This week’s roundup looks at the 3 types of customer analytics that drive success, essentials of machine learning algorithms using Python and R, the movement aimed at empowering women in data science, and the top 10 platforms and resources to learn data science skills.
by Brett Grossfeld, Associate Content Marketing Manager at Zendesk, featured on Business 2 Community
In customer service, customer analytics paints a more accurate picture of the different touch points in the customer journey. There are both opportunities and shortcomings to unearth; the sort of insights that skilled support managers can leverage into better strategies. With machine learning making better usage of Big Data, customer analytics can dive into even more complex use cases. This leads to sharper predictions about the future and can even provide an actionable roadmap for achieving those desired results.
by Sunil Ray, Business Analytics and Intelligence Professional, featured on Analytics Vidhya
This guide is intended to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world. Through this guide, the author provides the fundamentals that enable you to work on machine learning problems and gain from experience. This high level understanding about various machine learning algorithms along with R & Python codes to run them are a good start to getting your hands dirty in this exciting field.
by Elena Poughia, Managing Director at Dataconomy Media, featured on Dataconomy
2018 has brought a new focus to the careers, excellence and issues of working women, and this extends into the fields of tech and data science. At the recent Women in Data Science (WiDS) conference, Dataconomy’s MD and WiDS ambassador Berlin, Elena Poughia, sat down with Ann Rosenberg, Senior VP of SAP Next-Gen, and Alexa Gorman, Global VP of the SAP.io fund to discuss and reflect on where women are now in data science, and where they need to go.
by Kristen Nicole, Senior Editor at SiliconANGLE.com, featured on SiliconANGLE.com
As artificial intelligence technology gains real-world use cases, the field of data science has become a focal point for those pushing for gender equality in the technology industry. High-profile advocates like Melinda Gates have made public pleas for more women to get involved in the training of the algorithms powering artificial intelligence applications, as diversity at the creation level can help resolve the challenges of inadvertent machine bias. Yet even as gender equality becomes a mainstream message, top tech companies still struggle to achieve equal pay and diversity at the top.
by Bob Hayes, President at Business Over Broadway, featured on CustomerThink
There are many ways to acquire data science skills, including online courses, blogs, textbooks, trade books, YouTube videos and more. Which approach should an aspiring data professional use to learn data science skills? To answer this question, the author used data from the Kaggle 2017 State of Data Science and Machine Learning survey of over 16,000 data professionals (survey data collected in August 2017). This comprehensive survey asked a variety of questions about their education and work practices.
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