Data-Based Projections podcast

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הוסף לפני three שנים

תוכן מסופק על ידי Jim Harris. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Jim Harris או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

All About Change

1
Gary Sinise: Doing More for Our veterans with the Gary Sinise Foundation 27:29

לפני 15 ימים27:29

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אהבתי

27:29

Gary Sinise is an award winning actor, on the stage, TV and big screen. He is best known for playing Lieutenant Dan in Forrest Gump. Inspired by this role and his family members, Gary is now the head of the Gary Sinise Foundation, which offers support for service members who need help with mental wellness, trauma, physical recovery, and loss. He Also plays concerts worldwide for our nation’s defenders and their families, boosting morale and offering gratitude for their sacrifices as part of the Lt. Dan Band. Jay and Gary discuss the changing needs of American service members and their families, the many services the Gary Sinise Foundation provides, how Gary’s work helped him through personal loss and much more. Today's episode was produced by Tani Levitt and Mijon Zulu. To check out more episodes or to learn more about the show, you can visit our website Allaboutchangepodcast.com. If you like our show, spread the word, tell a friend or family member, or leave us a review on your favorite podcasting app. We really appreciate it. All About Change is produced by the Ruderman Family Foundation. Episode Chapters (0:00) intro (1:11) Veterans’ changing needs over the past half century (7:57) Veterans’ appreciation of Gary’s portrayal of Lt. Dan (10:25) By helping others, we step out of ourselves (11:46) The Lt. Dan Band (15:29) How the death of Gary’s son Mac impacts his activism (17:33) Bringing services to American heroes wherever they are (19:45) Accurate portrayals of veterans in film and TV (20:58) How can people get involved with the Gary Sinise foundation (24:24) Goodbye For video episodes, watch on www.youtube.com/@therudermanfamilyfoundation Stay in touch: X: @JayRuderman | @RudermanFdn LinkedIn: Jay Ruderman | Ruderman Family Foundation Instagram: All About Change Podcast | Ruderman Family Foundation To learn more about the podcast, visit https://allaboutchangepodcast.com/ Looking for more insights into the world of activism? Be sure to check out Jay’s brand new book, Find Your Fight , in which Jay teaches the next generation of activists and advocates how to step up and bring about lasting change. You can find Find Your Fight wherever you buy your books, and you can learn more about it at ⁠⁠www.jayruderman.com⁠⁠ .…

Data-Based Projections

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Data is often the basis for how we see the world, and how the world sees us. Understanding these data-based projections is the focus of this podcast, which discusses topics related to data analytics, machine learning, and data science. Produced and hosted by Jim Harris.

10 פרקים

#Tech #Jim Harris

Data-Based Projections

updated לפני 3 שנים

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10 פרקים

#Tech #Jim Harris

כל הפרקים

Data-Based Projections

1
That is Not Machine Learning 26:12

לפני 3 years26:12

26:12

Machine learning (ML) can provide unique analytical insights, as well as help automate some operational and decision-making processes more efficiently and effectively than non-ML alternatives. However, ML is also among the buzziest of buzzwords, and many are overselling and oversimplifying its usage. Do not let anyone frame a data analysis, business problem, or process improvement as an ML use case. Instead, say: That is Not Machine Learning — that is a data analysis, business problem, or process improvement where ML might be able to help. But not before we evaluate other options. And with the understanding that ML is rarely going to be either the first or only aspect of the solution. This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Machine Learning is Label Making 15:00

לפני 3 years15:00

15:00

Label Making. That is my simple two-word definition of Machine Learning. Machine Learning is Label Making. ML is LM. Especially supervised machine learning, which creates either numerical labels (using regression algorithms) to make predictions about a continuous data value (such as sale or stock prices), or categorical labels (using classification algorithms) to assign data to pre-defined groups also called classes (such as Fraud or Not Fraud for financial transactions). This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Cloudy with a Chance of Data Analytics 27:36

לפני 3 years27:36

27:36

Based on one of my presentations, this episode provides a five-part vendor-neutral framework for evaluating the critical capabilities of a cloud data analytics solution: Deploy, Store, Optimize, Analyze, Govern. This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Big Data Quality, Then and Now 29:49

לפני 3 years29:49

29:49

A decade ago, just before the beginning of the data science hype cycle was the big data hype cycle. At that time I had the privilege of sitting down with Ph.D. Statistician Dr. Thomas C. Redman (aka the “Data Doc”). We discussed whether data quality matters less in larger data sets, if statistical outliers represent business insights or data quality issues, statistical sampling errors versus measurement calibration errors, mistaking signal for noise (i.e., good data for bad data), and whether or not the principles and practices of true “data scientists” will truly be embraced by an organization’s business leaders. This episode is an edited and slightly shortened version of that discussion, which even though it is from ten years ago, I think it still provides good insight into big data quality, then and now. Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Three Questions for Data Analytics 12:44

לפני 3 years12:44

12:44

Before you get started on any data analytics effort, you need to have at least preliminary answers to three questions: (1) What problem are we trying to solve?, (2) What data can we apply to that problem?, and (3) What analytical techniques can we apply to that data? This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Machine Learning on Opening Day 9:23

לפני 3 years9:23

9:23

In time for opening day of the 2022 Major League Baseball (MLB) season, I discuss the initial results of my Baseball Data Analysis Challenge. See the extended show notes for links to my input data, my results as a Microsoft Excel file, and my SQL scripts on GitHub. I used logistic regression machine learning classification models to calculate win probabilities for the Boston Red Sox across nine (9) game metrics, and a Naïve Bayes machine learning classification model to predict individual game wins and losses with an associated probability. Think you can best my model? Game on! The baseball data analysis challenge continues. Play ball! Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Home Schooling your Machine Learning Model 11:39

לפני 3 years11:39

11:39

Why don’t more machine learning models graduate to production? Paige Roberts stops by to help explore this topic and drop some knowledge about how to get more machine learning models deployed in production. This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Data Science, Then and Now 32:22

לפני 3 years32:22

32:22

Back in 2012, Harvard Business Review declared Data Scientist was The Sexiest Job of the 21st Century. Less than a year later, I recorded a podcast discussion with an actual data scientist and Ph.D. Statistician, Dr. Melinda Thielbar , during which she discussed what a data scientist actually does and provided a straightforward explanation of key concepts, such as signal-to-noise ratio, how statistical results should be presented and explained to various audiences, uncertainty, predictability, experimentation, and correlation. This episode is an edited and slightly shortened version of that discussion, which even though it is from nine years ago, I think it still provides good insight into data science, then and now. Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Defining Data Analytics, Machine Learning, and Data Science 24:42

לפני 3 years24:42

24:42

Data Analytics, Machine Learning, and Data Science — those are the three things that this podcast focuses its discussions on. This episode provides my definitions in descending order of their complexity in terms of the depth of required knowledge, competencies, and practical, demonstrable skills related to computer science and programming, mathematics and statistics, critical thinking and overall approach to solving problems with data. My definitions also reflect a descending order of analytical advancement, because I see data science as advanced machine learning, and machine learning as advanced data analytics. This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

Data-Based Projections

1
Hello, World! 5:11

לפני 3 years5:11

5:11

Hello, World! Welcome to Episode Zero! Okay, technically it’s the first episode, but I’m a geek who thinks all indexes should start at 0 not 1. Anyway, this is more of a meta-episode introducing the host, explaining what the podcast is about, and letting you know what to expect from future episodes. The focus of this podcast is to discuss topics related to data analytics, machine learning, and data science. The goal is to provide a mix of information, education, thought leadership, and hopefully a little entertainment—so info-educa-thought-tainment. That’s a word. I just made it up. Which is okay since all words are made up. This episode is sponsored by: Vertica.com Extended Show Notes: ocdqblog.com/dbp Follow Jim Harris on Twitter: @ocdqblog Email Jim Harris: ocdqblog.com/contact Other ways to listen: bit.ly/listen-dbp…

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פודקאסטים ששווה להאזין

1
Gary Sinise: Doing More for Our veterans with the Gary Sinise Foundation 27:29

Data-Based Projections

Data-Based Projections

פודקאסטים ששווה להאזין

כל הפרקים

1
That is Not Machine Learning 26:12

1
Machine Learning is Label Making 15:00

1
Cloudy with a Chance of Data Analytics 27:36

1
Big Data Quality, Then and Now 29:49

1
Three Questions for Data Analytics 12:44

1
Machine Learning on Opening Day 9:23

1
Home Schooling your Machine Learning Model 11:39

1
Data Science, Then and Now 32:22

1
Defining Data Analytics, Machine Learning, and Data Science 24:42

1
Hello, World! 5:11

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פודקאסטים ששווה להאזין

Data-Based Projections

Data-Based Projections

פודקאסטים ששווה להאזין

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Amazon Basics Multipurpose Copy Printer Paper, 8.5 x 11 inches, 20 lb, 1 Ream, 500 Sheets, 92 Bright, White

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Amazon Basics Multipurpose Copy Printer Paper, 20 lb, 92 Bright, 8.5" x 11", White, 8 Reams, 4000 Sheets (500 Sheets/Ream)

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