All Videos Tagged " (Data Science Central) - Data Science Central 2019-08-23T15:50:21Z https://www.datasciencecentral.com/video/video/listTagged?tag=%22&rss=yes&xn_auth=no DSC Webinar Series: From Pandas to Apache Spark™ tag:www.datasciencecentral.com,2019-07-03:6448529:Video:851584 2019-07-03T19:18:10.864Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-from-pandas-to-apache-spark"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3189210470?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>***Please be aware there is a slight audio issue from approximately 10:45-13:00 in the recording***<br></br> <br></br> Presenting Koalas, a new open source project unveiled by Databricks, that brings the simplicity of pandas to the scalability powers of Apache Spark™.<br></br> <br></br> Data science with Python has exploded in popularity over the past few years and… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-from-pandas-to-apache-spark"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3189210470?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />***Please be aware there is a slight audio issue from approximately 10:45-13:00 in the recording***<br /> <br /> Presenting Koalas, a new open source project unveiled by Databricks, that brings the simplicity of pandas to the scalability powers of Apache Spark™.<br /> <br /> Data science with Python has exploded in popularity over the past few years and pandas has emerged as the lynchpin of the ecosystem. When data scientists get their hands on a data set, pandas is often the most common exploration tool. It is the ultimate tool for data wrangling and analysis. In fact, pandas’ read_csv is often the very first command students run in their data science journey.<br /> <br /> The problem? pandas does not scale well to big data. It was designed for small data sets that a single machine could handle. On the other hand, Apache Spark has emerged as the de facto standard for big data workloads. Today many data scientists use pandas for coursework, and small data tasks. When they work with very large data sets, they either have to migrate their code to PySpark's close but distinct API or downsample their data so that it fits for pandas.<br /> <br /> Now with Koalas, data scientists get the best of both worlds and can make the transition from a single machine to a distributed environment without needing to learn a new framework.<br /> <br /> In this latest Data Science Central webinar, the developers of Koalas will show you how:<br /> <br /> Koalas removes the need to decide whether to use pandas or PySpark for a given data set<br /> For work that was initially written in pandas for a single machine, Koalas allows data scientists to scale up their code on Spark by simply switching out pandas for Koalas<br /> Koalas unlocks big data for more data scientists in an organization since they no longer need to learn PySpark to leverage Spark<br /> <br /> Speakers:<br /> Tony Liu, Product Manager, Machine Learning - Databricks<br /> Tim Hunter, Sr. Software Engineer and Technical Lead, Co-Creator of Koalas - Databricks<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Making AI Work in the Real World: How Real Companies Get Real Value with AI tag:www.datasciencecentral.com,2019-07-02:6448529:Video:850987 2019-07-02T16:16:14.793Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-making-ai-work-in-the-real-world-how-real"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3181882056?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing is, a lot of those articles are light on detail or focus too heavily on algorithms and not on business value.<br></br> <br></br> In this latest Data Science Central webinar, Alyssa… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-making-ai-work-in-the-real-world-how-real"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3181882056?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing is, a lot of those articles are light on detail or focus too heavily on algorithms and not on business value.<br /> <br /> In this latest Data Science Central webinar, Alyssa Simpson Rochwerger of Figure Eight takes an industry-by-industry perspective on true AI adoption.<br /> <br /> We will cover:<br /> <br /> An approach to AI that realizes business value<br /> Real-world examples of businesses using AI to improve their bottom line<br /> Real-world use cases in e-commerce, enterprise software, robotics &amp; IoT, AgTech, and more<br /> <br /> <br /> We will separate the hype from the reality, the theoretical from the practical, and the research labs from ROI.<br /> <br /> <br /> <br /> Speaker:<br /> Alyssa Simpson Rochwerger, VP of Product - Figure Eight<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: The State of Data Preparation in 2019 tag:www.datasciencecentral.com,2019-06-25:6448529:Video:846235 2019-06-25T21:30:56.229Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-the-state-of-data-preparation-in-2019-1"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3109900816?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Over the past few years, data preparation has emerged as a stand-alone category within data management and analytics. A technology category that originated out of joint research across UC Berkeley and Stanford, it is now recognized as a critical technology by end users, organizations and industry analysts alike. Data preparation has evolved… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-the-state-of-data-preparation-in-2019-1"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3109900816?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Over the past few years, data preparation has emerged as a stand-alone category within data management and analytics. A technology category that originated out of joint research across UC Berkeley and Stanford, it is now recognized as a critical technology by end users, organizations and industry analysts alike. Data preparation has evolved tremendously since the category first emerged in 2015. So what’s new? How far have we come? Where are we headed in the future?<br /> <br /> Join this latest Data Science Central webinar with Dresner Advisory Service’s Chief Research Officer, Howard Dresner, for an overview of the data preparation market. In the session, Howard reviews findings from his 2019 “Wisdom of the Crowds Market Study” on data preparation, compiled from end user responses.<br /> <br /> This webinar will cover the following topics:<br /> <br /> How data preparation is being utilized within organizations – what users &amp; departments utilize data prep?<br /> What are the most critical features of data preparation technologies?<br /> Differences between traditional ETL technologies and this new generation of data preparation tools.<br /> <br /> Speakers:<br /> Howard Dresner, Chief Research Officer Analyst - Dresner Advisory Services<br /> Will Davis, Senior Director of Product Marketing - Trifacta<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: Making AI Work in the Real World: How Real Companies Get Real Value with AI tag:www.datasciencecentral.com,2019-03-12:6448529:Video:809082 2019-03-12T20:29:00.025Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-making-ai-work-in-the-real-world-how-real-1"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/1392344430?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing is, a lot of those articles are light on detail or focus too heavily on algorithms and not on business value.<br></br> <br></br> In this latest Data Science Central webinar, Alyssa… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-making-ai-work-in-the-real-world-how-real-1"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1392344430?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing is, a lot of those articles are light on detail or focus too heavily on algorithms and not on business value.<br /> <br /> In this latest Data Science Central webinar, Alyssa Simpson Rochwerger of Figure Eight takes an industry-by-industry perspective on true AI adoption.<br /> <br /> We will cover:<br /> <br /> An approach to AI that realizes business value<br /> Real-world examples of businesses using AI to improve their bottom line<br /> Real-world use cases in e-commerce, enterprise software, robotics &amp; IoT, AgTech, and more<br /> <br /> We will separate the hype from the reality, the theoretical from the practical, and the research labs from ROI.<br /> <br /> Speaker:<br /> Alyssa Simpson Rochwerger, VP of Product - Figure Eight<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: The Essentials of Training Data for Machine Learning tag:www.datasciencecentral.com,2018-09-26:6448529:Video:763549 2018-09-26T22:09:35.844Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-the-essentials-of-training-data-for-machine"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781544165?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>A machine learning algorithm isn’t worth much without great training data to power it. After all, algorithms learn from data, discovering relationships, developing understanding, making decisions, and evaluating their confidence from the training data they’re given. And the better the training data is, the better the model performs. In fact,… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-the-essentials-of-training-data-for-machine"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781544165?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />A machine learning algorithm isn’t worth much without great training data to power it. After all, algorithms learn from data, discovering relationships, developing understanding, making decisions, and evaluating their confidence from the training data they’re given. And the better the training data is, the better the model performs. In fact, the quality and quantity of your training data has as much to do with the success of your data project as the algorithms themselves.<br /> <br /> Join us for this latest Data Science Central webinar on the basics of training data where we will cover:<br /> <br /> What training data is and why it’s so important<br /> What training data looks like for a variety of projects<br /> Why training data should be labeled and how to get it labeled<br /> How much training data you need<br /> <br /> Speaker: Jennifer Prendki, VP of Machine Learning -- Figure Eight<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central