传智播客大数据_数据科学播客的终极清单

传智播客大数据

Podcasts are a great way to immerse yourself in an industry, especially when it comes to data science. The field moves extremely quickly, and it can be difficult to keep up with all the new developments happening each week!

播客是使自己沉浸于一个行业的好方法，尤其是在数据科学方面。 该领域的发展非常Swift，因此很难跟上每周发生的所有新发展！

Take advantage of those times in the day when your body is busy, but your mind is free: when you’re commuting to work, exercising at the gym, or cleaning up around the house. These are optimal times to engage your brain in learning something new and ensure you’re staying at the top of your field.

利用一天中身体忙碌但头脑自由的时间：上下班，在健身房锻炼或打扫房屋时。 这些是最佳时机，可以让您的大脑学习新知识，并确保您始终处于领先地位。

There are dozens of data science podcasts out there, covering everything from machine learning and artificial intelligence to big data analytics. We hope this will be a great resource for you to find useful, informative, and engaging shows.

那里有数十个数据科学播客，涵盖了从机器学习和人工智能到大数据分析的所有内容。 我们希望这将是您找到有用，内容丰富且引人入胜的节目的好资源。

Get ready to dive in!

准备潜水！

Free Bonus: Click here to get access to a free NumPy Resources Guide that points you to the best tutorials, videos, and books for improving your NumPy skills.

免费红利： 单击此处可获取免费的NumPy资源指南 ，该指南为您提供最佳教程，视频和书籍，以提高NumPy技能。

主动数据科学播客 (Active Data Science Podcasts)

As of this writing, these data science podcasts are active and still in production. Start deep in the archives and work your way up, or jump right into the latest episode!

在撰写本文时，这些数据科学播客仍处于活跃状态，并且仍在生产中。 从档案库开始深入学习，或者直接进入最新一集！

数据怀疑者 (Data Skeptic)

• Website: https://dataskeptic.com/
• Twitter: @dataskeptic
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //dataskeptic.com/
• 推特： @dataskeptic
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Data Skeptic is one of the best-known data science podcasts. This weekly show explores topics in data science, statistics, machine learning and artificial intelligence.

数据怀疑论者是最著名的数据科学播客之一。 这个每周的展览探讨了数据科学，统计学，机器学习和人工智能方面的主题。

Hosted by Kyle Polich, the show is going strong with over 200 episodes for listeners to dive into. Recently, the show has released series of themed episodes that revolve around a larger topic in the data science world, like fake news.

该节目由凯尔·波利奇（Kyle Polich）主持，节目进行得很顺利，有200多个节目供听众欣赏。 最近，该节目发布了一系列主题情节，围绕着数据科学世界中的一个较大主题，例如假新闻。

The episodes alternate between interviews with industry professionals and minisodes that explain high-level data science concepts.

在采访行业专家和解释高端数据科学概念的部门之间，这些情节交替出现。

The minisodes are co-hosted by Linh Da Tran, who talks with Kyle about data science topics, like natural language processing and k-means clustering. Listeners gain a better understanding of the topic as the hosts talk through it.

小型讲台由Linh Da Tran共同主持，Linh Da Tran与Kyle讨论数据科学主题，例如自然语言处理和k-means聚类。 主持人通过话题进行交谈时，听众可以更好地理解该话题。

线性离题 (Linear Digressions)

• Website: http://lineardigressions.com
• Twitter: @LinDigressions
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： http ： //lineardigressions.com
• 推特： @LinDigressions
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Katie Malone and Ben Jaffe host Linear Digressions, a weekly podcast that explores recent developments in data science, machine learning, and artificial intelligence. The hosts are good friends, and their rapport makes each episode very accessible and easy to understand.

凯蒂·马龙（Katie Malone）和本·杰斐（Ben Jaffe）主持每周播客Linear Linear Digressions，探讨数据科学，机器学习和人工智能的最新发展。 主持人是好朋友，他们的融洽关系使每一集都很容易理解。

As of this writing, there are over 100 episodes for listeners to dive into. Each episode clocks in at around half an hour, making it a breeze to gain a quick understanding of the topic at hand.

在撰写本文时，有超过100集可供听众深入学习。 每个插曲大约需要半小时，这使得轻松了解当前主题变得轻而易举。

Katie and Ben do a great job at distilling a complex technical topic down to its fundamentals. In just a few short minutes, they demystify neural networks, autoencoders, the Fourier transform, and more.

凯蒂（Katie）和本（Ben）在将复杂的技术话题精简为基本知识方面做得很好。 在短短的几分钟内，它们使神经网络，自动编码器，傅立叶变换等神秘化。

会说话的机器 (Talking Machines)

• Website: https://www.thetalkingmachines.com/
• Twitter: @TlkngMchns
• Listen: RSS ⋅ iTunes ⋅ Player FM

• 网站： https ： //www.thetalkingmachines.com/
• 推特： @TlkngMchns
• 听： RSS ⋅ 的iTunes ⋅ 播放FM

Former public radio producer Katherine Gorman believes that continuing the public conversation about data science, AI, and machine learning is absolutely essential to preventing another AI winter.

前公共广播制作人凯瑟琳·戈尔曼（Katherine Gorman）认为，继续就数据科学，人工智能和机器学习进行公开对话对于防止另一个AI冬季绝对至关重要。

She believes that data science podcasts are a great venue for that discussion. To this end, she hosts Talking Machines along with Professor Neil Lawrence.

她认为数据科学播客是进行讨论的理想场所。 为此，她与尼尔·劳伦斯教授一起主持了Talking Machines。

The podcast aims to introduce machine learning to a wide audience and help industry professionals, business leaders, and interested laypeople better understand these tools and technologies.

该播客旨在向广大受众介绍机器学习，并帮助行业专业人士，商业领袖和有兴趣的非专业人士更好地了解这些工具和技术。

The episodes generally follow a simple format: the hosts chat about industry news, interview a guest, and in the end may answer a listener question. Episodes are released in seasons and tend to be on the longer side at around 40 minutes.

这些情节通常采用一种简单的格式：主持人谈论行业新闻，采访客人，最后可以回答听众的问题。 剧集按季节发行，往往在40分钟左右较长。

This is where Katherine’s history as a radio host comes in handy: she keeps the show engaging and informative, and works hard to make sure the it presents an accurate picture of the machine learning industry.

这就是凯瑟琳作为无线电主持人的历史的得心应手的地方：她保持节目的吸引力和信息量，并努力确保它呈现出机器学习行业的准确情况。

奥莱利数据展示 (O’Reilly Data Show)

• Website: https://www.oreilly.com/topics/oreilly-data-show-podcast
• Twitter: @OReillyMedia
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //www.oreilly.com/topics/oreilly-data-show-podcast
• 推特： @OReillyMedia
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Ben Lorica is the Chief Data Scientist at O’Reilly Media. In each episode, he is joined by an industry professional to discuss topics in big data and data science. The episodes run anywhere from 30 to 40 minutes and are very accessible to listen to.

Ben Lorica是O'Reilly Media的首席数据科学家。 在每一集中，他都会与一位行业专家一起讨论大数据和数据科学中的主题。 这些剧集的播放时间从30分钟到40分钟不等，非常容易听。

At the beginning of each episode, the host promotes an event series that listeners can attend to learn more about the topics covered in the podcast. The ones mentioned in the intro are the Strata Data Conference and the Artificial Intelligence Conference, but you can find more of the O’Reilly conferences on their event page.

在每一集的开头，主持人都会推广一系列活动，听众可以参加该活动系列，以了解有关播客中涵盖的主题的更多信息。 简介中提到的是Strata数据会议和人工智能会议，但是您可以在其活动页面上找到更多O'Reilly会议。

不太标准偏差 (Not So Standard Deviations)

• Website: http://nssdeviations.com/
• Twitter: @NSSDeviations
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： http ： //nssdeviations.com/
• 推特： @NSSDeviations
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Roger Peng (of the Johns Hopkins Bloomberg School of Public Health) and Hilary Parker (of Stitch Fix) co-host this podcast. They discuss industry news as well as their personal experiences working with data.

播客由Johns Hopkins Bloomberg公共卫生学院的Roger Peng和Stitch Fix的Hilary Parker共同主持。 他们讨论行业新闻以及他们处理数据的个人经历。

Episodes air two or three times a month and can run on the longer side. Most episodes are at least 60 minutes, with some clocking in at almost an hour and a half. These are great for when you have a long commute or spend an evening at home doing chores, so you can really get into the discussion!

插播广告每月播出两次或三遍，并且播放时间更长。 多数情节至少需要60分钟，有些情节则需要将近一个半小时。 当您上下班途中或在家里度过一个晚上做家务时，这些功能非常有用，因此您可以真正参与讨论！

数据故事 (Data Stories)

• Website: http://datastori.es/
• Twitter: @datastories
• Listen: RSS ⋅ iTunes ⋅ Player FM

• 网站： http ： //datastori.es/
• 推特： @datastories
• 听： RSS ⋅ 的iTunes ⋅ 播放FM

This podcast on data visualization focuses on a very specific subset of the data analysis pipeline—a rare gem among data science podcasts. Data viz specialists Enrico Bertini and Moritz Stefaner sit down with a guest every other week to discuss data analysis and visualization.

这个关于数据可视化的播客着重于数据分析管道的非常特定的子集，这是数据科学播客中罕见的瑰宝。 数据可视化专家Enrico Bertini和Moritz Stefaner每隔一周与客人坐下来讨论数据分析和可视化。

The show has quite a conversational tone. The hosts bounce ideas off of one another, ask great questions of their guests, and generally keep the conversation flowing. With around 40 minutes of runtime, listeners can settle in to really learn about how we can better visualize our data, as well as the role that data plays in our everyday lives.

该节目颇具对话性。 主持人相互交流想法，向客人提出很好的问题，并通常保持对话畅通。 在大约40分钟的运行时间中，听众可以真正了解我们如何更好地可视化数据以及数据在日常生活中的作用。

超级数据科学 (SuperDataScience)

• Website: https://www.superdatascience.com/podcast/
• Twitter: @superdatasci
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //www.superdatascience.com/podcast/
• 推特： @superdatasci
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Kirill Eremenko is a data science coach and lifestyle entrepreneur, and he brings his experience as an influencer to the SuperDataScience podcast. In his interview episodes, he talks with data scientists and data analysts to learn more about their career paths and how they were able to succeed in the data industry.

Kirill Eremenko是一位数据科学教练和生活方式企业家，他将自己的影响力经验带到了SuperDataScience播客中。 在采访中，他与数据科学家和数据分析师进行了交谈，以了解有关他们的职业道路以及他们如何在数据行业成功的更多信息。

In addition to interviewing industry experts, the host airs minisodes that are purely inspirational! Called Five Minute Friday, these minisodes aim to inspire listeners to improve themselves as data scientists, and to offer advice on how to advance in a data science career. This is definitely one of the most motivational data science podcasts out there!

除了采访行业专家之外，主持人播放的minisode纯粹是鼓舞人心的！ 这些迷你教室被称为“五分钟星期五”，旨在激发听众提高自己作为数据科学家的地位，并就如何推进数据科学事业提供建议。 这绝对是其中最有启发性的数据科学播客之一！

在家中的数据科学 (Data Science at Home)

• Website: https://datascienceathome.com/
• Twitter: @ThisIsFrag
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //datascienceathome.com/
• 推特： @ThisIsFrag
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Francesco Gadaleta wants to make machine learning easy for everyone. In this podcast, he alternates between interview episodes with industry experts, and solo episodes where he discusses a topic on his own.

Francesco Gadaleta希望让所有人都能轻松进行机器学习。 在此播客中，他在与行业专家的访谈节目和他自己讨论主题的个人节目之间进行切换。

The show doesn’t seem to be on a fixed schedule, and the episode length varies as well, but in general the interview episodes run closer to an hour, while his solo episodes clock in at around twenty minutes.

该节目似乎没有按固定的时间表进行，而且插曲的长度也有所不同，但一般来说，访谈插曲的运行时间接近一个小时，而他的独奏插曲大约在20分钟左右。

The host is pretty opinionated, so it can be interesting to hear his perspective on topics like AI winter, optimization, and the minimum requirements you need to become a data scientist.

主持人固执己见，因此很有趣地听听他对AI Winter，优化以及成为数据科学家所需的最低要求等主题的看法。

本周机器学习与人工智能（TWiML＆AI） (This Week in Machine Learning & Artificial Intelligence (TWiML&AI))

• Website: https://twimlai.com/
• Twitter: @twimlai
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //twimlai.com/
• 推特： @twimlai
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

TWiML&AI is a weekly podcast that discusses the latest developments in data science, machine learning, and artificial intelligence. The host Sam Charrington interviews leading researchers and industry experts to inform a growing community of academics, engineers, business leaders, and other machine learning and AI enthusiasts.

TWiML＆AI是每周播客，讨论数据科学，机器学习和人工智能的最新发展。 主持人山姆·查林顿（Sam Charrington）采访了领先的研究人员和行业专家，以向不断壮大的学者，工程师，商业领袖以及其他机器学习和AI爱好者社区提供信息。

The show caters to a highly targeted audience, and can be pretty technical at times. Listeners who are not industry professionals may need to brush up on background knowledge in order to get the most out of each episode.

该节目迎合了高度针对性的观众，有时可能是非常技术性的。 非行业专业人士的听众可能需要重新掌握背景知识，才能充分利用每个情节。

There are over two hundred hour-long episodes to listen to. Because the podcast discusses recent developments in this tech space, you can jump right into the latest episode, or head back in the archives and check in on some historical developments in machine learning and AI.

有超过200小时的情节需要听。 由于该播客讨论了该技术领域的最新发展，因此您可以直接进入最新一集，也可以返回档案库，并查看机器学习和AI的一些历史发展。

数据框 (DataFramed)

• Website: https://www.datacamp.com/community/podcast
• Twitter: @DataCamp
• Listen: iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //www.datacamp.com/community/podcast
• 推特： @DataCamp
• 听： iTunes的 ⋅ Podbean ⋅ 播放FM

Data scientist, writer, and educator Hugo Bowne-Anderson hosts this podcast sponsored by DataCamp.

数据科学家，作家和教育家Hugo Bowne-Anderson主持了这个由DataCamp赞助的播客。

Each week, the host sits down with industry professionals and academic experts to discuss how the data science industry is impacting the world. The host asks great questions and invites guests who discuss interesting developments in the field as well as their own personal projects.

主持人每周都会与行业专家和学术专家坐下来讨论数据科学行业如何影响世界。 主持人提出了很好的问题，并邀请来宾讨论该领域有趣的发展以及他们自己的个人项目。

DataFramed also has short segments spaced throughout the episodes that give the listener more information on certain topics. For instance, in Freelance Data Science, Hugo and Susan Sun talk about how to navigate the data science space as an independent contractor. Justin Boyce gives practical advice on improving workflow in Data Science Best Practices.

在整个情节中，DataFramed还具有较短的片段，使听众可以了解某些主题的更多信息。 例如，在Freelance Data Science中，Hugo和Susan Sun讨论了如何以独立承包商的身份进入数据科学领域。 贾斯汀·博伊斯（Justin Boyce）为改善数据科学最佳实践中的工作流提供了实用建议。

Because it is sponsored by DataCamp, their products are pitched a lot, so it can feel a bit sales-y at times. Still, the show is interesting and informative, and Hugo does a great job of drawing in the listener.

由于它是由DataCamp赞助的，因此他们的产品推销了很多产品，因此有时感觉有点卖点。 尽管如此，该节目还是很有趣且内容丰富，而且雨果在吸引听众方面做得很出色。

学习机101 (Learning Machines 101)

• Website: https://www.learningmachines101.com/
• Twitter: @lm101talk
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //www.learningmachines101.com/
• 推特： @ lm101talk
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

Dr. Richard Golden, Professor of Cognitive Science and Electrical Engineering, hosts Learning Machines 101. The podcast aims to explain advanced concepts in machine learning and artificial intelligence to a wide audience.

认知科学和电气工程学教授Richard Golden博士主持了Learning Machines101。播客旨在向广大观众介绍机器学习和人工智能的高级概念。

Still, the episodes can get pretty technical, covering topics such as knowledge representation, expectation maximization, and spectral clustering.

尽管如此，这些剧集仍可以变得非常技术性，涵盖诸如知识表示，期望最大化和频谱聚类之类的主题。

Listeners might need to listen more than once to really grasp the topic at hand. This shouldn’t be too hard, as the episodes are no more than half an hour long and aren’t released too often. (Only 74 episodes have been released since April 2014.)

收听者可能需要多听一次才能真正掌握手头的话题。 这应该不太困难，因为这些情节的时长不超过半小时，而且发布的频率也不太高。 （自2014年4月以来，仅发布了74集。）

Listeners can use this podcast as a jumping-off point into more advanced machine learning topics.

听众可以将此播客用作进入更高级的机器学习主题的起点。

工业人工智能 (Artificial Intelligence in Industry)

• Website: http://techemergence.libsyn.com/
• Twitter: @Emerj
• Subscribe: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： http ： //techemergence.libsyn.com/
• 推特： @Emerj
• 订阅： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

This weekly podcast is focused on the practical applications of artificial intelligence in business settings. The episodes are short, insightful, and easy to understand. Within half an hour, host Dan Faggella interviews AI professionals to see how the technology is used in industries from finance and government to retail and education.

该每周播客重点关注人工智能在商业环境中的实际应用。 情节简短，有见识且易于理解。 在半小时内，主持人Dan Faggella采访了AI专业人员，以了解该技术如何在从金融，政府到零售和教育的行业中使用。

Together, Dan and his guests answer questions like “How can you use AI to hire employees?” and “When should you upgrade your AI hardware?” They touch on each topic just long enough to pique the listener’s interest and encourage them to dive deeper on their own later.

Dan和他的客人一起回答了“如何使用AI雇用员工？”之类的问题。 和“何时应该升级AI硬件？” 他们接触每个话题的时间刚好足以激起听众的兴趣，并鼓励他们以后自己深入研究。

存档数据科学播客 (Archived Data Science Podcasts)

As of this writing, these data science podcasts have run their course. The archives are still available for you to dive into, and are chock-full of useful information, so don’t hesitate to dive right in!

在撰写本文时，这些数据科学播客已经开始运作。 档案仍然可供您使用，并且充满了有用的信息，所以不要犹豫，马上开始！

偏导数 (Partially Derivative)

• Website: http://partiallyderivative.com/
• Twitter: @partiallyd
• Listen: iTunes ⋅ Podbean ⋅ Player FM

• 网站： http ： //partiallyderivative.com/
• 推特： @partiallyd
• 听： iTunes的 ⋅ Podbean ⋅ 播放FM

If you like heading to the bar and chatting about industry news with your fellow data scientists, then this is one of the best data science podcasts for you! Jonathan Morgan, Vidya Spandana, and Chris Albon get together to down a few drinks and discuss the latest in data science.

如果您喜欢去酒吧和与您的数据科学家同行谈论行业新闻，那么这是最适合您的数据科学播客之一！ 乔纳森·摩根（Jonathan Morgan），维迪亚·斯潘达娜（Vidya Spandana）和克里斯·阿尔邦（Chris Albon）聚在一起喝了几杯，讨论了最新的数据科学。

The episodes can run anywhere from 20 minutes to an hour, but generally clock in at around 30 to 40 minutes. While the show is no longer running, there are over one hundred episodes in the archive.

这些情节的播放时间从20分钟到一个小时不等，但通常大约需要30到40分钟。 尽管节目不再播放，但存档中有超过一百集。

Listeners can delve into the backlog and learn about data scraping, bias models, and pair-programming in Python, as well as review some of the trending news stories of years past.

收听者可以深入研究积压，并了解Python中的数据抓取，偏差模型和结对编程，还可以回顾过去几年的一些热门新闻报道。

机器学习指南/应用的机器学习 (Machine Learning Guide / Machine Learning Applied)

• Website: http://ocdevel.com/mlg
• Twitter: @lefnire
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： http ： //ocdevel.com/mlg
• 推特： @lefnire
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

These data science podcasts are both run by Tyler Renelli, and each has a slightly different approach to machine learning and AI.

这些数据科学播客均由泰勒·雷内利（Tyler Renelli）负责，每个播客在机器学习和AI方面的方法略有不同。

Machine Learning Guide (MLG) aims to gently introduce listeners to the world of machine learning by explaining topics from the ground up, from the classic algorithms (linear and logistic regression) on up to reinforcement learning and hyperparameters.

机器学习指南（MLG）旨在通过从头开始解释主题，从经典算法（线性和逻辑回归）到强化学习和超参数，向听众介绍机器学习领域。

The episodes run anywhere from 45 minutes to an hour, but it’s easy to become engrossed in Tyler’s explanations. It’s the perfect podcast to complement other activities, like commuting, exercising, or cleaning up around the house.

这些情节的播映时间从45分钟到一个小时不等，但是很容易被泰勒的解释所吸引。 这是补充其他活动的理想播客，例如上下班，锻炼或打扫房屋。

One of the best parts of this podcast is the curated learning resources that the host provides at the end of each episode. After listening to a high-lever overview, you can dive deeper into the topic by taking a recommended course or reading a suggested textbook.

该播客的最佳部分之一是主持人在每一集结尾处提供的精选学习资源。 在听了一个高水平的概述之后，您可以通过参加推荐的课程或阅读推荐的教科书来更深入地研究该主题。

His episode on languages and frameworks includes a link to a primer on Python deep learning frameworks. If you follow the episodes in order from beginning to end, and complete the supplemental resources, you will have quite a detailed foundation in machine learning.

他关于语言和框架的一集包括指向Python深度学习框架入门的链接。 如果您从头到尾按顺序进行操作并完成补充资源，那么您将在机器学习方面有相当详细的基础。

As of this writing, MLG has run its course at 29 full-length episodes.

截至撰写本文时，MLG已有29集完整剧集。

A second podcast called Machine Learning Applied is currently airing, where Tyler focuses on the more practical aspects of machine learning. He answers questions such as what kind of salary one can expect, the best way to store data, and how to get the most out of Jupyter notebooks. Listeners can gain access to Machine Learning Applied by becoming a supporter on Patroen.

目前正在播出第二个名为Machine Learning Applied的播客，Tyler重点关注机器学习的更多实际方面。 他回答了诸如可以期望的薪水，存储数据的最佳方式以及如何从Jupyter笔记本中获得最大收益的问题。 通过成为Patroen的支持者，听众可以访问“应用机器学习”。

成为数据科学家 (Becoming a Data Scientist)

• Website: https://www.becomingadatascientist.com/category/podcast/
• Twitter: @BecomingDataSci
• Listen: RSS ⋅ iTunes ⋅ Podbean ⋅ Player FM

• 网站： https ： //www.becomingadatascientist.com/category/podcast/
• 推特： @BecomingDataSci
• 听： RSS ⋅ 的iTunes ⋅ Podbean ⋅ 播放FM

This podcast does exactly what its title says. The host, Renee Teate, sits down each week with someone who is on their way to “becoming a data scientist.”

该播客完全按照标题中的内容进行操作。 主持人瑞妮·泰特（Renee Teate）每周与正在“成为数据科学家”的人坐下。

She interviews other data science professionals to see exactly how they were able to carve a path for themselves into the industry. In the very first episode, Renee talks about her own journey into becoming a data scientist as she transitions from her previous role as a data analyst.

她采访了其他数据科学专业人士，以确切地了解他们如何为自己进入该行业开辟道路。 在第一集中，蕾妮谈到了自己从成为数据分析师的角色转变为数据科学家的过程。

As of this writing, the podcast is not currently active. The last episodes came out in early 2017. Still, there is a wealth of information contained in the twenty hour-long episodes that have aired.

在撰写本文时，播客当前未处于活动状态。 最后几集于2017年初发布。不过，播出的20小时长的插曲中仍然包含大量信息。

If you’re just beginning your foray into the data science world, take a weekend to blast through the archive and see where the possibilities lie!

如果您刚刚开始涉足数据科学领域，请花一个周末浏览档案库，看看可能存在的地方！

结论 (Conclusion)

This list is not exhaustive! There are new podcasts airing all the time, and we can only expect the number of data science podcasts to grow as the field continues to explode in popularity.

此列表并不详尽！ 新的播客一直在播出，我们只能期望随着该领域的持续爆炸式增长，数据科学播客的数量会不断增长。

Don’t see your favorite show on this list? Leave us a comment down below and let us know your favorite data science podcasts!

在此列表上没有看到您喜欢的节目？ 在下方给我们留言，让我们知道您最喜欢的数据科学播客！

翻译自: https://www.pybloggers.com/2019/02/the-ultimate-list-of-data-science-podcasts/

传智播客大数据