• Ten-fold differences have been reported in Covid-19 death rates.
• Estimate issues are because of how the data is calculated.
• Why the same data can yield gross overestimates and underestimates.
  
  

What is the actual death rate for Covid-19? After nearly a year of the pandemic, no one can…

Is it accurate to mention that you are simply checking out top software development trends in 2021? All things considered, you're within the perfect spot! Here you'll peruse different software development trends of 2021. The greater a part of the IT anticipates come up short? It’s hardly any stunning for a big number of folks who realize the complexities and dynamic nature of the software development industry. So on make due in this capricious industry of consistent…

Over the last decade or so, object storage use cases have evolved considerably as they replace traditional file and block use cases. Specifically the need to work with small data objects is becoming commonplace. Yes, there’s still plenty of large objects but small objects are becoming more prevalent than large for specific workloads and application environments.

Traditional object storage systems were designed for large objects that were infrequently accessed. With today’s…

AI is taking over. In 2021, we’re going to see machine learning taking on a bigger role in data analysis as algorithms become capable of reducing error and producing more accurate models within themselves. These algorithms cover iterative processes, decision trees as well as multi-dimensional splitting of datasets. Machine learning is enabling data analysts to have new and greater insights, affecting everything from marketing departments to the way we learn. Here are the essential machine…

Prescriptive analytics is the most sophisticated type of business analytics and can bring the greatest intelligence and value to businesses. It aims at suggesting (prescribing) the best decision options in order to take advantage of the predicted…

Trustable data is defined as data that comes from reliable sources and used according to its intended and delivered in the appropriate formats and time frames for the specific users.

Trustable data helps in effective decision making. The properties mentioned in the definition makes data trustworthy for effective decision making.

Trust Factors of data:

Trustable data are good only if they meet certain basic requirements. Here are some of the…

I am a data scientist, and decided to open a restaurant last November, 10 days before the governor in my state banned dining-in (who knows for how long) and customers were already rare. Some data scientists in managerial positions dream about exiting the corporate world and envied me, at least before the Covid, when I told them my plan.

Here I explore the options and opportunities available, and this article reflects my optimism. I will also discuss analytics in some detail. The…

As I state in the opening paragraph of my new book “The Economics of Data, Analytics, and Digital…

In another article, we discussed how to simplify your Python code for optimal readability. That piece covered why writing simpler code will help with the reader’s comprehension of your syntax and force you to think about problems in a way that is easier to explain to those who may not be as technically savvy. This article will…

In a previous article, you learned how to format your Python code for optimal readability. That piece went into detail about spacing, number of characters on a line, and more. In this article, you will learn how to keep your Python code explicit to ensure others fully understand your work and eliminate any possible confusion. This good practice will…

Naming Conventions 

During a recent workshop, bootcamp instructor Alex Baransky shared some of the best practices in writing Python code for better readability. Through this series of articles, we will summarize and give you a few examples of those best practices to help you write more elegant Python code, benefiting those who may read and use your…

The development of business intelligence

Like many technologies, business intelligence models are also evolving. From the early days when BI was limited to excel worksheets, data was mainly stored on paper. Today, BI platforms have evolved so much that they define the course of action for organizations with new and novel approaches to improving efficiency in production, delivering sales and service experience to customers. In addition, knowing where our customers are,…

I originally introduced the idea of social teratogenesis to Data Science Central in relation to the employees of a company. I will be taking this opportunity to apply the concept to the clients of a company.

Social teratogenesis - or just teratogenesis here for…

In the blog “How Can Your Organization Manage AI Model Biases?”, I wrote:

“In a world more and more driven by AI models, Data Scientists cannot effectively ascertain on their own the costs associated with the unintended consequences of False Positives and False Negatives. Mitigating unintended consequences requires the collaboration across a diverse set of stakeholders in…

I myself do epidemiologic research, which rarely calls for developing machine learning models. Instead, I spend my time developing logistic regression models that I have to be able to interpret for the broader scientific (and sometimes non-scientific) community. I have to be able to explain in some easy, risk-communication way not only the…

The continued evolution of technology has empowered humanity quite unlike anything else, offering tools and technologies to assist us in our daily endeavors. Take machine learning, for example; one of the most advanced technologies out there in the world right now has virtually changed how we do even the simplest of tasks or, say, run our businesses. Of course, its impact has been observed across all industries, including healthcare. Yes, even healthcare, where machine learning has now…

I have included a lot of Excel spreadsheets in the numerous articles and books that I have written in the last 10 years, based either on real life problems or simulations to test algorithms, and featuring various machine learning techniques. It is time to create a new blog series focusing on these useful techniques that can easily be handled with Excel. Data scientists typically use programming languages and other visual tools for these techniques, mostly because they are unaware that it can…

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• Data analysis is fraught with pitfalls, including too-small sample sizes.
• Bias can creep into the most well-intentioned of studies,
• Tips to avoid bias and choose the best statistical test.

So you've formed your breakthrough hypothesis, created a bulletproof test procedure and waited eagerly for the results to come in. To your surprise, the magnificent effect you were sure of just isn't there. What went wrong? Was it the way your hypothesis was worded? An…