Debug Data
Talk Data:
{
"code": "BUP9MZ",
"title": "Stats Meets ML - What I learned from my Machine Learning Certification",
"speakers": [
{
"code": "ADEDDZ",
"name": "James Donahue",
"biography": "Raised in Nashville, Tennessee, USA, I have bounced around the world, finally settling in Hamburg, Germany. My professional background reflects my vagabond nature. I taught English in Asia, Latin America, and online (full-time remote work before Covid!), worked in adventure tourism in Appalachia (USA) and Chile, and did my masters in Economics in Hamburg. Somewhere along the way, I gathered a few stories and soft skills.\r\n\r\nI met Python during my masters, but stuck with Matlab through the my PhD coursework, until I decided that academia is not my forever home. Currently I am occupying myself with ensemble methods such as XGBoost, as well as expanding into computer vision with PyTorch and toying with more advanced data visualizations. Naturally, NumPy will always hold a special place in my heart, along with the Cython package to sample everything a Bayesian needs.",
"submissions": [],
"avatar_url": "https://pretalx.com/media/avatars/ADEDDZ_G8dK3qa.webp",
"answers": [
492000,
492001
],
"email": "jimbodonahue@gmail.com",
"timezone": "UTC",
"locale": "en",
"has_arrived": false,
"availabilities": [
{
"start": "2026-04-08T00:00:00+03:00",
"end": "2026-04-11T00:00:00+03:00",
"allDay": false
}
],
"internal_notes": ""
}
],
"submission_type": {
"id": 6673,
"name": {
"en": "Talk"
},
"default_duration": 25,
"deadline": null,
"requires_access_code": false
},
"track": {
"id": 6399,
"name": {
"en": "Data Day - Apr 9"
},
"description": {},
"color": "#3776aa",
"position": 2,
"requires_access_code": false
},
"tags": [],
"state": "confirmed",
"abstract": "Statisticians and machine learning specialists have a lot to learn from each other (even if they don't think so). This talk lightheartedly awards points to both classical statistics and machine learning, with an attempt not to offend anyone (but to annoy everyone). Topics include: Are confidence intervals worth it? What is bias, anyway? Can I just code it in Python?",
"description": "I recently began a journey into a machine learning certification with a major cloud provider. Given my background in econometrics, I had some opinions about ML. Some of these suspicions were confirmed, but many proved to be wrong. This talk lightheartedly awards points to both classical statistics and machine learning, with an attempt not to offend anyone (but to annoy everyone).\r\n\r\nAfter my presentation, you will be able to explain confidence intervals. Not how they work, mind you, but why statisticians are so obsessed with them (and whether or not you should be). You will be able to decode all three meanings of bias in machine learning. Perhaps most importantly, we will attempt to definitively classify popular Python packages as machine learning or statistical packages (the results may surprise you!).\r\n\r\nMost topics will be broad and accessible. There will be some light derivations regarding topics like statistical bias, the audience can generally ignore them (lots of professionals ignore them, why shouldn't you?). A basic understanding of probability principles and interest in data science are helpful. No cloud service providers will be harmed in the presentation.",
"duration": 25,
"slot_count": 1,
"content_locale": "en",
"do_not_record": false,
"image": null,
"resources": [],
"slots": [],
"answers": [
491999
],
"pending_state": null,
"is_featured": false,
"notes": "",
"internal_notes": null,
"invitation_token": "GLSCRBXW9PNZKAENWKU3UTXWXCECFBXU",
"access_code": null,
"review_code": "NYTDHVTN3M9BC7F3YAEQAQKZRGSDM9JY",
"anonymised_data": "{}",
"reviews": [],
"assigned_reviewers": [],
"is_anonymised": false,
"median_score": null,
"mean_score": null,
"created": "2026-01-12T10:27:57.356054+02:00",
"updated": "2026-01-12T10:27:57.356071+02:00",
"invitations": []
}Parsed Values:
{
"speakers": [
{
"code": "ADEDDZ",
"name": "James Donahue",
"biography": "Raised in Nashville, Tennessee, USA, I have bounced around the world, finally settling in Hamburg, Germany. My professional background reflects my vagabond nature. I taught English in Asia, Latin America, and online (full-time remote work before Covid!), worked in adventure tourism in Appalachia (USA) and Chile, and did my masters in Economics in Hamburg. Somewhere along the way, I gathered a few stories and soft skills.\r\n\r\nI met Python during my masters, but stuck with Matlab through the my PhD coursework, until I decided that academia is not my forever home. Currently I am occupying myself with ensemble methods such as XGBoost, as well as expanding into computer vision with PyTorch and toying with more advanced data visualizations. Naturally, NumPy will always hold a special place in my heart, along with the Cython package to sample everything a Bayesian needs.",
"submissions": [],
"avatar_url": "https://pretalx.com/media/avatars/ADEDDZ_G8dK3qa.webp",
"answers": [
492000,
492001
],
"email": "jimbodonahue@gmail.com",
"timezone": "UTC",
"locale": "en",
"has_arrived": false,
"availabilities": [
{
"start": "2026-04-08T00:00:00+03:00",
"end": "2026-04-11T00:00:00+03:00",
"allDay": false
}
],
"internal_notes": ""
}
],
"track": {
"id": 6399,
"name": {
"en": "Data Day - Apr 9"
},
"description": {},
"color": "#3776aa",
"position": 2,
"requires_access_code": false
},
"submissionType": {
"id": 6673,
"name": {
"en": "Talk"
},
"default_duration": 25,
"deadline": null,
"requires_access_code": false
}
}Stats Meets ML - What I learned from my Machine Learning Certification
Speaker
James Donahue
Raised in Nashville, Tennessee, USA, I have bounced around the world, finally settling in Hamburg, Germany. My professional background reflects my vagabond nature. I taught English in Asia, Latin America, and online (full-time remote work before Covid!), worked in adventure tourism in Appalachia (USA) and Chile, and did my masters in Economics in Hamburg. Somewhere along the way, I gathered a few stories and soft skills.
I met Python during my masters, but stuck with Matlab through the my PhD coursework, until I decided that academia is not my forever home. Currently I am occupying myself with ensemble methods such as XGBoost, as well as expanding into computer vision with PyTorch and toying with more advanced data visualizations. Naturally, NumPy will always hold a special place in my heart, along with the Cython package to sample everything a Bayesian needs.
Abstract
Statisticians and machine learning specialists have a lot to learn from each other (even if they don't think so). This talk lightheartedly awards points to both classical statistics and machine learning, with an attempt not to offend anyone (but to annoy everyone). Topics include: Are confidence intervals worth it? What is bias, anyway? Can I just code it in Python?
Description
I recently began a journey into a machine learning certification with a major cloud provider. Given my background in econometrics, I had some opinions about ML. Some of these suspicions were confirmed, but many proved to be wrong. This talk lightheartedly awards points to both classical statistics and machine learning, with an attempt not to offend anyone (but to annoy everyone).
After my presentation, you will be able to explain confidence intervals. Not how they work, mind you, but why statisticians are so obsessed with them (and whether or not you should be). You will be able to decode all three meanings of bias in machine learning. Perhaps most importantly, we will attempt to definitively classify popular Python packages as machine learning or statistical packages (the results may surprise you!).
Most topics will be broad and accessible. There will be some light derivations regarding topics like statistical bias, the audience can generally ignore them (lots of professionals ignore them, why shouldn't you?). A basic understanding of probability principles and interest in data science are helpful. No cloud service providers will be harmed in the presentation.