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Modern tools like Incorta can dramatically improve the effectiveness of an organization's analytics while virtually eliminating the need for traditional, slow and expensive data pipelines. But rather than minimize the need for data engineering, these new analytics tools actually make the role even more critical. 

 

Elevated to a new position of prominence, data engineers can finally greatly expand self-serve access to data, dramatically reduce cycle times, and significantly improve the accuracy and timeliness of organizational data.

12 Desde Data Science Central

 

Reduce risk with analytics-based  credit scoring Credit scoring is complex and involves  high volumes of data – it is not efficient  or cost-effective to perform manually.  Thankfully, the use of artificial intelligence  and machine learning is well established  and widespread in the credit risk sector. how an analytics-based credit scoring  model as part of a complete enterprise  decision management system can help  you mitigate credit risk and reduce  costs for your organization.

12 Desde Data Science Central

 

Key tips for accurate and successful 

predictive models

The AI market is booming, with the total global

 revenue for AI software, hardware, and 

services projected to soon reach $554 billion.

AI and ML underpin many core enterprise operations. 

This is especially true of predictive modeling, which

 turns historical data into behavioral insights and

 predictions about future behavior.

Download the white paper and eBook below to 

discover the limitations of relying on first-party

 data and why the budding data-centric AI

 approach is the new way forward:

• The Missing Ingredient to Predictive
•  Modeling Success
• Increasing Predictive Modeling 
• Accuracy with Data-Centric AI
  

Chris Carter
Data Science Central

12 Desde Data Science Central

 

La parte más difícil de ser analista no es crear el panel (dashboard), es obtener los datos en un estado en el que pueda obtenerse información. Los datos siempre están desordenados, en muchos lugares diferentes y, a menudo, en diferentes plataformas, lo que hace que el trabajo de back-end ocupe la mayor parte del tiempo para crear un tablero.

 

Hay que descubrir el proceso matizado de crear un conjunto de datos y un tablero que se utilizará por completo.

12 Desde Data Science Central

 

Augmented Intelligence: Celebrating Human Expertise

Data Science Central

 

The most powerful computers on the planet still can't perform some of the tasks a human toddler is capable of. Join us on Wednesday, July 28th at 11am EDT for a conversation will discuss why you need both human and artificial intelligence, as well as how to increase your chances of success with AI by involving your subject matter experts.

In this webinar, we will discuss:

Human intelligence

Artificial intelligence

The meaning of augmented intelligence and why it is needed

How to best leverage augmented intelligence to convince stakeholders that AI systems are working and delivering value

 

As we enter 2021, AI can do some incredible things. While human tasks can be automated, we still need humans to fully understand how to leverage AI for success.

The human brain is a remarkable tool that discovered general relativity, mRNA vaccines, and, in this generation, may even take humans to Mars. This talk will dive deep into human intelligence, the history of artificial intelligence, and the key differences between them. The talk will introduce the concept of augmented intelligence where humans work in tandem with computers and discuss what this means for society.

12 Desde Data Science Central

 

Data Science Demo with Snowflake and Dataiku

Organizations looking to take their models into production often struggle to integrate trained models with the continuously growing volumes of production data in a way that is secure and scalable. Join us in this live demo to see how Dataiku is simplifying this process by removing the need to move data from where it is securely stored with scalable model inference inside Snowflake’s processing engine. Taking AI to the Next Level with Model Inference in Snowflake with Dataiku  Organizations looking to take their models into production often struggle to integrate trained models with the continuously growing volumes of production data in a way that is secure and scalable. Join us in this live demo to see how Dataiku is simplifying this process by removing the need to move data from where it is securely stored with scalable model inference inside Snowflake’s processing engine. IN THIS DEMO YOU’LL SEE HOW TO: Incorporate third party data from Snowflake Data Marketplace into your models Prepare data and perform feature engineering using Java UDFs Train and evaluate a predictive model using Dataiku Deploy model to Snowflake as Java UDF and monitor in production

12 Desde Data Science Central

 

La parte más difícil de ser analista no es crear el panel (dashboard), es obtener los datos en un estado en el que pueda obtenerse información. Los datos siempre están desordenados, en muchos lugares diferentes y, a menudo, en diferentes plataformas, lo que hace que el trabajo de back-end ocupe la mayor parte del tiempo para crear un tablero.

 

Hay que descubrir el proceso matizado de crear un conjunto de datos y un tablero que se utilizará por completo.

12 Desde Data Science Central

 

DSC Featured Articles

• Deep visualizations to Help Solve Riemann's Conjecture
• Facial Recognition Biometric Technology gaining wide popularity for advertising and marketing
• How the Blend of Artificial Intelligence and Big Data Is Helping Industries During The Pandemic
• Triumvirate – AI, IoT, and Blockchain – Converge to Revolutionize Global Businesses!
• Encyclosearch.org is launched
• How Data Science Ensures that Customer’s Needs are Always at the Forefront
• Are You Excited About Getting Vaccinated? Don’t Let Overconfidence Make You A Super Spreader!
• Women in Politics | Exploratory Data Analysis in R
• FinTech: How AI is Improving This Industry
• Top Advanced Analytics Use Cases
• How poor data quality impacts your business?
• 20 Of The Most Important Machine Learning Interview Questions and Answers
• Data Science Central Weekly Digest, 4 Jan 2021

FinTech: How AI is Improving This Industry

•  by Ryan Williamson 

FinTech has achieved staggering growth levels over the past few years, establishing itself as one of the mainstays of the modern world. And as customers become more and demanding and the competition in the market continues to rise, the focus on this sector and the many technologies that aid and enable it has grown too. Take artificial intelligence and machine learning, for example; this potent technology has already demonstrated a wide scope of application in this sector, especially thanks to its ability to execute tasks that generally need human intelligence. Now, as the FinTech industry also becomes increasingly popular, its union with AI was only a matter of time.

In fact, according to a recent study, AI’s share in the FinTech sector will touch about $35.4 billion in value by 2025. This staggering figure begs the question: What exactly is artificial intelligence contributing to the financial technology market? Well, the answer to that question is: Plenty. For starters, FinTech companies all over the world are now tapping into artificial intelligence to offer enhanced services, such as online financial coaching and advice, to their customers. Now, allow us to walk you through some of the other critical AI applications in this industry.

1. Fraud prevention: As the market offers an increasing number of mediums for transactions, fraud concerns have also gone up. AI can tend to these concerns since it can identify fraudulent activities based on the already established baseline for standard customer behavior. It, then, not only flags such potential transactions but also enables measures aimed at the prevention of fraudulent activities.
2. Automatic claim process: To offer tailored services to customers, financial institutions are now using AI to provide exceptional, high-quality services. Case in point, automation of the claims process. AI-based chatbots help with that by engaging with customers, collecting data, evaluating the claim's veracity, and triggering the claim process. This not only accelerates the whole process but also allows employees to focus on other essential tasks.
3. Customer support: It is unlikely you have missed business’s increase in customer experience in the past decade or so. This has been an essential area of focus for the FinTech market as well. But how is the sector going about it? With help from artificial intelligence, of course. AI has brought along a plethora of tools, including intelligent chatbots, that serve to ensure the delivery of top-notch customer support round the clock. A study predicts that AI can also help the FinTech industry save as much as 22 percent costs too. That’s about $1 trillion, in case you were wondering.

As the world continues to generate a staggering amount of data with every passing day, the scope of AI application in this sector is bound to grow for the foreseeable future. And suppose you too wish to leverage it to your company’s benefit. In that case, we recommend engaging the services of an expert fintech application development company at the earliest.

12 Desde Data Science Central

 

Building Fair AI: A Practical Guide

The stories of bias in AI are everywhere: Amazon’s recruiting tool, Apple’s credit card limits, Google’s facial recognition, and dozens more. The quick solution is just to blame the algorithm and its designers. But it’s not a question of whether or not you have bias in your institution, but rather how you plan to handle it.

In part 2 of 2 of this latest Data Science Central podcast, ‘How to Start Tackling AI Bias, Part 2: Building Fair AI’, Jett Oristaglio, Data Science Product Lead of Trusted AI at DataRobot, takes a deeper dive into how to tackle AI bias, including:

• How machine learning can highlight implicit bias in institutions and using AI to measure and change it
• Implementing a practical plan to improve your AI development and increase trust in your AI

Speaker: Jett Oristaglio, Data Science Product Lead of Trusted AI - DataRobot

Hosted by: Sean Welch, Host and Producer - Data Science Central

Download Now >

12 Desde Data Science Central

 

Wrangling the Data Abyss: Scoping

The toughest part of being an analyst isn’t building the dashboard; it's getting data into a state where you can gain insights. Data is messy and often scattered across multiple platforms, making the back end work frustrating and time-consuming. 

In part one of the Wrangle the Data Abyss series, we take a deep dive into the most important and often overlooked step of the analytics process: scoping.

 Listen to this latest Data Science Central podcast to learn how to best handle initial requests, thoroughly evaluate the available data, and create a plan for success.

Speaker: Lauren Alexander, Senior Marketing Analyst - Tableau Hosted by: Sean Welch, Host and Producer - Data Science Central

Download Now

12 Desde Data Science Central

 

Sponsored News from Data Science Central

The Human Side of AI Bias The stories of bias in AI are everywhere: Amazon’s recruiting tool, Apple’s credit card limits, Google’s facial recognition, and dozens more. The quick solution is just to blame the algorithm and its designers. But it’s not a question of whether or not your institution has bias, but rather how you plan to handle it. In part 1 of 2 of this latest Data Science Central podcast, ‘How to Start Tackling AI Bias, Part 1: The Human Side of AI Bias’, Jett Oristaglio, Data Science Product Lead of Trusted AI at DataRobot, will explore how to handle both general and AI bias, as well as: How to think about bias as it relates to AI specifically, and the human systems that it is used in Reframing the conversation around AI to understanding it as the first step in building a more ethical system Speaker: Jett Oristaglio, Data Science Product Lead of Trusted AI - DataRobot Hosted by: Sean Welch, Host and Producer - Data Science Central Download Now >

Desde Data Science Central

 

Here is our selection of featured technical resources and articles posted since Monday:

Announcements

• It’s supposed to be one of the most exciting jobs. But data scientists spend a lot of time doing things they don’t really enjoy.
• Data Mastering @ Scale with Michael Stonebraker

Resources

• Architectures Data Scientists And Big Data Engineers Should Know
• How to Handle Missing Data
• Quantitative Trading 101
• Understanding Artificial Neural Networks
• The Journey to Optimizing Billion-scale Image Search

Articles

• Big Data: The Most Misunderstood Industry of 2020?
• How AI is Reshaping the Manufacturing Industry
• Here's Why Your Customer Success Needs Data Science
• Mobile App Development: How Big Data Affects it
• How HR Technologies are Changing HR practices?

Enjoy the reading!

Desde Data Science Central

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link. Announcement Unify your analytics, and leave your data where it is - The Race for a Unified Analytics Warehouse

Featured Resources and Technical Contributions Feature engine python package for feature engineering Calculus For Data Science: What Do You Really Need to Know? Introduction to Dropout to regularize Deep Neural Network + Machine Learning with TensorFlow for Absolute Beginners - Part 1 50 Fascinating and Free Data Sources for Data Visualization Milestones of Deep Learning Implementing Dynamic Data Update and Query Advantages And Disadvantages Of Flutter App Development Featured Articles Why do some traditional engineers not trust Data Science? China's Quest for AI Dominance - And How It's Going Prion Computer Viruses For China, With Love AI: Does it Impact Online Proctoring? 5 Ways AI Voice Synthesis Will Change Our Lives E-Commerce: The Many Ways in Which IoT Impacts it Education software development behind curtains How does the Automated Valuation Model work in the real estate industry? Picture of the Week Source for picture: contribution marked with a + From our Sponsors Selected Data Science Programs from Top Universities Data Science Leadership Exchange: Best Practices for Driving Outcomes Find out now: Importance of unifying your analytics O'Reilly ebook: Machine Learning at Enterprise Scale eBook: Accelerating Spark 3 The State of AI and Machine Learning in 2020 Join the world's largest data visualization contest Executive series: Strategies for success (and pitfalls to avoid) Get Faster Return On Data With Dataops And Lumada Unify your analytics, and leave your data where it is - The Race for a Unified Analytics Warehouse

Desde Data Science Central,

Here is our selection of featured technical resources and articles posted since Monday:

Resources

• Accelerated AI and Advanced Graphics to Accelerate Multiple Workloads
• Traditional vs Deep Learning Algorithms in the Telecom Industry
• Statistical Distributions in One Picture
• Selected Data Science Programs from Top Universities
• Adding Uncertainty to Deep Learning
• How to Become a Data Scientist - On your own

Articles

• Why we need more Bayesian trained data scientists than frequentist post COVID 19
• Reinforcement Learning Starts to Deliver on Its Promise
• 6 Steps to Get the Best Out of Your RPA Implementation
• 5 ways to improve the adoption of data science in your organization
• Supply Chain Analytics Need to Be Smarter
• 8 Most-Used Data Science and Machine Learning Services for 2020
• Fitting Human IQ into AI - Where and When It's a Win-Win

Enjoy the reading!

Desde Data Science Central

Articles

• Explaining Data Science to a Non-Data Scientist
• AI replacing journalists and what it means for jobs
• The VC's Guide to Machine Learning
• Visualizing the Performance of COVID Models
• Gamification and Simulation in Education and Corporate Learning

Desde Data Science Central

Optimization and The NFL's Toughest Scheduling Problem Join us for the latest DSC Webinar on June 23th, 2020

Learn how the National Football League (NFL) uses mathematical optimization to solve one of the hardest scheduling problems in existence. At first glance, the NFL’s scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. Making the problem particularly hard is the necessary inclusion of thousands of constraints addressing stadium availability, travel considerations, competitive equity, and television viewership. In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi’s mathematical optimization solver to tackle this complex scheduling problem. With mathematical optimization, NFL planners can generate and analyze more than 50,000 feasible schedules despite adding more constraints to the process every year. Now rather than spending months manually constructing one schedule, the NFL planners can focus on evaluating and comparing thousands of completed schedules to determine which should be selected as the final schedule. In this webinar, you will learn: How the NFL uses mathematical optimization to solve one of the most challenging scheduling problems in existence. How the NFL switched from a linear to a parallel approach to optimization. Speaker: Mike North, Vice President of NFL Broadcast Planning & Scheduling -- NFL Hosted by: Sean Welch, Host and Producer -- Data Science Central   Title:  Optimization and The NFL's Toughest Scheduling Problem Date:  Tuesday, June 23th, 2020 Time:  9 AM - 10 AM PDT