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Addressing the Key Mandates of a Trendy Mannequin Danger Administration Framework (MRM) When Leveraging Machine Studying 

It has been over a decade for the reason that Federal Reserve Board (FRB) and the Workplace of the Comptroller of the Forex (OCC) revealed its seminal steering centered on Mannequin Danger Administration (SR 11-7 & OCC Bulletin 2011-12, respectively). The regulatory steering introduced in these paperwork laid the muse for evaluating and managing mannequin danger for monetary establishments throughout the USA. In response, these establishments have invested closely in each processes and key expertise to make sure that fashions used to help important enterprise choices are compliant with regulatory mandates.

Since SR 11-7 was initially revealed in 2011, many groundbreaking algorithmic advances have made adopting subtle machine studying fashions not solely extra accessible, but in addition extra pervasive inside the monetary providers business. Now not is the modeler solely restricted to utilizing linear fashions; they could now make use of various information sources (each structured and unstructured) to construct considerably increased performing fashions to energy enterprise processes. Whereas this offers the chance to vastly enhance the establishment’s working efficiency throughout completely different enterprise capabilities, the extra mannequin complexity comes at the price of vastly elevated mannequin danger that the establishment has to handle.

Given this context, how can monetary establishments reap the advantages of recent machine studying approaches, whereas nonetheless being compliant to their MRM framework? As referenced in our introductory put up by Diego Oppenheimer on Model Risk Management, the three important elements of managing mannequin danger as prescribed by SR 11-7 embody:

  1. Mannequin Growth, Implementation and Use
  2. Mannequin Validation
  3. Mannequin Governance, Insurance policies, and Controls

On this put up, we are going to dive deeper into the primary part of managing mannequin danger, and have a look at alternatives at how automation supplied by DataRobot brings about efficiencies within the growth and implementation of fashions. 

Creating Sturdy Machine Studying Fashions inside a MRM Framework

If we’re to remain compliant whereas making use of machine studying methods, we should demand that the fashions we construct are each technically appropriate of their methodology and in addition utilized inside the acceptable enterprise context. That is confirmed by SR 11-7, which asserts that mannequin danger arises from the “hostile penalties from choices based mostly on incorrect or misused mannequin outputs and reviews.” With this definition of mannequin danger, how will we make sure the fashions we construct are technically appropriate? 

Step one can be to guarantee that the info used at first of the mannequin growth course of is completely vetted, in order that it’s acceptable for the use case at hand. To reference SR 11-7: 

The info and different data used to develop a mannequin are of important significance; there ought to be rigorous evaluation of information high quality and relevance, and acceptable documentation.

This requirement makes certain that no defective information variables are getting used to design a mannequin, so inaccurate outcomes should not outputted. The query nonetheless stays, how does the modeler guarantee this?  

Firstly, they have to guarantee that their work is instantly reproducible and could be simply validated by their friends. Via DataRobot’s AI Catalog, the modeler is ready to register datasets that may subsequently be used to construct a mannequin and annotate it with the suitable metadata that describes the datasets’ perform, origin, in addition to supposed use. Moreover, the AI Catalog will mechanically profile the enter dataset, offering the modeler a fowl’s eye overview of each the content material of the info and its origins. If the developer subsequently pulls a more moderen model of the dataset from a database, they can register it and maintain monitor of the completely different variations.

The advantage of the AI Catalog is that it helps to foster reproducibility between builders and validators and ensures that no datasets are unaccounted for throughout the mannequin growth lifecycle. 

Determine 1: AI Catalog inside DataRobot offers important capabilities for information administration, model monitoring, in addition to profiling.

Secondly, the modeler should be sure that the info is free from any potential high quality points that will adversely impression mannequin outcomes. At first of a modeling undertaking, DataRobot mechanically performs a rigorous information high quality evaluation, which checks for and surfaces frequent information high quality points. These checks embody:

  1. Detecting instances of redundant and non-informative information variables and eradicating them
  2. Figuring out probably disguised lacking values
  3. Flagging each outliers and inliers to the person
  4. Highlighting potential goal leakage in variables

For an in depth description of all the info high quality checks DataRobot performs, please seek advice from the Data Quality Assessment documentation. The advantage of including automation in these checks is that it not solely catches sources of information errors the modeler could have missed, nevertheless it additionally permits them to shortly shift their consideration and concentrate on problematic enter information variables that require additional preparation. 

Determine 2: Output of the automated Information High quality Evaluation supplied by DataRobot, flagging potential information points to the modeler.

As soon as we’ve the info in place, the modeler should then guarantee they design their modeling methodologies in a fashion that’s supported by concrete reasoning and backed by analysis. The significance of mannequin design is additional bolstered by the steering articulated in SR 11-7:

The design, principle, and logic underlying the mannequin ought to be nicely documented and customarily supported by revealed analysis and sound business follow.

Within the context of constructing machine studying fashions, the modeler has to make a number of choices close to partitioning their information, setting characteristic constraints, and choosing the suitable optimization metrics. These choices are all required to make sure they don’t produce a mannequin that overfits current information, and generalizes nicely to new inputs. Out of the field, DataRobot offers clever presets based mostly upon the inputted dataset and affords flexibility to the modeler to additional customise the settings for his or her particular wants. For an in depth description of the all design methodologies supplied, please seek advice from the Advanced Options documentation.

Determine 3: Superior Choices provides the modeler additional flexibility to make sure the design of the mannequin suits the wants of the mannequin customers

Lastly, whereas designing a correct model methodology is a important and crucial prerequisite for constructing technically sound options, it’s not ample by itself to adjust to the steering supplied in MRM frameworks. To elaborate, when approaching enterprise issues utilizing machine studying, modelers could not all the time know what mixture of information, characteristic preprocessing methods, and algorithms will yield the most effective outcomes for the issue at hand. Whereas the modeler could have a favourite modeling strategy, it’s not all the time assured that it’s going to yield the optimum answer. This sentiment can be captured within the steering supplied by SR 11-7: 

Comparability with various theories and approaches is a basic part of a sound modeling course of.

A serious problem that this offers the modeler is that they need to spend giant quantities of time growing further mannequin pipelines and experiment with completely different fashions and information processing methods to see what’s going to work finest for his or her explicit software. When kicking off a brand new undertaking in DataRobot, the modeler is ready to automate this course of, and concurrently check out a number of completely different modeling approaches to match and distinction their efficiency. These completely different approaches are captured in DataRobot’s Model Leaderboard, which highlights the completely different Blueprints, and their efficiency towards the enter dataset. 

Determine 4: DataRobot’s Mannequin Leaderboard showcases a number of completely different modeling approaches, utilizing all kinds of recent machine studying algorithms.

Along with mechanically creating a number of machine learning pipelines, DataRobot offers the modeler further flexibility by Composable ML to instantly modify the blueprint, so they could additional experiment and customise their mannequin to fulfill enterprise wants. In the event that they want to usher in their very own code to customise particular elements of the mannequin, they’re empowered to take action by Custom Tasks — enabling the developer to inject their very own area experience to the issue at hand. 

Determine 5: Customizable Blueprints permits the modeler to experiment with further characteristic engineering and information preprocessing methods to guage competing approaches.
Determine 6: Customized Duties allow the modeler to deliver their very own code to the blueprint, offering them a method to inject their area experience into the mannequin.

Conclusion

Algorithmic advances prior to now decade have supplied modelers with a greater variety of subtle fashions to deploy in an enterprise setting. These newer machine studying fashions have created novel mannequin danger that must be managed by monetary establishments. Utilizing DataRobot’s automated and steady machine studying platform, modelers cannot solely construct leading edge fashions for his or her enterprise functions, but in addition have instruments at their disposal to automate most of the laborious steps as mandated of their MRM framework. These automations allow the info scientist to concentrate on enterprise impression and ship extra worth throughout the group, all whereas being compliant. 

In our subsequent put up, we are going to proceed to dive deeper into the varied elements of managing mannequin danger and talk about each the most effective practices for mannequin validation and the way DataRobot is ready to speed up the method.

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In regards to the creator

Harsh Patel
Harsh Patel

Buyer-Dealing with Information Scientist at DataRobot

Harsh Patel is a Buyer-Dealing with Information Scientist at DataRobot. He leverages the DataRobot platform to drive the adoption of AI and Machine Studying at main enterprises in the USA, with a selected focus inside the Monetary Companies Trade. Previous to DataRobot, Harsh labored in a wide range of data-centric roles in each startups and main enterprises, the place he had the chance to construct many information merchandise leveraging machine studying.
Harsh studied Physics and Engineering at Cornell College, and in his spare time enjoys touring and exploring the parks in NYC.

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Top 5 AI Business Technologies to Watch Out in 2022

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Top 5 AI Business Technologies to Watch Out in 2022

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The internet age has brought many new technologies, and artificial intelligence (AI) is one of the most important. AI technology can be used in many fields and help organizations improve their operations. Some businesses use AI to boost customer service, while others use it to make greater profits by targeting potential customers with personalized offers. This article will discuss the top five AI business technologies that could be adopted by 2022.

Virtual Receptionist Software

Virtual receptionist software is an effective way to supply your prospects a customized expertise. The software program solutions inbound calls, serving to prospects discover info and schedule appointments. It can be used for companies closed throughout regular working hours, like hospitals or grocery shops (for instance).

Digital receptionist software program performs greatest when it’s arrange with a transparent understanding of your model’s messaging and tone of voice. Typically the digital receptionist might want to use phrases that replicate this character. It will be important as a result of it helps you stand out from corporations utilizing the identical know-how.

Sensible Calendar Options

Sensible calendars have gotten more and more essential for companies and people as we transfer towards a extra paperless society. AI can assist streamline this course of, permitting you to arrange conferences, appointments, and reminders simply. With sensible calendar options, it’s straightforward to create occasions that suit your schedule whereas additionally bearing in mind the provision of everybody concerned. Suppose you may have an appointment with somebody on Tuesday at 9 am however want to alter the time as a result of they’ve an early assembly at 7 am. In that case, your sensible calendar will robotically replace their availability and allow you to know once they’re free once more as a way to reschedule accordingly.

These identical options may also permit companies utilizing AI solutions like clever assistants or enterprise bots (like Siri) to schedule duties reminiscent of making cellphone calls or sending emails with out human intervention. They’ll even deal with follow-up duties like setting reminders for many who missed an preliminary assembly as a result of they forgot about it till it was too late. Once more because of the ability of machine studying algorithms which be taught from previous failures to enhance future outcomes.

AI Chatbots

AI Chatbots are used to reply easy questions conversationally. They’re additionally used to reply advanced questions however use pure language processing know-how to know and reply as a human would.

AI Chatbots can be utilized in real-time or asynchronously, relying on the aim of their use. For instance, an AI Chatbot can be utilized for stay assist if you wish to join along with your prospects by way of chat purposes like Fb Messenger or WhatsApp Messenger 24/7 through the use of pure language processing (NLP) know-how. Therefore saving corporations from hiring people for this activity since these instruments price lower than hiring somebody who works full-time hours day-after-day at an organization’s workplace location(s).

Private Assistants

Private assistants are a type of AI. They’ll carry out duties which can be time-consuming and tedious, or they can be utilized to automate processes inside a company by primarily performing the be just right for you. In 2022, over 95% of smartphone customers are actively utilizing voice assistants on their telephones, in keeping with a report.

Private assistants can be found in your smartphone or different units, permitting them to observe you wherever you go. It makes them particularly helpful for scheduling conferences, setting reminders, and plenty of different duties.

Private assistants could be accessed by way of apps reminiscent of Siri, Google Assistant, and Cortana, which make it straightforward to benefit from their options even when your palms are full or if there’s one thing in entrance of your face that forestalls handbook enter (like driving).

Predictive Analytics

Predictive analytics is a technique of forecasting future occasions. It makes use of historic knowledge to foretell the likelihood of an end result. A method to consider predictive analytics is that it permits you to predict buyer conduct, worker conduct, or tools failure based mostly on historic knowledge.

For instance, you should utilize predictive analytics to trace prospects who’ve been repeating and what number of occasions they’ve returned. It provides you perception into which prospects are beneficial and what merchandise they like most so what objects have the best likelihood of promoting in your retailer or on-line store at any given time.

One other instance, if a machine breaks down ceaselessly and requires repairs each month as a result of there being some defect in its design, then utilizing predictive analytics will assist decide which components could be defective earlier than they break down, inflicting downtime prices for what you are promoting in addition to extra labor prices related to fixing them every time this occurs.

Conclusion

There isn’t any doubt that AI is a vital know-how for enterprise. It may be used to enhance effectivity and make processes smarter. However this doesn’t imply each firm wants to make use of it. Every firm has totally different wants, so the easiest way to guage whether or not or not an AI answer matches what you are promoting mannequin is by taking a look at what issues you’re making an attempt to resolve with the know-how, who your opponents are, and if there’s already an answer obtainable for these issues in one other type or format.








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Why Swipecart is the best in Best No-code App Builders 2022?

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Why Swipecart is the best in Best No-code App Builders 2022?

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The past decade has been great in terms of technological advancements. We have moved from 4G to 5G on the internet. We are incorporating Augmented Reality into our mobile applications. Artificial Intelligence has made decision-making more predictive and accurate. And yes, one more substantial improvement has been the arrival of no-code platforms to make building websites and mobile applications easy and fast.

These no-code tools have changed the mobile app development dynamics significantly. It has opened doors for non-technical users and citizen developers to build mobile apps without having to learn to code, making it much easier than the traditional methods of app development.

The growth of these no-code app builders has seen appreciable evolution. At first, they had been constructed to assist in creating MVPs (Minimal Viable Merchandise). However now, it has branched into absolutely scalable cell app constructing giving a head begin for startups whereas being equally helpful for small and large enterprises.

Why does what you are promoting want a cell app?

The query that comes foremost to our thoughts is why is it a dire necessity to have an app when you might have a web site that’s doing nice! That is the place that you must dig a bit additional and perceive the patron wants. As per statistics, buyers view 5 occasions extra merchandise per session inside e-commerce apps in comparison with web sites because of a sooner, much less cluttered expertise.

Additionally, your retailer icon on their machine serves as a continuing reminder of your model and it provides that delicate nudge to open the app and examine for affords or merchandise they may be serious about, leading to impulse buys or repeat purchases.

E-commerce apps have 3 times extra conversions when in comparison with cell web sites and one and half occasions greater than desktops. So, now you already know why a web site or perhaps a mobile-friendly web site is not going to suffice if you wish to have distinctive gross sales and income in your on-line enterprise.

What are the advantages of no-code app builders?

Upon getting set your sights on having an enticing app for what you are promoting, the following step is to search out and construct one. You possibly can choose to construct one from scratch hiring the perfect cell app improvement workforce or outsource the duty.

However then, it comes with its set of challenges. And thoughts it, it isn’t simple. The prices hold including up and even the time for its completion. You possibly can ask your friends or mates about their cell app improvement expertise and for positive it’s going to dissuade you from going for one.

A neater route, and also you’ll not remorse it, is to go for no-code cell app builders. They’re the proper reply for each problem you face whereas constructing a cell app. They show you how to construct your individual app quick and at a much less price.

No matter no-code app builders you utilize, your final aim is to dramatically cut back the time taken to construct an app. With drag and drop builders, you may construct apps just like a coded app full with options and features in a fraction of the time. And since money and time are essential concerns, a no-code app builder makes app improvement fast-completing and cheaper.

Why is Swipecart the perfect no-code app builder for 2022?

Among the many many no-code app builders chances are you’ll discover on-line, Swipecart is a most well-liked possibility when you might have your e-commerce retailer on WooCommerce. We’ll inform you why. Learn on as a result of there’s a shock for you on the finish.

1. It has a simple drag-and-drop modifying device

In case you are serious about how to create a mobile app with Swipecart, allow us to inform you, it’s thrilling and tremendous simple. It has a drag-and-drop editor device which makes the app designing process simple to grasp and execute. Principally, all of the elements take a visible type and now we have to pull and drop these into the app structure display screen.

It has a number of choices for slider banners, single banners, carousels, and plenty of different widgets. All it’s a must to do is choose a function from a dropdown menu and drag and drop your alternative of widget design to the display screen structure part. You’ve got an edit possibility to change options of the widget like fonts, colours, and rather more.

2. You get flexibility in designs

Generic app builders pressure you to make use of certainly one of their pre-designed themes. Swipecart doesn’t. It encourages you to begin from a clean template and regularly full designing the app screens. And sure, you may add as many additional pages as you need. Swipecart has no cookie-cutter templates to restrict your app scalability. It provides you sufficient flexibility to customise your app as per the branding necessities.

3. Construct utterly scalable apps

With Swipecart, you utterly recreate the look and performance of your e-commerce retailer on the app by integrating third-party plugins for cost gateway, checkout choices, buyer opinions, popup messages, push notifications, and rather more. So, no worries, even when your e-commerce app has heavy visitors, the plugins work effectively to provide a seamless expertise to your customers.

4. Straightforward to construct a cell app out of your current WooCommerce retailer

The cell apps created utilizing Swipecart replace mechanically with any modifications you make in your web site. So, there’s nothing so as to add to your workflow. You keep one database and sync the identical product catalog to each your web site and the app immediately with out the necessity to migrate. Additionally, Swipecart works together with your current cost processors, making it simple to handle.

5. Attain clients by way of push notifications

The goal of constructing a cell app for what you are promoting is to spice up gross sales. It’s potential whenever you get new clients, interact them together with your app, and nudge them to purchase merchandise. With cell apps, attaining buyer engagement turns into a tad simple by way of push notifications.

It’s a direct line of communication together with your most loyal clients to alert them about affords, new merchandise, and to maintain them up to date with the order supply course of. With Swipecart, we go one step forward. It has options for person segmentation, personalised messaging, and automatic push notification configuration to assist your advertising and marketing campaigns and within the course of make your buyer journey seamless and fruitful.

6. Characteristic-loaded cell app

Ultimately, allow us to inform you about what options you may count on in your cell app whenever you use Swipecart.

Tab menu– It’s the core of your cell app. It makes all options of your app simply accessible.

Message heart– Right here is the place the obtained messages are saved for the person to revisit it.

Push preferences– You possibly can design popups to let customers resolve what kind of notifications they’ll obtain.

Customizable splash display screen– Your customers will see a branded splash display screen and can get suggestions with a progress bar.

Configuration dashboard– You management every part in real-time on the dashboard from app designing to advertising and marketing and analytics.

Computerized score prompts– Gently nudge your customers to go away favorable scores and opinions of your app with automated score immediate triggers.

There are lots of such wonderful options to make your app really feel like a local app and provides your app customers a seamless procuring expertise.

Wrapping Up

So, right here’s our trustworthy evaluation of Swipecart, the perfect of the no-code app builders for 2022. We’ve tried it and our app is now up and operating with a big enhance in gross sales and income. And sure, we haven’t forgotten. We instructed you we’ll let loose a secret ultimately. Right here it’s. Get free mobile app with Swipecart!

Swipecart has easy pricing plans with a free trial to make your cell app. However, there’s additionally a free plan excellent for brand new companies. You possibly can all the time improve your plan as what you are promoting begins getting traction.

For those who’re searching for an answer to get the content material out out of your WordPress e-commerce web site right into a cell app, we advise going with Swipecart. It’s simple to make use of and extremely priceless if boosting conversions and gross sales are what you are promoting targets!








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이젠 야구도 데이터로 배운다! (The Batting Lab)

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이젠 야구도 데이터로 배운다! (The Batting Lab)

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야구에 데이터를 더한다면 어떤 효과가 일어날까요? 야구 실력도, 데이터 활용 능력도 향상시켜줄 어린이를 위한 데이터 리터러시 프로그램, SAS ‘배팅 랩’을 소개합니다.

▲ 배팅 랩(The Batting Lab)

전 세계적으로 7,000명 이상의 경영진을 대상으로 진행한 한 설문조사에 따르면 85percent가 미래에는 데이터 활용 능력이 오늘날 컴퓨터 사용 능력만큼 더욱 중요해질 것[1]이라고 답했습니다. 반면, 48percent의 어린이는 현재 교육 과정이 데이터 활용 능력을 발전시키기 위해 필요한 자신감과 기술을 충분히 제공하지 않는다[2]고 생각하는 것으로 나타났는데요.

이처럼 데이터 활용 능력은 더이상 선택이 아닌 필수인 만큼 어린이들이 학교 교육 외에도 생활 속에서 데이터 활용 방안을 다양하게 접하면서 데이터를 읽고 분석하며, 목적에 맞게 활용할 수 있는 능력, 즉 ‘데이터 리터러시(Knowledge Literacy)’를 키울 수 있도록 고민해야 합니다.

‘딱딱하고 지루한 책 대신 놀면서 배우는 데이터 활용법이 있다면 얼마나 좋을까…’라고 생각하는 여러분을 위해 어린이가 더욱 쉽고 친근하게 데이터를 활용하고 분석할 수 있도록 SAS가 마련한 ‘배팅 랩(Batting Lab)’ 프로그램을 소개합니다!😉

먼저 ‘배팅 랩’이란 무엇인지 알아볼까요? 👀

▲ 배팅 랩(Batting Lab) 프로그램

바로 이름 속에 힌트가 있는데요! 공을 치는 방망이를 뜻하는 ‘배트(Bat)⚾’라는 용어에서 알 수 있듯이 배팅 랩은 야구와 소프트볼을 통해 데이터 리터러시 향상을 돕는 프로그램입니다.

정리해보면, 배팅 랩은 인공지능(AI)과 영상이나 이미지에서 시각적 세계를 해석하고 이해하도록 컴퓨터를 학습시키는 인공지능 기술인 컴퓨터 비전 및 사물인터넷(IoT) 분석 기술을 활용해 어린이들이 야구 및 소프트볼 타격 실력을 향상시키고, 데이터 리터러시의 기본 요소인 데이터 분석과 활용에 대한 자신감을 가질 수 있도록 돕는 쌍방향 체험 프로그램이라고 할 수 있는데요. 데이터 과학과 스포츠를 접목한 배팅 랩은 어린이들에게 야구를 통해 일상생활과 밀접한 데이터 활용 사례를 보여줌으로써, 데이터 수집 및 분석에 대한 막연한 어려움과 진입 장벽을 낮춰주는 효과를 기대해 볼 수 있습니다. 야구나 소프트볼 같은 스포츠로 데이터를 배울 수 있다니 흥미롭지 않은가요?

배팅 랩 프로그램의 자세한 기능과 활용 방법

 배팅 랩에는 센서와 카메라가 장착된 배팅 케이지가 있어 타자의 자세, 타격, 공 던지기와 같은 세부적인 동작🏃‍♂️을 포착할 수 있습니다. 타자가 배팅 케이지에서 타격하면 SAS의 AI, 컴퓨터 비전 및 IoT 분석 기술은 타자의 자세와 타격 데이터를 실시간으로 분석하고, 케이지 바닥과 벽면에 부착된 디스플레이를 통해 피드백과 개선점을 보여주는 방식이죠.

배팅 랩의 센서와 카메라는 스윙 당 50,000여 개 이상👀의 데이터 포인트를 수집할 수 있으며, 한 세션에서 50회의 스윙을 하면 250만 개가 넘는 데이터 포인트를 분석할 수 있다고 합니다. 엄청난 숫자죠?

▲ 배팅 랩(Batting Lab) 프로그램

이렇게 실시간으로 분석된 데이터를 토대로 타자는 자신의 체중 분포, 손 위치, 신체 움직임 및 기타 요소를 최적화하는 방법을 빠르게 파악할 수 있어 공을 더 멀리, 더 빠르게 치는 방법을 손쉽게 찾을 수 있게 됩니다. 특히, 배팅 랩은 미국 노스캐롤라이나 주립대학의 엘리트 야구 및 소프트볼 선수들의 스윙을 수천 번 이상 분석한 데이터를 활용해 최적의 스윙 모델을 구축했는데요. 이 모델을 표준 삼아 어린이가 보다 나은 배팅 자세를 익힐 수 있도록 도와줍니다.

배팅 케이지에 부착된 디스플레이

그렇다면 최고의 배팅 자세를 찾는 데 SAS의 어떤 기술이 사용 되었을까요? 바로 SAS 이코노메트릭스(SAS Econometrics)입니다! 이 기술은 가령 숲의 벌채 상황을 알아내거나, 암의 진행률을 파악하거나, 제조생산 라인에서 불량품을 찾아내기 위해 활용하는 데이터 분석 기술인데요.

SAS는 이코노메트릭스를 사용해 엘리트 선수들을 관찰한 데이터를 기반으로, 시스템이 숨겨진 상태 정보와 관찰가능한 결과 정보의 두 가지 요소로 이루어졌다고 보는 은닉 마르코프 모형(Hidden Markov Mannequin)으로 만들어 38차원 시계열 모델로 최적의 스윙을 찾아냈습니다. 배팅 랩은 타자의 스윙 중 몸의 움직임을 포함한 모든 요소를 카메라와 센서로 잡아 분석하는데요. 이렇게 분석한 어린이 선수들의 스윙 관련 데이터와 엘리트 선수들을 관찰해 얻은 스윙 모델을 비교해 몇 초 만에 더 나은 스윙을 위한 피드백을 제공한답니다!

▲ 미국 배팅 랩(Batting Lab)에 참여한 어린이들의 모습

배팅 랩 프로그램에 참여한 어린이들은 실제로 데이터 차트와 그래프를 읽는 데 더 높은 자신감을 갖게 되었다고 하는데요. 프로그램 시작 전에는 참여 어린이 10명 중 3명만이 그래프와 차트를 해석하는 데 자신감을 보인 반면, 프로그램이 끝난 후에는 10명 중 9명이 모두 그래프와 차트를 해석하는 데 자신이 있다👍고 말해 무려 3배나 증가한 수치를 보였습니다. 배팅 랩은 그만큼 쉽고 재미있게 데이터와 가까워질 수 있는 프로그램인거죠!

▲ 배팅 랩(Batting Lab) 프로그램

프로그램에 참가한 12살 젠카이 샤르민(Genkai Sharmin)“평범하고 오래된 케이지에 그저 카메라만 많이 달려있을 거라고 예상했는데 우주선 같은 모양이라 정말 놀랐다😀”면서 “코치님의 설명을 말로만 들을 때는 이해하기가 어려웠는데 배팅 랩은 내가 보완해야 할 부분을 정확하게 보여줘서 무엇을 잘못하고 있는지 이해하기 쉬웠다”고 체험에 대한 소감을 밝혔습니다.

▲ 배팅 랩 데이터 플레이북(Knowledge Playbook)

데이터 플레이북(Knowledge Playbook)

한국에서는 배팅 랩 프로그램에 직접 참여하지 못해 아쉬우시다고요? 그렇다면 배팅 랩의 온라인 버전인 가정용 데이터 플레이북(Data Playbook)을 이용해 보세요. 부모와 어린이가 함께 데이터 플레이북의 워크시트를 활용해 배팅 자세를 단계별로 배우고 분석하며 프로그램을 직접 진행해볼 수 있어 집에서도 손쉽게 배팅 랩 체험이 가능하답니다! (국문 버전도 곧 출시됩니다!)

SAS는 데이터가 넘치는 세상에서 사람들이 데이터를 제대로 탐색하고 이용할 수 있도록 데이터 활용 능력을 키울 수 있는 다양한 프로그램을 제공해 왔는데요.

데이터를 소비하고 해석하며 효과적으로 이해할 수 있는 능력인 데이터 리터러시 역량은 오늘날 사회와 직장에서 성공하기 위해 반드시 갖춰야 하는 필수 역량이 된만큼 SAS는 앞으로도 배팅 랩과 같은 교육 프로그램과 교육자들이 이용할 수 있는 무료 도구를 제공해 모든 사람들이 데이터 활용 능력을 키울 수 있도록 적극 지원해나갈 예정입니다.😎

SAS와 함께 데이터 리터러시 역량을 키워보는건 어떨까요?

SAS 교육 프로그램에 대한 보다 자세한 정보는 홈페이지에서 확인해보세요!

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[1] Data Literacy: The Upskilling Evolution, March 2022

[2] The right way to shut the info literacy hole to energy the long run world of labor

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