Note: This is a blog that I wrote back in 2014, but seems even more apropos given all the discussions about the use of technology to achieve digital transformation. Bottom-line: you best invest the time to truly understand your sources of value creation – where you are providing competition differentiation and where you are just providing competitive parity – and drive your technology and people investments accordingly. Remember that Digital Transformation is more about reinventing your…

Technology is changing the way we live and breathe. We’d even go a step ahead, and quote:

Everything we do as humans, including every feat we’ve achieved as a modern civilization, is marked by dynamic…

Data Points

• Chip manufacturer AMD is in talks to acquire…

SaaS solutions literally power the business world. The rise of AWS, API networks, app marketplaces, and a number of other new developments have made it simpler than ever to build SaaS solutions, and nearly every niche and use case is now being served.…

Most companies have big data but are unaware of how to use it. Firms have started realizing how important it is for them to start analyzing data to make better business decisions.

With the help of big data analytics tools, organizations can now use the data to harness new business opportunities. This is return will lead to smarter business leads, happy customers, and higher profits. Big data tools are crucial and can help an organization in multiple ways – better decision making,…

I always seek good resources for my teaching at #universityofoxford and this is a great free book on Cloud native – a topic that is very much in the focus

The book needs a free registration and is provided as a full ebook by d2iq.

D2iq was formerly mesosphere – the commercial arm of mesos – but…

Artificial intelligence, or AI, technology is continuously reshaping small business processes, techniques, and strategies. Businesses of all sizes are always looking to AI to accelerate repetitive tasks, minimize human errors, and increase accuracy. In fact, over 25% of businesses will use AI in their operational processes by the end of 2020. As a result, AI capability is expected to remain here to stay. To ensure that you are not left behind the competition, small business owners…

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.  

Featured Resources and Technical…

Predict Number of Active Cases by Covid-19 Pandemic based on Medical Facilities (Volume of Testing, ICU beds, Ventilators, Isolation Units, etc) using Multi-variate…

While there are several dozen different types of possible variables, all can be categorized into a few basic areas. This simple graphic shows you how they are related, with a few examples of each type. 

More info:…

Here is our selection of articles and technical contributions featured on DSC since Monday:

Announcements

• Penn State Master’s in Data Analytics – 100% Online
• eBook: Data Preparation for Dummies

Technical Contributions…

The future of software development will be model-driven, not code-driven.

Now that my 4^th book (“The Economics of Data, Analytics and Digital Transformation”) is in the hands of my publisher, it’s time to get back to work investigating and sharing new learnings.  In this blog I’ll take on the subject of Software 2.0.  And thanks Jens for the push in this direction!

Imagine trying to distinguish a dog from other animals in a photo…

Hello fellow Data Science-Centralists!

I wrote a post on my LinkedIn about why you should NEVER run a Logistic Regression. (Unless you really have to).

The main thrust is:

• There is no theoretical reason why a least squares estimator can't work on a 0/1.
• There are very very narrow theoretical reasons that you want to run a logistic, and unless you fall into those categories it's not worth the time.
• The run time of a logistic can be up to…

Introduction

Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify. These tuning knobs, the so-called hyperparameters, help us control the behavior of machine learning algorithms when optimizing for performance, finding the right balance between bias and variance. Hyperparameter tuning for performance optimization is an art in itself,…

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology.

There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the…

At dinner with friends last Sunday, the topic of conversation fixated on -- what else -- the upcoming presidential election. That morning, a poll had been released by the …

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…

Interesting picture summarizing several types of techniques used in machine learning, contrasting unsupervised learning with unsupervised learning and reinforcement learning.

The difference between supervised and unsupervised learning is described…

                                           Source…

Knowledge of the basic rules of probability is a must-have for any data scientist. But if you're a visual learner like me, learning the algebraic representations of the 5 basic rules of probability (i.e. P(A) + P(B) = 1) is a challenge. I've never been very good at memorizing formulas, but images stick in my head like ear worms.  Whenever I come across a new formula, I try to make it visual with a picture or doodle. The following picture shows some of the images I created to…