Lambda Functions with Practical Examples in Python by Susan Maina by @TDataScience

How, when to use, and when not to use Lambda functions.

This is a really useful and a great "primer" to help you get into using them. I suggest you do some practice and look at the results to make sure you understand them and how they work. Then you are in a position to use them more in anger.

All You Need to Know About Pandas Cut and Qcut Functions by @snr14 via @TDataScience

What exactly is the difference between them?

I found this interesting and think I would use this in the future.

How Can Artificial Intelligence Improve Health Care? by Emily Newton via @Datafloq

Artificial intelligence (AI) benefits numerous industries by providing insights that would take too long or may otherwise be impossible to get through other methods. That additional information is crucial in the health care sector, where professionals regularly make critical decisions while diagnosing a patient, treating their ailments or helping them manage chronic symptoms. Here are some fascinating ways that applying AI in health care brings benefits to everyone involved.

It's always great to remember some of the good things that AI does amid all of the hysteria or hype. This technology could do with more positive stories showing how it has improved our world or our health.

How to Generate Automated PDF Documents with Python by @mokhorasani via @kdnuggets

Discover how to leverage automation to create dazzling PDF documents effortlessly.

I never knew this was even possible - it could save you time if you have to publish and distribute information frequently.

6 Python Projects You Can Finish in a Weekend by Frank Andrade via @TDataScience

Beginner and advanced projects that will help you level up your Python code.

These are simple tasks but could be seen as a good workout or test to see how good your Python coding is (or is not).

LIVE DEMO - Scalable MLOps With Snowflake and DataRobot - 24 June 2021

 

Live Demo: Scalable MLOps With Snowflake and DataRobot June 24, 2021 | 10:00 AM PT | 1 Hour Register now Organizations of all sizes continue to invest in machine learning to improve upon a variety of revenue drivers and operational efficiencies, but many of these initiatives struggle to go beyond proof of concept. Join us to see first-hand how Snowflake and DataRobot can provide a scalable and repeatable path to production with an integrated solution for model deployment and monitoring that leverages the power of the cloud.  In this demo you'll see how to: Incorporate third party data from Snowflake Data Marketplace into your models Prepare data and perform feature engineering using Snowpark Train and evaluate a predictive housing prices model using DataRobot Deploy and monitor models in production Register now

WEBINAR: Mapping Challenges for Self-Service Analytics - 24 June 2021

 

Data Science Central Webinar Series Event

Mapping Challenges for Self-Service Analytics Join us for this latest DSC Webinar on June 24th, 2021 There are many tools designed to make mapping easy for anyone who wants to explore, analyze, and communicate the spatial patterns in their data. In this latest Data Science Central webinar, Research Scientist Sarah Battersby will address some of the technical design and analytics challenges, including technical base map data, map projections, and distortion. Attend and learn how to help people visualize and use spatial information more effectively. Speaker:   Sarah Battersby, Research Scientist - Tableau   Hosted by:   Sean Welch, Host and Producer- Data Science Central   Title:  Mapping Challenges for Self-Service Analytics Date:  Thursday, June 24th, 2021 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your seat now

WEBINAR: Using external data to accelerate business in a post-vaccinated world - 24 June 2021

 

NEWSLETTER . Using external data to accelerate business in a post-vaccinated world REGISTER NOW . You’re Invited! THURSDAY, JUNE 24 11AM PT | 2PM ET 60 MIN SESSION REGISTER NOW Join this webinar to learn how companies are developing insights to better prepare for growth opportunities, improve business performance, and mitigate risk in a post-pandemic economy. Join this webinar to learn how companies are using data to build and enhance visualizations, train machine-learning algorithms, and facilitate valuable insights. Attendees will learn: Use diverse data to help enrich business analytics. Integrate data into machine-learning models and data pipelines to create powerful visualizations. Utilize data analysis strategies to help apply faster insights and business outcomes. Leverage data to understand consumer behaviours from online purchasing patterns. Moderator: Samantha Gibson WW Leader, AWS Data Exchange Category Management, AWS Data Exchange Samantha Gibson leads the industry and regional verticals team for AWS Data Exchange at Amazon Web Services (AWS). In this capacity, her team engages with data subscribers globally across industries such as Financial Services, Healthcare and Life Sciences, Retail, Marketing & Advertising, and Media & Entertainment to discover, procure, and use traditional and alternative data assets in the AWS Cloud. Samantha’s team also works with data providers to help reduce their infrastructure, sales, and support costs and grow their customer base through cloud-native distribution. Prior to joining AWS, Samantha was a part of the Strategy and Corporate Development team at Bloomberg L.P. Samantha managed strategic initiatives and transactions spanning the company’s financial products, enterprise, and data businesses, as well as market surveillance and exploration of emerging market trends and financial technologies. Samantha was the inaugural product manager of the Bloomberg Gender-Equality Index, a first-of-its-kind reference index launched in May 2016, which measures the performance of global public companies recognized for supporting both data disclosure and best-in-class policies and practices in the gender-equality space. Samantha graduated Magna Cum Laude with a B.S. in Finance from the Stern School of Business at New York University, is a CFA charter holder, and a 2018 Aspen Institute First Movers Fellow. She is part of the NYU Stern Alumni Council and represents Amazon Web Services as an Executive Committee member of the Financial Information Services Association (FISD). Presenters: Jace McLean Director, Data Insights, Domo Jace McLean has more than 15 years of experience in data, analytics, and technology. His passion revolves around solving complex problems in a data-driven manner. Prior to Domo, he spent two years at Cargill building out analytics capabilities for its North American finance department. He also led analytics teams at Target in their Enterprise Data Analytics and Business Intelligence (EDABI) Center of Excellence with a focus on new products in e-commerce. Prior to that he spent nine years in the software industry. Jace has a bachelor’s degree in Computer Science from the University of Minnesota’s Institute of Technology, and a Master of Business Administration from The University of Chicago Booth School of Business. Jonathan Kay Founder and CEO, Apptopia Jonathan Kay co-founded Apptopia at the age of 25. As the CEO, he leads the daily operations and strategic direction, including product development and global sales. He’s an expert on the mobile landscape, app economy, and how data and predictive modeling add transparency to the ecosystem. As someone who believes deeply in the importance of customer engagement, he is constantly striving to find scalable intimacy. He’s extremely passionate about branding and storytelling. John Rogers Chief Innovation Officer, CoreLogic John Rogers holds the role of Chief Innovation Officer at CoreLogic. He is responsible for driving innovation through a state-of-the-art R&D platform to act as a catalyst to transform the industries CoreLogic serves. Prior to joining CoreLogic, John was a Partner with IBM Global Business Services where he focused on the delivery of large multi-million transformational programs within the financial sector. John earned a bachelor’s degree from University of Glasgow, United Kingdom in Aerospace Engineering. Colin Marden Senior Solutions Architect, AWS Colin Marden is a Solutions Architect in the Financial Services industry supporting AWS customers in their journey to modernize, transform, and migrate on-premises workloads to the AWS Cloud. Colin is a champion and specialist for Amazon QuickSight and AWS Data Exchange. He regularly works with AWS customers to create data engineering architectures and speaks at AWS and partner events on these subjects of interest. Kanchan Waikar Senior Solutions Architect for Machine Learning, AWS Kanchan Waikar is a Senior Partner Solutions Architect at Amazon Web Services with AWS Marketplace for machine learning group. She has over 14 years of experience building, architecting, and managing, NLP, and software development projects. She has a master’s degree in computer science (data science major), and she enjoys helping customers build solutions backed by AI/ML-based AWS services and partner solutions. REGISTER NOW © 2021 AWS Marketplace. 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WEBINAR: The Data Engineering Cloud: Visualize the Transform with Trifacta - 22 June 2021

 

Data Science Central <mail@newsletter.datasciencecentral.com> Unsubscribe	26 May 2021, 21:37 (3 days ago)
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Data Science Central Webinar Series Event The Data Engineering Cloud: Visualize the Transform with Trifacta Join us for this latest DSC Webinar on June 22nd, 2021 Over the past two decades, we have watched as data has become increasingly strategic to nearly every organization. During that time we have lived through the evolution from Big Data (new challenges) to Data Science (new projects) to Data Engineering (new practices). The next step is now in sight: an open and inclusive Data Engineering Cloud that brings together a diversity of stakeholders to transform businesses. In this latest Data Science Central webinar, join Trifacta co-founders and noted technologists Jeffrey Heer and Joe Hellerstein as they talk about what an inclusive Data Engineering Cloud looks like, and how a decade of innovation in computer science—from AI to interactive visualization to program synthesis to data ops—enables a truly inclusive and transformational Data Engineering Cloud. Speakers:   Joe Hellerstein, Co-Founder & CSO - Trifacta   Jeff Heer, Co-Founder & CXO - Trifacta   Hosted by:   Sean Welch, Host and Producer- Data Science Central   Title:  The Data Engineering Cloud: Visualize the Transform with Trifacta Date:  Tuesday, June 22nd, 2021 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your seat now

How I Doubled My Income with Data Science and Machine Learning by Terence Shin via @kdnuggets

Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.

This looks interesting and he has some great links to separate articles that can help you get to that level.

A Guide On How To Become A Data Scientist (Step By Step Approach) by Aditya Agarwal via @kdnuggets

Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist.

My advice is to do the knowledge but a Data Scientist is not as easy a job to get so you might want or think a bit more about that as an achievable goal. Consider becoming a Data Engineer or other role in the same region of jobs with the option to move up to a Data Scientist at some point when you have a lot more experience to rely upon.

Pattern Recognition and the Fundamental Methods of Machine Learning by Andreas Maier via @TDataScience

 A Comprehensive Overview of Classical ML Methods.

This contains some links to some great videos and transcripts. Very very useful as a jumping point to expand your knowledge around this.

A Comprehensive Introduction to Bayesian Deep Learning by Joris Baan via @TOPBOTS

Even knowing basic probability theory, you may have a hard time understanding and connecting that to modern Bayesian deep learning research. In this article, Joris Baan aims to bridge that gap and provides a comprehensive introduction.

I found this useful especially if you can do with a reminder of Bayesian DL and certainly I appreciated the reminder and was able to approach it with a fresher viewpoint.