Analytics Vidhya
SEPTEMBER 29, 2020
Overview Winning data science competitions can be a complex process – but you can crack the top 3 if you have a framework to. The post How I Became a Data Science Competition Master from Scratch appeared first on Analytics Vidhya.
Analytics Vidhya
SEPTEMBER 27, 2020
Overview Microsoft Excel is an excellent tool for learning and executing statistical functions Here are 12 statistical functions in Excel that you should master. The post 10 Statistical Functions in Excel every Analytics Professional Should Know appeared first on Analytics Vidhya.
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O'Reilly on Data
SEPTEMBER 8, 2020
In a conversation with Kevlin Henney, we started talking about the kinds of user interfaces that might work for AI-assisted programming. This is a significant problem: neither of us were aware of any significant work on user interfaces that support collaboration. Most AI systems we’ve seen envision AI as an oracle: you give it the input, it pops out the answer.
Corinium
SEPTEMBER 21, 2020
COVID-19 has affected workplaces everywhere, the impacts of which could greatly alter how different organizations will approach the way they do business. The need to identify and prepare for a shift in operations and strategic goals is incredibly important. Organisations that respond efficiently can have a major role in establishing their companies as top competitors within their respective industries.
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.
BA Learnings
SEPTEMBER 26, 2020
According to a study conducted by IBM in 2012, companies that perform well tend to innovate their business models quite frequently, compared to underperformers. Innovation is essential to remaining competitive if a business is to stay afloat and remain relevant. History provides examples of companies that have lost out by missing out on opportunities to innovate – Think Motorola, Nokia, Lehman Brothers, Kodak, American Airlines – the list goes on.
MLWhiz
SEPTEMBER 1, 2020
Before I even begin this article, let me just say that I love iPython Notebooks, and Atom is not an alternative to Jupyter in any way. Notebooks provide me an interface where I have to think of “Coding one code block at a time,” as I like to call it, and it helps me to think more clearly while helping me make my code more modular. Yet, Jupyter is not suitable for some tasks in its present form.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Analytics Vidhya
SEPTEMBER 30, 2020
This article was published as a part of the Data Science Blogathon. Introduction The first step towards problem-solving in data science projects isn’t about. The post Hypothesis Generation for Data Science Projects – A Critical Problem Solving Step appeared first on Analytics Vidhya.
Smart Data Collective
SEPTEMBER 21, 2020
Two or three decades ago, gathering data was the biggest challenge businesses faced. Leaders craved more information and access. Today, these same companies are drowning in data. The challenge of today is organizing and making sense of the data. 4 Tips to Help You Make Sense of Your Data. With so much emphasis on collecting and accessing data, it’s easy to become so paralyzed by information that you fail to do anything with it.
Boris Evelson
SEPTEMBER 21, 2020
The perception of legacy enterprise business intelligence (BI) platforms comes with some legitimate stigma and baggage. It’s technology first, not business-led; the graphical user interface (GUI)-based user experience (UX) doesn’t address ease of use for all business decision-makers; there are too many underutilized reports and dashboards floating around in the enterprise; and signals produced by […].
BA Learnings
SEPTEMBER 26, 2020
According to a study conducted by IBM in 2012, companies that perform well tend to innovate their business models quite frequently, compared to underperformers. Innovation is essential to remaining competitive if a business is to stay afloat and remain relevant. History provides examples of companies that have lost out by missing out on opportunities to innovate – Think Motorola, Nokia, Lehman Brothers, Kodak, American Airlines – the list goes on.
Speaker: David Bard, Principal at VP Product Coaching
In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.
MLWhiz
SEPTEMBER 7, 2020
PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of. I remember picking PyTorch up only after some extensive experimentation a couple of years back. To tell you the truth, it took me a lot of time to pick it up but am I glad that I moved from Keras to PyTorch.
erwin
SEPTEMBER 17, 2020
Replace manual and recurring tasks for fast, reliable data lineage and overall data governance. It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.
Analytics Vidhya
SEPTEMBER 28, 2020
Overview Amazon Web Services (AWS) is the leading cloud platform for deploying machine learning solutions Every data science professional should learn how AWS works. The post What is AWS? Why Every Data Science Professional Should Learn Amazon Web Services appeared first on Analytics Vidhya.
Smart Data Collective
SEPTEMBER 17, 2020
Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. These terms are fundamentally tied predominantly to matters involving digital transformation as well as growth in companies. In this modern age, each business entity is driven by data. Data analytics are now very crucial whenever there is a decision-making process involved.
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.
Cloudera
SEPTEMBER 28, 2020
Cloudera delivers an enterprise data cloud that enables companies to build end-to-end data pipelines for hybrid cloud, spanning edge devices to public or private cloud, with integrated security and governance underpinning it to protect customers data. Cloudera has found that customers have spent many years investing in their big data assets and want to continue to build on that investment by moving towards a more modern architecture that helps leverage the multiple form factors.
FineReport
SEPTEMBER 2, 2020
Free data visualization tools are professional in different categories: dashboard, chart, maps, network, and so on. Today, let’s review the top free data visualization tools on the market. What are the Benefits of Using Free Data Visualization Tools? The most significant advantage is free, and open-source data visualization tools can help you control your budget.
TDAN
SEPTEMBER 1, 2020
Getting the business engaged with data governance can sometimes be a challenge. Any sort of driver to make that a more organic experience for your organization will be an asset. At NAIT (the Northern Alberta Institute of Technology), we have put together a process to visually identify and connect our reports to Data Governance. The […].
erwin
SEPTEMBER 10, 2020
Not Documenting End-to-End Data Lineage Is Risky Busines – Understanding your data’s origins is key to successful data governance. Not everyone understands what end-to-end data lineage is or why it is important. In a previous blog , I explained that data lineage is basically the history of data, including a data set’s origin, characteristics, quality and movement over time.
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Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.
Analytics Vidhya
SEPTEMBER 21, 2020
Overview NoSQL databases are ubiquitous in the industry – a data scientist is expected to be familiar with these databases Here, we will see. The post 5 Popular NoSQL Databases Every Data Science Professional Should Know About appeared first on Analytics Vidhya.
Smart Data Collective
SEPTEMBER 23, 2020
The concept of cryptocurrency is still foreign to so many in the United States and around the world. There is a lot more mass appeal of cryptocurrencies like Bitcoin, Litecoin, and others. Generally speaking, though, they are still mysterious in the eyes of the common individual. In the cryptocurrency market, we are starting to see the emergency and convergence of crypto and big data analytics.
Cloudera
SEPTEMBER 29, 2020
Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. . In this blog post, we compare Cloudera Data Warehouse (CDW) on Cloudera Data Platform (CDP) using Apache Hive-LLAP to Microsoft HDInsight (also powered by Apache Hive-LLAP) on Azure using the TPC-DS 2.9 benchmark.
Dataiku
SEPTEMBER 4, 2020
In the previous three posts in the How They Work (In Plain English!) series, we went through a high-level overview of machine learning and took a deep dive into two key categories of supervised learning algorithms — linear and tree-based models — and the most popular unsupervised learning technique, clustering. Today, we’ll dive into a second key unsupervised learning technique — dimensionality reduction.
Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO
The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.
TDAN
SEPTEMBER 1, 2020
We just finished a conversation with a client who was justifiably proud of having centralized what had previously been a very decentralized business function (in this case, it was HR, but it could have been any of a number of functions). They had seemingly achieved many of the benefits of becoming data-centric through decentralization: all […].
DataCamp
SEPTEMBER 1, 2020
All of DataCamp is completely free to everyone—again. Because we believe that everyone deserves the chance to become data literate safely at home.
Analytics Vidhya
SEPTEMBER 17, 2020
Introduction A virtual assistant, also called an AI assistant or digital assistant, is an application program that understands natural language voice commands and completes. The post How to create your AI Virtual Assistant using Python appeared first on Analytics Vidhya.
Smart Data Collective
SEPTEMBER 14, 2020
There is a common misconception prevalent amongst businesses that cyberattacks , and data breaches only target large scale enterprises. This is not true as almost half of the cyberattacks target small to midsize businesses. This misconception prevents businesses from taking data breaches and cybersecurity attacks seriously. They not only ignore it but also do nothing to protect themselves from it.
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Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.
Srividya Sridharan
SEPTEMBER 3, 2020
The digital future is here. And data, analytics, and AI are going to drive this future. These capabilities are becoming more crucial to stay ahead of uncertainty and change and get smarter about every aspect of your business: your customers, your suppliers and partners, your competitors, your employees, your processes, your operations, and your markets.
Dataiku
SEPTEMBER 17, 2020
Moving from one phase of AI maturity to the next is significantly easier said than done. In fact, a recent benchmarking study of senior executives in over 1,200 companies revealed how few organizations are truly advanced in their maturity — 20% are “beginners” in AI, 32% are “early implementers” starting to pilot AI and use simple applications, 33% are “advancers” using AI in key parts of their business and seeing gains, and only 15% are “leaders” widely using AI to drive tangible benefits.
Domino Data Lab
SEPTEMBER 10, 2020
Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. Yet despite all this hard work, few models ever make it into production (VentureBeat AI concluded that just 13% of data science projects make it into production) and in terms of delivering value to the business, Gartner predicts that only 20% of
Jet Global
SEPTEMBER 30, 2020
Webinar Date: 15 October, 2020 12 PM – 1 PM UTC. Register Now. We’ve considered challenges and solutions for tax forecasting in the short-term. How prepared are we for the risks and opportunities in the long-term? In this final session Susie Cooke, Partner, Deloitte Canada, alongside insightsoftware’s Jamie Eagan, VP Product Management, and Kathryn Abate, Pre Sales Director, Tax, will look further ahead; incorporating potential and/or likely longer-term government measures occurring
Advertiser: Data Robot
The buzz around generative AI shows no sign of abating in the foreseeable future. Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments. Ultimately, the market will demand an extensive ecosystem, and tools will need to streamline data and model utilization and management across multiple environments.
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