What does it literally mean, and why should business care? Let’s consider some of the most popular buzzwords and realize the actuality behind the hype.
We’ll begin with the prominent one — what does data science indeed propose? It began with Coca-Cola’s Chair in Engineering Statistics at the Georgia Institute of Technology, Professor Jeff Wu, who first familiarised the catchword
data science during a presentation more than twenty years before. Earlier the term
statistician was extensively used instead, however, Professor Wu took the view that
Statistician no longer included the range of work being done by statisticians and that
Data Scientist better summarised the multi-faceted position.
data science involves something very different. The most useful definition we can apply is that data science is the proactive use of data and excellent analytics to shoot better decision-making. It isn’t some obscure unicorn that will fix all of an organization’s problems but is a functional approach to using the information in an intelligent, more business-centric way.
From a trading point of view, the modern part of this definition — approach better decision-making — is likely the most significant. If disregarded, there is a substantial risk of causing all the expensive, sleek stuff and not really totaling much value. As companies spend more massively in data science, it’s important that data science produces or face a situation in which data science as a phrase becomes associated with expensive initiatives that do not make a meaningful business impact.
Some more offensive advisors will say data science is just a new marketing term for business intelligence (BI). However, whereas BI uses technologies and methods for accomplishing and managing data to present actionable information to business decision-makers, data science is more complicated, leveraging advanced analytics and statistics to analyze or forecast business synopses.
2. Deep Learning in Self-Driving Cars
3. Generalization Technique for ML models
4. Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own)
Artificial intelligence (AI)
If you consider everything you read AI is either proceeding to create a supreme vision of society’s future, or it’s going to leave us all jobless and under the control of microcomputer troops. The fact is quite different. AI holds only algorithms that enable the simulation of human intelligence methods by a processor.
AI has horribly attracted contrary intentions, largely created by sci-fi movie franchises like
The Terminator and
Matrix, which augment the perception that AI presents a warning to humankind. AI is not a robot, nor is it a microcomputer troop. What it is, is basically a set of machine-learning algorithms that are connected to unstructured data that seems to have human-like qualities. Back in the 1950s, Minsky and McCarthy described artificial intelligence as “any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task.” These days AI systems present some or all of the functions linked with specific intelligence, such as preparation, training, thinking, problem-solving, consciousness reproduction, attention, movement, and guidance.
The point that AI appears to have overtaken the imagination and enhance such a hyped phrase, means that the bar has been raised regarding expectations of design accuracy. Whereas people seem to understand that analytics is an iterative process where we seek to explain outputs and reduce error, the expectation for AI seems to be that it will just work perfectly with little training and next-to-no failure. As such, there’s a significant investment in this area often without much thought as to which queries should be asked in order to generate the type of outcome that will add business benefit.
Data science helps AI technologies find resolutions to problems by combining similar data for use in the future. Machine learning, the scientific study of algorithms and statistical models used by mainframe systems to efficiently produce a specific task without using precise instructions, relying on models and thought instead, is the section of AI that functions best with data science.