Data Science

Doing Dask Powered Data Science In The Saturn Cloud - Episode 331

A perennial problem of doing data science is that it works great on your laptop, until it doesn’t. Another problem is being able to recreate your environment to collaborate on a problem with colleagues. Saturn Cloud aims to help with both of those problems by providing an easy to use platform for creating reproducible environments that you can use to build data science workflows and scale them easily with a managed Dask service. In this episode Julia Signall, head of open source at Saturn Cloud, explains how she is working with the product team and PyData community to reduce the points of friction that data scientists encounter as they are getting their work done.

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Growing And Supporting The Data Science Community At Anaconda - Episode 328

Data scientists are tasked with answering challenging questions using data that is often messy and incomplete. Anaconda is on a mission to make the lives of data professionals more manageable through creation and maintenance of high quality libraries and frameworks, the distribution of an easy to use Python distribution and package ecosystem, and high quality training material. In this episode Kevin Goldsmith, CTO of Anaconda, discusses the technical and social challenges faced by data scientists, the ways that the Python ecosystem has evolved to help address those difficulties, and how Anaconda is engaging with the community to provide high quality tools and education for this constantly changing practice.

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Unleash The Power Of Dataframes At Any Scale With Modin - Episode 324

When you start working on a data project there are always a variety of unknown factors that you have to explore. One of those is the volume of total data that you will eventually need to handle, and the speed and scale at which it will need to be processed. If you optimize for scale too early then it adds a high barrier to entry due to the complexities of distributed systems, but if you invest in a lot of engineering up front then it can be challenging to refactor for scale. Modin is a project that aims to remove that decision by letting you seamlessly replace your existing Pandas code and scale across CPU cores or across a cluster of machines. In this episode Devin Petersohn explains why he started working on solving this problem, how Modin is architected to allow for a smooth escalation from small to large volumes of data and compute, and how you can start using it today to accelerate your Pandas workflows.

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Fast And Educational Exploration And Analysis Of Graph Data Structures With graph-tool - Episode 322

If you are interested in a library for working with graph structures that will also help you learn more about the research and theory behind the algorithms then look no further than graph-tool. In this episode Tiago Peixoto shares his work on graph algorithms and networked data and how he has built graph-tool to help in that research. He explains how it is implemented, how it evolved from a simple command line tool to a full-fledged library, and the benefits that he has found from building a personal project in the open.

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Traversing The Challenges And Promise Of Graph Machine Learning - Episode 319

The foundation of every ML model is the data that it is trained on. In many cases you will be working with tabular or unstructured information, but there is a growing trend toward networked, or graph data sets. Benedek Rozemberczki has focused his research and career around graph machine learning applications. In this episode he discusses the common sources of networked data, the challenges of working with graph data in machine learning projects, and describes the libraries that he has created to help him in his work. If you are dealing with connected data then this interview will provide a wealth of context and resources to improve your projects.

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Data Exploration and Visualization Made Effortless with Lux - Episode 313

Data exploration is an important step in any analysis or machine learning project. Visualizing the data that you are working with makes that exploration faster and more effective, but having to remember and write all of the code to build a scatter plot or histogram is tedious and time consuming. In order to eliminate that friction Doris Lee helped create the Lux project, which wraps your Pandas data frame and automatically generates a set of visualizations without you having to lift a finger. In this episode she explains how Lux works under the hood, what inspired her to create it in the first place, and how it can help you create a better end result. The Lux project is a valuable addition to the toolbox of anyone who is doing data wrangling with Pandas.

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Analyzing The Ecosystem of Python Data Companies With Tony Liu - Episode 305

There are a large and growing number of businesses built by and for data science and machine learning teams that rely on Python. Tony Liu is a venture investor who is following that market closely and betting on its continued success. In this episode he shares his own journey into the role of an investor and discusses what he is most excited about in the industry. He also explains what he looks at when investing in a business and gives advice on what potential founders and early employees of startups should be thinking about when starting on that journey.

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Go From Notebook To Pipeline For Your Data Science Projects With Orchest - Episode 304

Jupyter notebooks are a dominant tool for data scientists, but they lack a number of conveniences for building reusable and maintainable systems. For machine learning projects in particular there is a need for being able to pivot from exploring a particular dataset or problem to integrating that solution into a larger workflow. Rick Lamers and Yannick Perrenet were tired of struggling with one-off solutions when they created the Orchest platform. In this episode they explain how Orchest allows you to turn your notebooks into executable components that are integrated into a graph of execution for running end-to-end machine learning workflows.

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Giving Your Data Science Projects And Teams A Home At DagsHub - Episode 301

Collaborating on software projects is largely a solved problem, with a variety of hosted or self-managed platforms to choose from. For data science projects, collaboration is still an open question. There are a number of projects that aim to bring collaboration to data science, but they are all solving a different aspect of the problem. Dean Pleban and Guy Smoilovsky created DagsHub to give individuals and teams a place to store and version their code, data, and models. In this episode they explain how DagsHub is designed to make it easier to create and track machine learning experiments, and serve as a way to promote collaboration on open source data science projects.

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Turning Notebooks Into Collaborative And Dynamic Data Applications With Hex - Episode 294

Notebooks have been a useful tool for analytics, exploratory programming, and shareable data science for years, and their popularity is continuing to grow. Despite their widespread use, there are still a number of challenges that inhibit collaboration and use by non-technical stakeholders. Barry McCardel and his team at Hex have built a platform to make collaboration on Jupyter notebooks a first class experience, as well as allowing notebooks to be parameterized and exposing the logic through interactive web applications. In this episode Barry shares his perspective on the state of the notebook ecosystem, why it is such as powerful tool for computing and analytics, and how he has built a successful business around improving the end to end experience of working with notebooks. This was a great conversation about an important piece of the toolkit for every analyst and data scientist.

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