Dataiku
DECEMBER 13, 2019
New types of data, tools, and technologies are shaping the jobs of analysts, taking them in exciting new directions. In fact, things are moving so fast in the data analytics space, that some analysts are beginning to worry about what this could mean for the future of their jobs.
erwin
DECEMBER 12, 2019
Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance. So most early-stage data governance managers kick off a series of projects to profile data, make inferences about data element structure and format, and store the presumptive metad
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Corinium
DECEMBER 9, 2019
AI is now well into its ‘early adoption’ phase, with businesses throughout the Middle East and Africa clamoring to launch new initiatives.
Analytics Vidhya
DECEMBER 10, 2019
Overview The rise of artificial intelligence (AI) has disrupted many industries in recent years One of the most impacted industries – retail! Retail operations. The post 10 Exciting Real-World Applications of AI in Retail appeared first on Analytics Vidhya.
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datapine
DECEMBER 11, 2019
“Software as a service” (SaaS) is becoming an increasingly viable choice for organizations looking for the accessibility and versatility of software solutions and online data analysis tools without the need to rely on installing and running applications on their own computer systems and data centers. SaaS is taking over the cloud computing market. Gartner predicts that the service-based cloud application industry will be worth $143.7 billion by 2022—a level of growth that will shape SaaS trends
O'Reilly on Data
DECEMBER 10, 2019
Roughly a year ago, we wrote “ What machine learning means for software development.” In that article, we talked about Andrej Karpathy’s concept of Software 2.0. Karpathy argues that we’re at the beginning of a profound change in the way software is developed. Up until now, we’ve built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Analytics Vidhya
DECEMBER 11, 2019
Overview Streaming data is a thriving concept in the machine learning space Learn how to use a machine learning model (such as logistic regression). The post How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark appeared first on Analytics Vidhya.
Data Science 101
DECEMBER 12, 2019
It is Computer Science Education Week and in 2019 Machine Learning and Artificial Intelligence are two of the most popular and influential topics in technology. That is why I was so excited when Code.org launched a training specifically aimed at the topics. It is called AI for Oceans and it is geared for children (or really anyone, I had fun with it and so did my children).
O'Reilly on Data
DECEMBER 12, 2019
For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Without being able to troubleshoot models when they underperform or misbehave, organizations simply won’t be able to adopt and deploy ML at scale.
Corinium
DECEMBER 10, 2019
Navigating A Digital World.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Analytics Vidhya
DECEMBER 8, 2019
Overview Feature engineering is a skill every data scientist should know how to perform, especially in the case of time series We’ll discuss 6. The post 6 Powerful Feature Engineering Techniques For Time Series Data (using Python) appeared first on Analytics Vidhya.
KDnuggets
DECEMBER 12, 2019
Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python.
O'Reilly on Data
DECEMBER 9, 2019
Three reasons why confidence intervals should not be used in financial data analyses. Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment.
DataCamp
DECEMBER 10, 2019
The definitive guide to help you become a data engineer.
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The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
Analytics Vidhya
DECEMBER 9, 2019
Overview Game Theory can be incredibly helpful for decision making in competitive scenarios Understand the concept of Normal Form Games in the context of. The post Game Theory 101: Decision Making in a Competitive Scenario using Normal Form Games appeared first on Analytics Vidhya.
KDnuggets
DECEMBER 13, 2019
We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
Dataiku
DECEMBER 12, 2019
Want to learn about what's hot (and what's not) in AI for 2020? Look no further than Season 2, Episode 3 of The Banana Data Podcast, where hosts Triveni Gandhi and Will Nowak unpack topics like AutoML, explainable AI, cloud computing, federated learning, and more.
Smart Data Collective
DECEMBER 11, 2019
In 2013, Wired published a very interesting article about the role of big data in the field of integrated business systems. Author James Kobielus, the lead AI and data analyst for Wikibon and former IBM expert, said that there are a number of ways that integrated business systems are tapping the potential of AI and big data. -Kobielus points out that every vertical is finding ways to use big data to improve its competitive advantages.
Speaker: Tamara Fingerlin, Developer Advocate
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
Jet Global
DECEMBER 13, 2019
It’s easy to think of enterprise performance reporting as a necessary evil. Companies need reports to evaluate their success objectively and plan their next move strategically. Yet reporting is a complex, time-consuming process that can leave those responsible feeling frustrated by how much effort is involved. PeopleSoft is a valuable tool for enterprise data collection, full of insights companies need to find and leverage.
KDnuggets
DECEMBER 10, 2019
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
FineReport
DECEMBER 11, 2019
In the past few years, the term “data science” has been widely used, and people seem to see it in every field. “Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis. However, before you get started, you can’t help but ask questions: is it suitable for me to learn data analysis?
DataRobot
DECEMBER 12, 2019
Back in 2012, Harvard Business Review called data scientists “the sexiest job of the 21st century.” That may or may not be true, but I do believe that one of the hardest jobs in the latter half of this decade is that of the executive responsible for developing and implementing AI strategy in the enterprise. It’s a difficult job for a number of reasons.
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Sales and marketing leaders have reached a tipping point when it comes to using intent data — and they’re not looking back. More than half of all B2B marketers are already using intent data to increase sales, and Gartner predicts this figure will grow to 70 percent. The reason is clear: intent can provide you with massive amounts of data that reveal sales opportunities earlier than ever before.
Jet Global
DECEMBER 11, 2019
Compliance is complicated. That’s the sentiment echoed by 800 senior compliance officers responding to a Thompson Reuters survey. When asked to rank their top challenges, most put managing continuing compliance changes at the top of the list. Every year, regulatory frameworks ranging from Sarbanes Oxley to the NCAA rules undergo updates and revisions.
KDnuggets
DECEMBER 9, 2019
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.
FineReport
DECEMBER 10, 2019
Why We Need Data Cleaning?. Data analysis is a time-consuming task, but are you prepared before the data analysis, and have you omitted the important step: data cleaning? From Google. In the process of data analysis, data cleaning is such a preliminary preparation after data extraction. For data scientists, we will encounter all kinds of data. Before analyzing, we need to invest a lot of time and energy to “organize and trim” the data to the way we want or need.
DataRobot
DECEMBER 10, 2019
Gartner just named hyperautomation as one of its Top 10 Strategic Technology Trends for 2020. This makes it a trend that “enterprises need to consider” as part of their technology plans and which will have a “profound impact on people…across industries and geographies, with significant potential for disruption”.
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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
Sisense
DECEMBER 9, 2019
Blog. Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. Our BI Best Practices demystify the analytics world and empower you with actionable how-to guidance. Preserving insights. One of the biggest pitfalls in data is the preservation of insights when analysis is handed off from the data team to a business professional.
KDnuggets
DECEMBER 9, 2019
Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.
MLWhiz
DECEMBER 8, 2019
Algorithms are an integral part of data science. While most of us data scientists don’t take a proper algorithms course while studying, they are important all the same. Many companies ask data structures and algorithms as part of their interview process for hiring data scientists. Now the question that many people ask here is what is the use of asking a data scientist such questions.
DataRobot
DECEMBER 9, 2019
You’ve probably seen amazing AI news headlines such as: AI can predict earthquakes. Using just a single heartbeat, an AI achieved 100% accuracy predicting congestive heart failure. A new marketing model is promising to increase the response rate tenfold. It all seems too good to be true. But as the modern proverb says, "If it seems too good to be true, it probably is.".
Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com
Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.
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