune.com/2017/07/25/bitcoin-ethereum-cryptocurrency-predictions/

**Credit:** The screenshot for Litecoin pricing is from https://coinmarketcap.com/currencies/litecoin/ and the screenshot for Ether pricing is https://www.coindesk.com/ethereum-price/

Ep. 9 – Web scraping, APIs, and Programming Jul 24, 2017

Show notes

Jordy and Antonio discuss web scraping, APIs (application programming interface), and the benefits of programming solutions. They also talk about the challenges of combining data across multiple data sources (even if those data sets are open).

Beautiful soup is the premier solution for web scraping in Python. The following is an article that you will find helpful if you are interested in starting with the library: Web Scraping with Beautiful Soup.

Antonio created the following script to identify all of the people that he had emailed in the past few years: Python Script Utilizing GMail API. If you are interested in using it, you would need to follow some instructions here to get started with these APIs. As mentioned in the podcast, Antonio heavily leveraged the Google sample code.

Data sets are being created and shared by corporations and governments. Here are some examples:

The list goes on and on and only continues to grow.

Episode is archived here: http://traffic.libsyn.com/datascienceimposters/DSI_Episode_09.mp3

**Ep. 8 – Getting bad directions from Mr. Dijkstra** Jul 20, 2017
Show notes

In this episode, Antonio begins by discussing the seven hour car ride to Cape Code, MA and the eight hours spent on the return trip. This leads into a discussion about how Google and other companies use graphs to give us directions. Here’s a simplified graph created with Graph Online.

You can also use a free software package, GraphViz, to create these graphs. It requires a little bit more work but allows a lot more customization.

Book Recommendations

I highly recommend this book. It is technical but accessible to a wider audience than most of these books. (You can skip the coding sections and still get a good amount from this book)

Think Complexity – http://greenteapress.com/wp/think-complexity-2e/

Technical Details

The algorithm that we discussed in the podcast is Dijkstra’s algorithm.

If you are interested in reading a more advanced paper, consider reading the following dissertation:

Route Planning in Road Networks – http://algo2.iti.kit.edu/schultes/hwy/schultes_diss.pdf

Ep. 7 – You are making bad decisions. Jul 12, 2017

Show notes

Data Science Imposters take their show on the road to Cartagena, Colombia and partner with the Unobjective podcast team for their first collaboration. We tweeted this picture after recording and this one in our ad-hoc studio.

We decided to spend our time together talking about the way that we make decisions. We covered the following:

How do you make decisions?
Do you decide between a few options?
Do you think about what you want instead of the choices in your head?
Do you use math to make your decision?
How do you bias your decisions?
Do you rank your choices?
How do you think about chance (or probability) in your decision making?

Tweet us about your decision making process at @dsimposters

Home Purchase

In the show, I mentioned how I made my home purchasing decision. One major step was narrowing the selection to a few towns. While I cannot locate my final sheet, here’s a preliminary sheet that I used: https://docs.google.com/spreadsheets/d/1ii3x2s5xrdw6FSY56DJC-grjyi8aL3-DrV0GA06A51o/edit?usp=sharing

What would you improve?

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