Caroline Bartle Consultancy的动态

A fascinating blog from Caroline Bartle exploring the moral impact of the "datafication" of care. For as long as I can recall, the mantra has been ‘if it isn’t recorded it didn’t happen’. Whilst of use, this risks an unconscious, internal mantra of ‘whatever is not recordable need not be done’. Datafication is the process of translating everyday activities, interactions, and decisions into information that can be recorded and analysed. In her research, Caroline is exploring how care teams and staff make decisions and how the annexing of decision making to the algorithms of an electronic care system might impact the care and support outcomes experienced by a person living with dementia. Whilst the algorithms are evolving to consider more and more data points, they can only work with the data the system has been given. Analysis of ‘incidents’ (I prefer the term distressed responses), perhaps including a trawl of times and locations, alongside an analysis of potential delirium markers can be critical in enabling teams to better create effective management strategies. But I strongly agree with Caroline that we must ask ourselves how we use technologies and systems to preserve human dignity. Where is the data entry point , for example, that enables the person’s acts of resilience to be included in a system analysis? So often this is present only in the ‘soft data’, the hand typed entry by a member of the care and support team. ?It is only present if it is entered. It can only be entered, however, if it is seen and acknowledge for what it is; the actions and inactions of a person seeking to adapt in the face of adversity. The world of care data has evolved, and Caroline puts foreword a compelling argument that as such our approach to data must evolve too.

When in doubt, always lean towards quality over quantity of data. When I train a model, I spend time and resources on building quality datasets for model training. The improvement over just amounts of data is guaranteed. Following this one simple rule has always proved itself over the years. #datacentric #datafirst #dataisallyouneed

Learnt about how important it is to have a cleaned and verified data set before stepping into data analyzing. Worth the effort. ?

Truth and quality in training data is a big deal.

The course" Process Data from Dirty to Clean". check it out: https://lnkd.in/dyg7kT3g Insights from Data Cleaning process: 1. Compile Data. Data comes in different forms and formats. 2.Pre-Process Data and Transform data. 3.Clean Data to Make Sense of it #Data #datacleaning #dataprocessing....

?? ???????????? ?????? ?????????? ???????????? ???? ?????????? ????????????????? As a beginner, you might wonder: should you scale your data before or after splitting the dataset? The answer is???????????. ????????’?? ??????: ?? We split the dataset in order to test how good our model is facing new data, so the test set should be formatted just like the expected input. ?? However, if we scale before splitting, the test set will be scaled including data from the training set, and it will interfere with your predictions.

Hi Everyone! Today I got certified on the general aspect of '???????? ????????????????', a topic that continues to gain more traction as the question "???????? ?????? ???????? ???? ?????? ?????"?gets bigger with overflowing amount of data collected everywhere. This is an imposing topic not only for businesses in general, but also for us, personally. Health tracking apps, Sleep tracking apps, Expense tracking apps, Budgeting apps, apps for Financial Investments, there's a lot of data that we come across everyday and before all of it becomes noise in chaos, becoming '???????? ????????????????' helps to visualize and ask questions to our data that will help us solve relatable problems rooted in reality. Thank you DataCamp for providing the platform where I could build an understanding for data. It took hours of study, practice and assessments to build my foundation in fundamentals of ??????????????????, ???????? ?????????????????????????? and ???????? ????????????????????????.

Check out all the different skills you can get assistance with inside Data Mentor. There are endless ways to dive deep into any topic. With just a few clicks, you can create detailed documentation and learning materials. #DataMentor #EnterpriseDNA

In all the skill conversations I’ve had, the idea of data quality has been raised only a couple times by organizations embracing skills. (Honestly, I think it’s never but don’t trust my faulty memory.) Why is that? If the whole point is collecting skills data and using it to make better business decisions, data quality is of the biggest things we should be thinking about.

?? Excited to share that I've just completed the "Model Training: Best Practices for Data Practitioners" course on Pluralsight! ???? As a data enthusiast, staying updated with the latest techniques is key, and this course delivered just that. From optimizing hyperparameters to handling imbalanced datasets, I've gained invaluable insights to enhance my data modeling skills. Highly recommend it to fellow data practitioners looking to level up their game! #DataScience #MachineLearning #ContinuousLearning ???