Analytics Vidhya
OCTOBER 28, 2020
This article was published as a part of the Data Science Blogathon. Introduction The share price of HDFC Bank is going up. It’s on. The post Predicting Stock Prices using Reinforcement Learning (with Python Code!) appeared first on Analytics Vidhya.
Dataconomy
OCTOBER 30, 2020
The DN Unlimited Conference will take place online for the first time this year More than 100 speakers from the fields of AI, machine learning, data science, and technology for social impact, including from The New York Times, IBM, Bayer, and Alibaba Cloud Fully remote networking opportunities via a virtual. The post Europe’s largest data science community launches the digital network platform for this year’s conference appeared first on Dataconomy.
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Smart Data Collective
OCTOBER 25, 2020
The IoT is becoming increasingly commercialized. IDC estimates that there will be 41.6 billion IoT devices online by 2025. As the IoT continues to expand, companies across the world are looking for new ways to embrace its potential. One of the most overlooked benefits of the IoT is with indoor mapping. Companies can find a number of useful IoT approaches to achieve this goal.
Data Science 101
OCTOBER 24, 2020
This is a great talk for data scientists and managers of technology teams. If you do data science in 2020 or beyond, there is a good chance the cloud will be involved. Topics covered: Lessons learned when migrating data science (or technology in general) to the cloud AI services available via different cloud providers Workflows in the cloud and more.
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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?
Eugene Yan
OCTOBER 3, 2020
As careers grow, how does the balance between writing & coding change? Hear from 4 tech leaders.
Alation
OCTOBER 13, 2020
The post Alation is the Clear Leader in The Forrester Wave™: Machine Learning Data Catalogs, Q4 2020 appeared first on Alation.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Twilio Segment
OCTOBER 28, 2020
Segment has developed a Customer Data Maturity Model that will help you understand where you are on your journey and provide specific recommendations on how to take your organization to the next level.
Smart Data Collective
OCTOBER 25, 2020
Machine learning has become a major game changer for the cryptocurrency industry. Most of the benefits are machine learning have been positive for the market. Machine learning is being used to predict price patterns more easily. However, some of these changes are not as welcome. Machine learning is making cryptocurrencies easier to trace. Since their inception, cryptocurrencies have gained popularity in several parts of the world.
DataCentric podcast
OCTOBER 27, 2020
It's all about cloud-native and containers as IBM announces a slew of new features and offerings at its annual IBM Tech U event. The announcements included: Spectrum Protect Plus enhancements for containers in production. Spectrum Scale container-native storage access for Red Hat OpenShift. Spectrum Discover now has deeper integration with Red Hat Openshift Container Storage & IBMB Cloud Digest Storage File Access Vmware vSphere 7 support for end-to-end NVMe Increased capacity for SCM in
Eugene Yan
OCTOBER 13, 2020
Step-by-step walkthrough on the environment, compilers, and installation for ScaNN.
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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.
Data Science 101
OCTOBER 24, 2020
This is a great talk for data scientists and managers of technology teams. If you do data science in 2020 or beyond, there is a good chance the cloud will be involved. Topics covered: Lessons learned when migrating data science (or technology in general) to the cloud AI services available via different cloud providers Workflows in the cloud and more.
Analytics Vidhya
OCTOBER 14, 2020
Introduction In the recent years, face recognition applications have been developed on a much larger scale. Image classification and recognition has evolved and is. The post Building an end to end image classification/recognition application appeared first on Analytics Vidhya.
Tableau
OCTOBER 13, 2020
Tanna Solberg October 13, 2020 - 9:19pm Courtney Totten Director, Academic Programs We use data everyday for our jobs and in our daily lives. Whether you’re optimizing your sales organization or researching climate change, understanding data is a key ingredient for success. Although most people are familiar with the current trend of growing amounts of data, there is a skills gap in data literacy - defined as the ability to explore, understand, and communicate with data.
Smart Data Collective
OCTOBER 1, 2020
Email has proven to be a remarkably resilient marketing medium. The ROI of email marketing can be up to 4,400%. However, email marketing is also rather complicated. Businesses that depend on email marketing need to take advantage of various types of technology to leverage it effectively. We have previously written about the benefits of data driven marketing , but wanted to focus more on the benefits of machine learning as well.
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.
DataCentric podcast
OCTOBER 22, 2020
Containers have been embraced by IT organizations across the board. No longer strictly limited to dev ops and non-production environments, we're starting to see production enterprise applications being deployed into Kubernetes-managed containers. These deployments quickly expose the limitations of the technology. Joining hosts Matt Kimball & Steve McDowell (both analysts at Moor Insights & Strategy) is Diamanti's vice present of product, Brian Walden.
Eugene Yan
OCTOBER 10, 2020
Building prototypes helped get buy-in when roadmaps & design docs failed.
learn data science
OCTOBER 12, 2020
I’m super excited to announce Exploratory v6.2! ??? As always, we have a bunch of new features & enhancements, here’s a quick overview of the following areas. Summary View Analytics Chart Data Wrangling Dashboard Parameter Summary View Reference lines for Mean & Midian Now you can see the mean and the median values as reference lines on top of the histogram charts for numerical columns.
Analytics Vidhya
OCTOBER 12, 2020
This article was published as a part of the Data Science Blogathon. Introduction I have always been in love with Data Visualization since the. The post Create a Word Cloud or Tag Cloud in Python appeared first on Analytics Vidhya.
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.
Alation
OCTOBER 13, 2020
The post Alation + Dataiku: Drive Greater Value from Data Science appeared first on Alation.
Smart Data Collective
OCTOBER 13, 2020
Deloitte Analytics author Ashwin Patil recently talked about the incredible benefits of big data in the automotive sector. His article focused primarily on the applications of big data in auto manufacturing. “At the same time, big data and analytics today offer previously unthinkable possibilities for tackling these and many other challenges automakers face.
DeepMind
OCTOBER 18, 2020
In an article recently published in Physical Review Research, we show how deep learning can help solve the fundamental equations of quantum mechanics for real-world systems. Not only is this an important fundamental scientific question, but it also could lead to practical uses in the future, allowing researchers to prototype new materials and chemical syntheses in silico before trying to make them in the lab.
Eugene Yan
OCTOBER 17, 2020
Not 'How to build a data science portfolio', but 'Whys' and 'Whats'.
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.
Twilio Segment
OCTOBER 6, 2020
We're about to enter the era of the customer data platform. How did we get here and where are we headed?
Analytics Vidhya
OCTOBER 7, 2020
This article was published as a part of the Data Science Blogathon. Introduction Exploratory Data Analysis(EDA) is one of the most underrated and under-utilized. The post Exploratory Data Analysis – The Go-To Technique to Explore Your Data! appeared first on Analytics Vidhya.
Alation
OCTOBER 2, 2020
The post Alation 2020.3: Discussing Customer-Centric Design with Colin Poindexter, Alation’s Product Design Lead appeared first on Alation.
Smart Data Collective
OCTOBER 21, 2020
Countless companies recognize the growing importance of big data. However, many of them lack the insights needed to acquire and utilize data effectively. There are so many possible sources of data, but they don’t tap them to their full advantage. One of the most overlooked sources of data is from inbound calls. Companies are finding new ways to leverage call data to their full advantage.
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.
DeepMind
OCTOBER 11, 2020
Imagine if you had to learn how to chop, peel and stir all over again every time you wanted to learn a new recipe. In many machine learning systems, agents often have to learn entirely from scratch when faced with new challenges. It’s clear, however, that people learn more efficiently than this: they can combine abilities previously learned. In the same way that a finite dictionary of words can be reassembled into sentences of near infinite meanings, people repurpose and re-combine skills they a
Eugene Yan
OCTOBER 24, 2020
What questions do they answer? How do they compare? What open-source solutions are available?
Christian Haschek
OCTOBER 31, 2020
If you've ever thought of building a file server, you probably had one or more of these thoughts
Analytics Vidhya
OCTOBER 5, 2020
This article was published as a part of the Data Science Blogathon. Overview Finishing in the top 10% in Machine Learning Hackathons is a. The post Ultimate Beginners Guide to Breaking into the Top 10% in Machine Learning Hackathons appeared first on Analytics Vidhya.
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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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