The Art of Digital War: Enter the Blockchain
Jul 30, 2019
Show notes
Would you give up Control for Convenience? Blockchain will protect you.
Vikal Kapoor joins us on this conversation. Vikal is an experienced entrepreneur whose latest venture as CEO of Dapps is solving customer experience problems using Blockchain technology. Specifically, they are building customer relationship management (CRM) software on top of an Enterprise Blockchain computing platform.
Vikal reminds us that for every convenience that we are afforded by technology, we may be losing a bit of control – control of ourselves and our data. Our Twitter poll taken by 12 people shows that more would give up control for convenience. We think that percentage is much higher (our poll was just very limited).
Vikal paints Blockchain as the hero we didn’t know we needed. Is he right? Only time will be able to tell.
Democratize your data. Do this first to get real results…. (w/ Leigha Jarett)
Jul 15, 2019
Show notes
Your company wants to catch up with the buzz of data science, machine learning, or artificial intelligence. What do you do first? Hire a PhD in Physics to build artificial intelligence algorithms? Hire a team of consultants?
Leigha Jarett join us to give us her view. As an employee of Looker, she’s had this conversation with her clients many times. Democratize your data.
As always, we get to know our interviewee and hopefully Leigha teaches Antonio how to perform a hockey stop because we’re tired of him crashing to stop.
In this data science podcast, we mix technology, data science, AI, and a bunch of other data in a casual conversation.
How do I become a serial tech entrepreneur? (with Elissa Shevinsky @ElissaBeth)
Jul 01, 2019
Show notes
Elissa Shevinsky, CEO of Faster Than Light, gives us some insight about entrepreneurship in technology. As a successful entrepreneur, she’s seen a few things and has gained much knowledge on entrepreneurship which she shares on this episode.
Join us as she walks us through the journey of starting multiple technology companies.
Give us 1 star.
Jun 17, 2019
Show notes
Antonio and Jordy talk about the company in California that’s so fed up with Yelp that they have requested 1 star reviews – they’ve almost made it mandatory given that they give 50% off their otherwise very expensive pizza pies.
Their conversation then devolves into other tangents.
What’s a p-value and can you trust it?
Jun 10, 2019
Show notes
The notes below are inside look on how we structured this week’s episode …
If your p-value is higher than .05, you can’t publish research.
Antonio: What is a p-value?
Jordy: The P value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H0) of a study question is true
Antonio: Okay, so if you’re like me … not a statistician … you want to have simpler language even if the explanation is longer. I reread that and think man … what is this null hypothesis? That’s really what got me hung up. How about you Jordy?
Jordy: ….
Antonio: So, the null hypothesis is that whatever you are trying to test has no significant difference from the population. Your hypothesis, whatever you are trying to prove,is called the alternative hypothesis.
Okay, so let’s make up an example. I’ll use Penn State’s List of 7 steps.
Define null hypothesis
So, let’s make up a null hypothesis – College freshmen students gain an average of 10 lbs during their first year of college. Let’s say we have the standard deviation of 3 lbs. Our null hypothesis is that there will be no significant difference between this population and our sample.
2. Define alternative hypothesis
Our alternative hypothesis is that students that are given an electronic scale at the beginning of their college year will impact their weight by the end of the year.
3. Set probability / alpha
.05 – 5% ?
Why would you make this number lower than 5% ? What if you get it wrong?
4. Collect Data
Experimental or Observational
5. Calculate the test statistic
Okay, I think where the Statistician magic happens the most. Essentially, this statistic measure compares the data that we collected or observed compared to our overall population.
I say that the most magic happens here because you need to know a bit about the distribution of the data and the pluses and minuses of each statistic. You then need to plug in the data to the formula – even I can do that part.
6 / 7 – Based on that measure – which is sometimes just how many standard deviation a our data is from the population – we then need to measure the likelihood of that
Now you have the likelihood on it and then you compare if that’s lower than your alpha value. If it is, you can now reject the Null Hypothesis.
….
It sounds decent to me so why is there such issue with p values? Well, I think when people are given one measure for success, they’ll figure out how to beat it, fudge it, or bend the rules a bit.
Inflation bias, also known as “p-hacking” or “selective reporting,” is the misreporting of true effect sizes in published studies (Box 1). It occurs when researchers try out several statistical analyses and/or data eligibility specifications and then selectively report those that produce significant results [12–15].
Common practices that lead to p-hacking include:
conducting analyses midway through experiments to decide whether to continue collecting data [15,16];
and stopping data exploration if an analysis yields a significant p-value [18,19].
recording many response variables and deciding which to report postanalysis [16,17],
deciding whether to include or drop outliers postanalyses [16],
excluding, combining, or splitting treatment groups postanalysis [2],
including or excluding covariates postanalysis [14],
According to one paper we found,
The Extent and Consequences of P-Hacking in Science, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured.
270 authors work to repeat 100 experiments. ‘Even with all the extra steps taken to ensure the same conditions of the original 97 studies only 35 of the studies replicated (36.1%), and if they did replicate their effects were smaller than the initial studies effects.’
Climate Change – how can we use data?
Jun 03, 2019
Show notes
Two days after recording this, we saw the article ‘Trump Administration Hardens Its Attack on Climate Science’ – https://www.nytimes.com/2019/05/27/us/politics/trump-climate-science.html
Climate Change is a topic that requires arguments based on data not rhetoric. Science not politics. Facts not opinions.
During our conversation, we referenced the following:
Starting a Podcast – DSI Guide
May 20, 2019
Show notes
Here’s our simple, straightforward guide to starting a podcast. Yes, you can do it.
For our hardcore data science listeners, we’ll be back next week.
How does New York City use data?
May 13, 2019
Show notes
Dr. Alaa Moussawi joins us to talk about how the NYC Council is leveraging data and how this data is helping New Yorkers along the way.
NYC makes their data available so you too can explore it and answer the questions that you care about – https://opendata.cityofnewyork.us/
To learn more about Alaa’s Data Science Team, visit https://council.nyc.gov/data/
Predicting crimes without bias and prejudice – is it possible?
May 06, 2019
Show notes
Predictive policing companies have received some unwanted attention. Police departments are minimizing their use, if not eliminating these programs altogether.
Predictive policing aims at leveraging more data when determining crime areas, identifying perpetrators, and allocating resources.
What do you think? Tell us on Twitter @dsimposters or email us at jordy@datascienceimposters.com
DSI Week in Data Science-y News with Miguel Garcia
Apr 29, 2019
Show notes
Our friend, Miguel Garcia, pops in on this episode. We always love having Miguel on the show. This is especially true today when we get his take on the latest news events we are covering in this episode.
While we have Miguel on he talks to us about his role as a Senior Solutions Engineer at Looker. Miguel also tells us about his journey from a risk analyst to his new role and what helped him make that transition.
We discuss the Professional Engineer’s Creed and whether the Data Science industry needs a similar oath.
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