Data Science – its tools & technologies
Data Science is a very broad field. It is the extraction of actionable knowledge directly from data through a process of discovery, hypothesis and analytical analysis.
Within the umbrella of Data Science, there is the number of tools of data science which will talk about in this article:
- Artificial Learning – AI
- Machine learning
- Data mining
- Natural language processing
Another is the development of a chatbot (Biometric), which can interact with a human and help them to do basic tasks.
The project combined the latest technologies around neural nets and NLP in order to set up and train the bot to respond to a particular set of questions. Big data analytics makes it possible to identify patterns in consumer spending and identify risky transactions a lot quicker than they can be done currently. This reduces the cost with real-time transactions:
BIG DATA –
No matter how big the data one uses, at the of the day of you have a person or a team looking at spreadsheets or charts or numbers and making a decision after possibly a discussing with 50 other people, and then tweaking something about the way the business operates, then you’re doing pure business analytics.
If you are really doing big data, then those 50 people probably get fired laid off or even more likely are never hired in the first place, and the computer is programmed to update itself via an optimization method or some sort of machine learning technique.
In other words, in a big true data set up, the human had been replicated by the machine and lets the machine do its thing.
ARTIFICIAL INTELLIGENCE (AI) & MACHINE LEARNING (ML) –
AI & ML have become recent buzzwords. All the big digital giants, such as Google, AMAZON, IBM, FB are embracing it in a big way. So are the abilities to understand human speech and translate the written material from almost any language in the world of self-driving cars.
What made AI possible now???
It is related to the ability to process large quantities of data, which has been made possible by enormous increases in computing power. This huge amount of data is used to train something called neural networks, which are hardware and software implementations of what goes on in the human brain. This kind of processing takes place in the outer layers of the human brain called the neo-cortex.
Neo-cortex is present only in human beings, not in any other species on Earth.
Neural networks can be trained using huge amounts of data which is what leads to machine learning. The machine understands what a human being is saying by being trained with millions of sounds.
Google translate from English to Russian, although it doesn’t know either of the languages. By feeding millions of English documents and their corresponding Russian translations, the machine starts learning how to translate.
A neural network consists of several layers. The output of each layer being fed as input into the next layer and so on:
- the first layer may identify an object as a living being
- the second layer may sense it as an animal
- the third layer as a human being
- the final layer may identify the face which forms part of facial recognition in the newer Windows computers. Once recognized as a valid user, the user is logged in without any user id or password.
AI will change our world much like electricity did as it replaced steam as the primary source power for machines.
From a definition perspective, AI is intelligence demonstrated by machines, in contrast to the neural intelligence displayed by humans and other animals, or the ability of a computer or a computer-enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making and solving problem.
The near future of AI looks a bit more like intelligence that is not visible embedded in systems rather than robots that look like and act as humans do.
Artificial Intelligence is used in –
- Countless areas of automation
- Fraud detection
- Supply chain planning
- Demand forecasting
- Financial advisory or virtual assistants
- We see a more complex picture coming into focus, with AI encouraging a gradual evolution in the Job market that – with the right preparation – will be positive.
- AI savvy employees won’t just need to know how to choose thw right algorithm and feed data into an AI model. They’ll also have to know how to interpret the results.
- Providing forward-looking intelligence to strengthen human decisions.
- Fraud detection and reduced congestion
It is a branch of engineering that involves the conception, design, manufacture, and operation of robots.
With regards to helping make day-to-day life better, robots have been designed to entertain, play games, and care for elderly/sick by performing tasks that are hard for a human to do.
These “bots” need not be human-like or have a body; in fact, robots can be computer programs that do a task without an actual physical body of any type. In short, they are a machine that can replicate human actions & beyond.
BLOCKCHAIN is a distributed database system that acts as an ‘open ledger’ to store and manage transactions.
Each record in the database is called a block and contains details such as the transaction timestamp as well as a link to the previous block. This makes it impossible for anyone to alter information about the records retrospectively.
Furthermore, because the same transaction is recorded over multiple distributed database systems, the technology is secure by design.
The value of Bitcoins and other cryptocurrencies is determined almost solely by market demand because the number of coins on the market is predictable and is not tied to any physical goods.
Bitcoins are predominantly traded by individuals rather than large institutions.
BLOCKCHAIN AND BIG DATA:
The business imperative in financial services for blockchain is powerful. Huge data lakes of blocks that contain the full history of every financial transaction, all available for analysis.
Blockchain provides for the integrity of the ledger, but not for the analysis. That’s where big data and accompanying analysis tools will come into play.
Blockchain greatly improves transparency in data analytics. Unlike previous algorithms, the blockchain design rejects any input that it can’t verify and is deemed suspicious.