KDnuggets

SEPTEMBER 17, 2019

We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

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Analytics Vidhya

SEPTEMBER 18, 2019

Overview Writing optimized Python code is a crucial piece in your data science skillset Here are four methods to optimize your Python code (with. The post 4 Unique Methods to Optimize your Python Code for Data Science appeared first on Analytics Vidhya.

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Dataconomy

SEPTEMBER 3, 2019

A data management plan in a company is not something that can be implemented in isolation by one department or a team in your organisation, it is rather a collective effort – similar to how different players perform in a basketball court. From the smallest schoolyard to the biggest pro. The post Lessons from the Basketball Court for Data Management appeared first on Dataconomy.

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Data Science 101

SEPTEMBER 28, 2019

Amazon and Coursera have teamed up to create Getting Started with AWS Machine Learning. Topics covered are: Machine Learning. Computer Vision. NLP. Amazon Sagemaker. Happy Learning!

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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Eugene Yan

SEPTEMBER 2, 2019

OMSCS CS6750 (Human Computer Interaction) - You are not your user! Or how to build great products.

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Smart Data Collective

SEPTEMBER 13, 2019

As industry buzzwords, “Big Data” is one of those phrases that has become seemingly ubiquitous. Everyone wants to be using big data to better their operation. The maintenance department is no exception to this trend. Accordingly, maintenance teams are beginning to embrace the use of big data and analytics to improve performance. In emphasizing the use of “big data”, maintenance can establish predictive maintenance programs, which reduce downtime and save on maintenance costs.

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Analytics Vidhya

SEPTEMBER 1, 2019

Overview Working on Data Science projects is a great way to stand out from the competition Check out these 7 data science projects on. The post Here are 7 Data Science Projects on GitHub to Showcase your Machine Learning Skills! appeared first on Analytics Vidhya.

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Dataconomy

SEPTEMBER 12, 2019

What is the big problem with the right to be forgotten (right to erasure, Article 17) under the GDPR? As Blockchain generally is immutable, and the GDPR requires personal data to be deleted – many people therefore conclude that it is impossible to store any kind of personal data on. The post Everything you want to know about GDPR’s Right to be Forgotten in Blockchain appeared first on Dataconomy.

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Data Science 101

SEPTEMBER 24, 2019

A number of new impactful open source projects have been released lately. Open Source Data Science Projects. Pythia – from Facebook for deep learning with vision and language, “such as answering questions related to visual data and automatically generating image captions “ InterpretML – from Microsoft, ” package for training interpretable models and explaining blackbox systems “ ML framework for Julia – from Alan Turing Institute, MLJ is a machine learni

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Victor Zhou

SEPTEMBER 16, 2019

I finally started using Git more heavily a few years ago when I first began building some of my bigger side projects. Now, it’s true that typing git status and git push is pretty easy, but if you’ve got some Git experience you know some commands can get rather long. The one that always got me was: $ git commit --amend --no-edit This amends your staged changes into your most recent commit without changing its commit message (so Git won’t open a text editor!).

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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Smart Data Collective

SEPTEMBER 15, 2019

Big data is changing the financial industry in a truly astounding way. Countless financial professionals are looking towards machine learning and other new tools to improve the quality of the services that they offer to their customers. K. Hussain of Atos Spain published a white paper on the growing relevance of big data in the finance and insurance verticals.

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KDnuggets

SEPTEMBER 23, 2019

Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.

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Analytics Vidhya

SEPTEMBER 4, 2019

Overview What is p-value? Where is it used in data science? And how can we calculate it? We answer all these questions and more. The post Everything you Should Know about p-value from Scratch for Data Science appeared first on Analytics Vidhya.

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Dataconomy

SEPTEMBER 26, 2019

Before you start using the next amazing new femtech innovation, you may want to weigh the benefits against your right to privacy. You might want to think twice before accepting that sweet employer incentive for participating in health monitoring. Yes. Corporate wellness monitoring programs offer benefits, but they also have. The post The Rise Of “Menstrual Surveillance” and the Fight for Data Privacy in Women’s Health appeared first on Dataconomy.

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Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Data Science 101

SEPTEMBER 26, 2019

I am putting together some of my own resources on Data Strategy. Here are a few of the top resources I found helpful so far. What is a Data Strategy? – various definitions of a data strategy The 5 essential Components of a Data Strategy – a detailed whitepaper(PDF) from SAS How to Create a Successful Data Strategy – a detailed report from MIT How Do You Develop a Data Strategy (including 6 steps) – by Bernard Marr, He has created more data strategies than anyone, so his a

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DataCentric podcast

SEPTEMBER 25, 2019

A catch-up podcast as Moor Insights & Strategy technology analysts Matt Kimball & Steve McDowell run through a very news-filled September. The guys recap Red Hat Summit, Pure Accelerate, and also hit product announcements from Dell EMC, IBM, and Huawei, bemoan the dwindling quality of conference SWAG, and more. Much, much, more: Red Hat might be owned by IBM, but its about helping enterprises manage digital transformation, focused on where "opportunity and innovation intersect" S

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Smart Data Collective

SEPTEMBER 25, 2019

Data breaches are becoming more common in today’s society. Hackers know they can sell compromised information on the dark web or use it for purposes such as blackmail. However, encryption technology for data protection is widely available. It involves protecting information with cryptography via a scrambled code. Only people with the key to decode the data can read it.

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KDnuggets

SEPTEMBER 3, 2019

These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.

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Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Analytics Vidhya

SEPTEMBER 24, 2019

Overview Google’s BERT has transformed the Natural Language Processing (NLP) landscape Learn what BERT is, how it works, the seismic impact it has made, The post Demystifying BERT: A Comprehensive Guide to the Groundbreaking NLP Framework appeared first on Analytics Vidhya.

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Dataconomy

SEPTEMBER 24, 2019

With ecommerce sales skyrocketing, the options for online transactions are manifold. But what are the problems that come with these many choices to pay? Find out. Global e-commerce sales hit $29 trillion in 2017 according to data released by the United Nations Conference on Trade and Development (UNCTAD) early this. The post Three Trends in E-commerce Payments to be Concerned About appeared first on Dataconomy.

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Data Science 101

SEPTEMBER 20, 2019

Just released this week, Nuts about Data , is a fun introductory book about the data science process. Meor Amer tells a witty story about squirrels, mining for nuts, teamwork, and survival. It brings together the entire data science lifecycle from asking questions to final storytelling. It is a quick read and really fun. I highly recommend it and hope you enjoy it.

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DataCentric podcast

SEPTEMBER 12, 2019

Want to hear a good origin story? Or about the future of data? You're in luck. As Pure Storage heads into its annual Pure Accelerate Conference in Austin next week, it's looking to celebrate its 10th anniversary. 10 years in which Pure has grown from a seed-stage start-up to a ~$4B publically traded company. And Pure continues to be a disrupter in the storage industry.

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Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.

Analytics

Smart Data Collective

SEPTEMBER 13, 2019

Big Data is among one of the most impressive tech advancements that have hit the marketing world in recent memory. While it has been tossed around as a buzzword in certain circles, Big Data is so much more than just a phrase. For a definition , Oracle recommends Gartner’s 2001 description of Big Data, which describes it as data containing a greater variety, getting to the source in increasing volume and at ever-higher velocity.

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KDnuggets

SEPTEMBER 5, 2019

This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.

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Analytics Vidhya

SEPTEMBER 29, 2019

Overview Seaborn is a popular data visualization library for Python Seaborn combines aesthetic appeal and technical insights – two crucial cogs in a data. The post Become a Data Visualization Whiz with this Comprehensive Guide to Seaborn in Python appeared first on Analytics Vidhya.

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Dataconomy

SEPTEMBER 17, 2019

Vice-President of XING’s Data Science team, Dr. Sébastien Foucaud, believes the time of blackbox AI is behind us. The market leader in the DACH region has a vision for its Machine Learning: it should be explainable to the boss, as well as to users. Focussing on German-speaking countries since 2012, The post A Simple and Transparent Machine Learning Approach Proves to Conquer the German Market appeared first on Dataconomy.

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Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Twilio Segment

SEPTEMBER 5, 2019

Segment had a security incident. Here's what you need to know.

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DataCentric podcast

SEPTEMBER 6, 2019

VMworld, which has become ground-zero for setting the direction for IT infrastructure, just wrapped up in San Francisco. Attended by over 21,000(!) people, it's become one the largest tech conferences in the world. This year the dominant themes were all about Containers and Clouds, with product announcements touching every aspect of the edge-to-core-to-cloud world.

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Smart Data Collective

SEPTEMBER 19, 2019

Machine learning has played a very important role in the development of technology that has a large impact on our everyday lives. However, machine learning is also influencing the direction of technology that is not as commonplace. Text to speech technology is a prime example. Text to speech technology predates machine learning by over a century. However, machine learning has made the technology more reliable than ever.

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KDnuggets

SEPTEMBER 24, 2019

Deep Learning is/has become the hottest skill in Data Science at the moment. There is a plethora of articles, courses, technologies, influencers and resources that we can leverage to gain the Deep Learning skills.

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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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