Many posts online are saying that: a Data Scientist's role is obsolete or can be classified as Analyst or Engineer. Some claim that this role was overhyped and that data scientists would ultimately move on to specialise in analysis or engineering. Design by Geoff Sence . Saying this means that one missed the word that explains the role of a data scientist: Science Science is a word that has been largely misunderstood. Britannica defines Science as the knowledge about or stud

Bias in the data is one of the many issues revolving around data science and AI. I believe the community accepted this as one of the items to check for when assessing the data quality. But what is it, and what gives rise to a bias? More importantly, do we always need to eliminate it? In this article, I intend to explain bias and share an unpopular opinion that: To get a good model, sometimes you need to keep that bias! The reason I say this begins with the mathematical def

When I started this blog, I intended to share as many data science elements with the broader public to bridge the gap between data science and businesses or natural persons . Sharing data science knowledge around techniques and algorithms is one way to do so. The other is to speak directly to the heart of the business, costs . **All the estimates below will be linked to their sources to help businesses plan their costs and understand their expenses. When hiring a data scienti

We'll be talking about AI but I like catchy non-data science titles. I encountered the term 'ghosts in the machines' whilst reading The Dark Tower series by the great Stephen King. However, the term was initially coined by the British philosopher Ryle Gilbert. Gilbert used " ghosts in the machines " to "describe and critique the concept of the mind existing alongside and separate to the body." As soon as I read the term, my mind thought of AI and in a way, that's what Stephe

We are familiar with the idiom: Out of the woods. It refers to a situation that no longer poses any threat or danger. With the latest advancements in artificial intelligence , more commonly known as AI , different individuals have taken part in various debates and discussions about the threat that AI poses or not. Image designed by Geoff Sence I will start by saying a simple thing. AI is an IT . Maybe not the ominous clown, but AI does not understand its output. Imagine a co

A couple of years ago, I was assigned a task requiring getting some data and visualising it for a business presentation. Data experts are often asked to present some insight or results, and communication has become a skill businesses require from scientists. I did what I STILL do to this day. I first got the data and numbers I needed to present. Then I chose the best visualisation that would be simplistic and well-understood. **Due to confidentiality, I am changing the unde

Robert J. Oppenheimer is known as the "Father of the Atom Bomb". A title like that carries a lot of weight, but in a well-researched, deeply thought and woven biography, "Inside The Centre: The Life of J. Robert Oppenheimer" by Ray Monk, I learned that such a heavy weight was put on his shoulder due to his scientific interests and passion. Figure 1: The cover of the most remarkable biography about Oppenheimer. Reading about someone famous who impacted different sectors glob

Data scientists aim to build a fantastic model that is reliable, accurate, correct and trusted. Such confidence can only be achieved if the data used by the model is equally reliable, accurate, correct and trusted. In the real world, this is hardly ever (to my knowledge) the case, and most of the data scientists' work revolves around cleaning the data for better quality. A lot of effort has been put into getting better data quality . The issue arises from various aspects. Wh

The second part of the series Accuracy vs Correct uses classifiers to draw out some practices to evaluate whether the model is accurate or correct. In a previous article , I discussed this concept related to linear models, which are easier to visualise and explain. I hope that with patience I can do the same with classifiers. A classifier is a model that looks at the data and attempts to place each data point in its respective category. As an example, suppose there are 100

Have you ever spoken with a data scientist who said: When I see an accuracy of 80% or higher, I know something might be wrong. I bet your first reaction is confusion because the above statement defies any logic, at least theoretically. It is normal to ask: Why? Is not a higher accuracy better? Theoretically, YES! In practice, higher accuracy may not be better. Most data science courses promote high accuracy or show statistics that translate to 100% accuracy or are equally r

“That’s one small step for man, one giant leap for mankind” - Neil Armstrong In 1969, three brave American men, relying on the expertise and genius of thousands of other men and women, landed on the closest planetary-mass object and walked on the Moon . When President J.F.Kennedy announced that the U.S. will land on the Moon, a lot of public opinions were against spending that much money towards space research when in fact, that money could be spent elsewhere on Earth. This r

Some would say that the Scientific Method ends with results . However, most would describe the method as cyclical rather than directional. Nonetheless, the series on The Scientific Method shall conclude with Communication . This may be a presentation, a report of results or an interactive dashboard with different graphs and statistics. Essentially, they all represent one thing: translating the findings into a language that is understood by whoever is your customer. I love sci

Experimenting in data science is what one imagines an experiment to be. Instead of chemicals, there is data that describes different characteristics relating to the problem, such as age, distance travelled and so on. Instead of putting the ingredients into a beaker, data scientists put the data into a model, which is a machine built to process and learn from the data. More importantly, instead of risking exploding the lab, data scientists risk overheating or overloading the c

Research is, in my opinion, another very important step in the project lifecycle. I read articles claiming that Data Science is dead because most of the work can be automated. However research can never be automated and because of it, I believe that data science shall live on. I make this claim with great awareness about how businesses are evolving and how the industry is educating itself on data richness and machine learning. Research is about searching for something. Acco

Science has been around for centuries and shall continue for many more. The idea that science can be stopped and removed from our lives is unfathomable, simply because we live by its method - The Scientific Method . It is a technique which we use to find answers to our questions, choose which car to buy or which road is optimal to get home quickly after a late dinner. It is a behaviour that we practice but do not put a label on because it comes naturally. Academically speaki