November, 2021

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The Benefits and Drawbacks of DataOps in Practice

DataKitchen

The post The Benefits and Drawbacks of DataOps in Practice first appeared on DataKitchen.

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Talend Data Fabric Simplifies Data Life Cycle Management

David Menninger's Analyst Perspectives

Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a un

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A Complete Beginner-Friendly Guide to SQL for Data Science

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. SQL stands for Structured Query Language which is used to deal with Relational Databases to query from and manipulate databases. In the field of Data Science most of the time you are supposed to fetch the data from any RDBMS and run some […]. The post A Complete Beginner-Friendly Guide to SQL for Data Science appeared first on Analytics Vidhya.

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Why Machine Learning Engineers are Replacing Data Scientists

KDnuggets

The hiring run for data scientists continues along at a strong clip around the world. But, there are other emerging roles that are demonstrating key value to organizations that you should consider based on your existing or desired skill sets.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence. There's no question that it is challenging to figure out where to focus and how to advance when it’s a new field that is evolving everyday. 💡 This new webinar featuring Maher Hanafi, VP of Engineering at Betterworks, will explore a practical framework to transform Generative AI pr

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How to Utilize Artificial Intelligence in Your eCommerce SEO Strategy

Smart Data Collective

If you have not lived under a rock for several years, you have undoubtedly heard about artificial intelligence (AI). However, how might artificial intelligence be used in e-commerce operations? Artificial intelligence (AI) is starting to fill every facet of our daily lives. For example, self-checkout cash registers, airport security checks, and other automated processes all use artificial intelligence to some degree.

Strategy 143
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11 Data Presentation Tips and Resources to Deliver More Client Value

Juice Analytics

Whether you are a consultant, marketer, researcher, or financial analyst…a big part of your job is presenting data. It takes a special combination of skills to articulate your insights and support them with effectively visualized data. You need to be part salesperson, part data analyst, and part author. We’ve collected 11 of the most useful tips and resources to help you improve how you present data.

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What Makes a Metric a KPI?

David Menninger's Analyst Perspectives

How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad.

Metrics 325
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The Fundamentals of Exploratory Data Analysis

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Table of Contents Introduction About the Dataset Let’s Go 2D Scatter Plot 3D Scatter Plot Pair Plot Histogram Univariate Analysis using PDF CDF Mean, Variance, and Standard Deviation Median, Percentile, Quantile, IQR, MAD Box Plot Violin Plot Multivariate Probability Density Contour Plot Final Note […].

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3 Differences Between Coding in Data Science and Machine Learning

KDnuggets

The terms ‘data science’ and ‘machine learning’ are often used interchangeably. But while they are related, there are some glaring differences, so let’s take a look at the differences between the two disciplines, specifically as it relates to programming.

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Why Is Data Consulting Essential For A New Business?

Smart Data Collective

More companies than ever are being driven by data. They use a number of important data analytics tools to help implement their functions more efficiently. Unfortunately, big data can be mysterious for many companies. Only 13% of companies with data strategies are meeting the objectives outlined in them. They need to know how to use it effectively to get the most value out of it.

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Leading the Development of Profitable and Sustainable Products

Speaker: Jason Tanner

While growth of software-enabled solutions generates momentum, growth alone is not enough to ensure sustainability. The probability of success dramatically improves with early planning for profitability. A sustainable business model contains a system of interrelated choices made not once but over time. Join this webinar for an iterative approach to ensuring solution, economic and relationship sustainability.

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Getting Started with Data Storytelling

Juice Analytics

How to get started with data storytelling? For the beginner — and even for the experienced data analyst or data scientist — data storytelling can be a vague, disorientating concept. This question posted on Reddit is a good example of the interest and confusion about the topic: …which was then followed by this pure-gold response: I hope to make data storytelling a bit more accessible by laying out some of the basic concepts and skills required.

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The vast majority of data engineers are burnt out. Those working in healthcare are no exception

DataKitchen

The post The vast majority of data engineers are burnt out. Those working in healthcare are no exception first appeared on DataKitchen.

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Analytic Ops: The Last Mile of Data Ops

David Menninger's Analyst Perspectives

Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps.

Analytics 300
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Build Face Recognition Attendance System using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction In this article, you will learn how to build a face-recognition system using Python. Face recognition is a step further to face detection. In face detection, we only detect the location of the human face in an image but in face recognition, we […]. The post Build Face Recognition Attendance System using Python appeared first on Analytics Vidhya.

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Navigating the Future: Generative AI, Application Analytics, and Data

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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Design Patterns for Machine Learning Pipelines

KDnuggets

ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.

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Using Machine Learning to Lower the Cost of 3D Printing

Smart Data Collective

Machine learning technology has become an integral part of many different design processes. Many entrepreneurs use machine learning to improve logo designs. However, there are a lot of other benefits as well. One of the areas where machine learning has proven particularly useful has been with 3D printing. 3D Printing is Crucial for Cost Optimization in 3D Printing.

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The Four Pillars of a Data Fluent Organization

Juice Analytics

When it comes to using data, many organizations are reminiscent of the famous poem “Rime of the Ancient Mariner”: “Water, water, every where, Nor any drop to drink”. Data is everywhere, but it seldom seems to quench the thirst for smarter decisions. (As a side-note, this poem originated the phrase “albatross around your neck” — which is how a lot of CIOs and CTOs feel with both the data and expectations.

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Battle for Data Pros Heats Up as Burnout Builds

DataKitchen

The post Battle for Data Pros Heats Up as Burnout Builds first appeared on DataKitchen.

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Get Better Network Graphs & Save Analysts Time

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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Using Event Data in Financial Services to Improve Business Processes

David Menninger's Analyst Perspectives

Our research shows that nearly all financial service organizations (97%) consider it important to accelerate the flow of information and improve responsiveness. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, financial service organizations can quickly turn events into valuable business outcomes in the form of new products and ser

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Performing Time Series Analysis using ARIMA Model in R

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Welcome to the World of Time Series Analysis! From this article, you will learn how to perform time series analysis using the ARIMA model (with code!). The dataset used in this article can be downloaded here. The usage time series data consist of the […]. The post Performing Time Series Analysis using ARIMA Model in R appeared first on Analytics Vidhya.

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Top Stories, Nov 15-21: 19 Data Science Project Ideas for Beginners

KDnuggets

Also: How I Redesigned over 100 ETL into ELT Data Pipelines; Where NLP is heading; Don’t Waste Time Building Your Data Science Network; Data Scientists: How to Sell Your Project and Yourself.

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5 Ways Machine Learning Can Boost Your Digital Marketing Efforts

Smart Data Collective

One of the best things about digital marketing is that it’s often at the forefront of the latest online technologies. It doesn’t get any more cutting-edge at the moment than machine learning, and it’s not only large companies that have already started to take advantage. As far back as 2018, a veritable eternity in the world of online marketing, over 80% of marketing organizations reported the deployment or growth of their AI and machine learning efforts.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

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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Why Metaversial Business Is a Very Long Way Off

Mark Raskino

[Reminder – these blogs are analyst personal opinion, not Gartner published research]. “Open up your firewalls to let your people access us!” said Philip Rosedale, founder of Second Life, as I recall. He was being interviewed on stage by my colleague Steve Prentice (now retired), who asked what the hundreds of CIOs and IT leaders in the audience could do to advance corporate use of immersive virtual worlds for business.

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Make Your Models Matter: What It Takes to Maximize Business Value from Your Machine Learning Initiatives

Cloudera

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. But, we also know that experimentation alone doesn’t yield business value. Organizations need to usher their ML models out of the lab (i.e., the proof-of-concept phase) and into deployment, which is otherwise known as being “in production”. .

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3 Trends in Financial Services and AI for 2022

Dataiku

Many of you reading this will either have first-hand experience with the challenges of achieving data-driven success within financial firms or will have a reasonable concern that such success will not come easily to your organization. It is certainly true that finding actionable insights within your organizations — and then actually taking meaningful action based on those insights — is not a trivial matter.

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How to Deploy Machine Learning(ML) Model on Android

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Solving Machine learning Problems in a local system is only not the case but making it able to community to use is important otherwise the model is up to you only. When it is able to serve then you came to know the feedback and […]. The post How to Deploy Machine Learning(ML) Model on Android appeared first on Analytics Vidhya.

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The Key to Sustainable Energy Optimization: A Data-Driven Approach for Manufacturing

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. 📊 Join us for a practical webinar hosted by Kevin Kai Wong of Emergent Ene

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Where NLP is heading

KDnuggets

Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.

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3 Strategies Employed by the Leading Enterprise Cybersecurity Platforms

Smart Data Collective

Much has changed since the time when organizations only knew of antiviruses and simple firewalls as the tools, they need to protect their computers. To address newer challenges, security providers have developed new technologies and strategies to combat evolving threats. Stephanie Benoit-Kurtz, Lead Area Faculty Chair for the University of Phoenix’s Cybersecurity Programs, offers a good summary of the changes security organizations should anticipate , especially in the time of the pandemic.

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Why Your Data Governance Strategy is Failing

TDAN

What is Data Governance and How Do You Measure Success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Answers will differ widely depending upon a business’ industry and growth strategy. But what […].

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Ten Things I’ve Learned in 20 Years in Data and Analytics

Teradata

Teradata's Martin Willcox recently passed 17 years at Teradata and a quarter of a century in the industry. Here are the ten things he's learned about data analytics in those 20-odd years.

Analytics 111
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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.