Introduction to Digital and Data-Driven Technologies – Data Science, ML, AI, Big Data Analytics, Deep Learning, ANN, NLP, IoT, Cybersecurity & Blockchain Technology

The new-age digital and data-driven technologies like Data Science, Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, Natural Language Processing (NLP), Big data Analytics, Cybersecurity, and Blockchain technology are creating a havoc impact all around the globe. While plenty of jobs are being created in these fields, these new technologies are also taking away the traditional and boring human jobs. So, it’s quite important for the new generation to understand the new technologies, terms, and be aware of the required skills to get jobs in the future.

Introduction to Digital and Data-Driven Technologies

Data Science, ML, AI, Big Data Analytics, Deep Learning, ANN, NLP, IoT, Cybersecurity & Blockchain Technology

As the world is on the cusp of the 4th Industrial Revolution, there will be a radical impact on the future of the jobs and employment. As per the World Economic Forum (WEF) report, automation, artificial intelligence, and robots are going to take away 5 million jobs by 2020. Job cuts, skill gap, and unemployment are causing a lot of trouble.

Top 5 Hottest Fields of the Future

  1. Digital and Data-Driven Technologies: Artificial Intelligence, Machine Learning, Automation, Cyber Security, Data Science and Data Analytics
  2. Biotechnology, Biomedical Science & Biomedical Engineering, and Healthcare
  3. Climate, Energy, Natural Resources & Environment
  4. Liberal Arts, Design & Creative Technologies
  5. Education and Skill Training

So, in this post, we will have a sneak peek at the digital and data-driven technologies in order to get a basic understanding of Data Science, ML, AI, Big Data Analytics, Deep Learning, ANN, NLP, IoT & Blockchain Technology.

What is Data Science?

Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms – both structured and unstructured, similar to data mining or data extraction.

Related: So You Think You can be a Data Scientist?

What is Big Data?

Big Data is a broad, popular and evolving term for the extremely large amount of structured, semi-structured and unstructured (non-structured) datasets, which can be analyzed using special software and techniques in order to find patterns, trends, and associations of human behaviors and interactions. In true sense, it is much more than data; it is about insights and impacts and hence it is massive and messy. It has got the potential for solving business problems and creating new business opportunities. Companies that are willing to transform digitally will find themselves in good stead to progress and capitalize on further opportunities to expand in the future. Finding out more about cloud transformation framework might be a good starting point.

Difference between Data Science & Big Data

What is Big Data Analytics?

In order to achieve a certain level of accuracy and speed, deep learning programs require access to immense amounts of training data and processing power. Now, this is very much possible in today’s age of big data (and big data analytics) and the internet of things.

Big Data Analytics is the process of analyzing big data to reveal hidden patterns, unique correlations, popular trends, customer (consumer) behavior and other critical and useful business information. With data analytics, data scientists and other professionals can analyze massive volumes of data, which cannot be done (or too complex and/or too much time-consuming) by conventional analytics and business intelligence tools.Big Data Analytics

There is a huge amount of Big Data floating around everywhere. This massive amount of data need to be collected, preserved and analyzed in order improve business, decision making and giving a competitive edge over the competitors. Big Data Analytics involves the use of analytics to help the business; predicts many aspects of the business and provides the competitive edge in understanding the mindset, trends, and patterns of customers.

Different between Big Data Analytics & Business Intelligence

Business Intelligence (BI) involves all the components of the operation; from the time when data is collected at the time, it gets accessed. BI deals with the transformation of raw data into useful insights for analytical purposes. Analytics is the process performed on the data delivered by BI to generate meaningful insights into the purpose of decision driving and revenue (or impact) generation.

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Data Science vs Data Analytics

Data Scientists work towards estimating the unknown, building statistical models, and conducting casual experiments to figure out the root cause of an observed phenomenon and/or predict the future incidents. In contrast, data analysts (or business analysts) are looking at the known, i.e. historical data, from new perspectives. They will write custom queries to answer complex business questions, incremental new data acquisition and addressing data quality issues, such as data gaps or biases in data acquisition.

In the business or commercial context, a data scientist will identify new products or features that come from unlocking the value of data. A business (or data) analyst will work on conceiving and implementing new metrics on capturing previously poorly understood parts of the business or product.

What is Artificial Intelligence (AI)?

Artificial intelligence is the field of study by which a computer (and its systems) develop the ability for successfully accomplishing complex tasks that usually require human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. AI is usually defined as the “science of making computers do things that require intelligence when done by humans”. In other words, artificial intelligence is concerned with solving tasks that are easy for humans but hard for computers.

Machine Learning vs Artificial IntelligenceWhat is Machine Learning (ML)?

Machine learning is a field of study that applies the principles of computer science and statistics to create statistical models. The models are then used for future predictions (based on past data or Big Data) and identifying (discovering) patterns in data. Machine learning is itself a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

Machine learning is the ability for a computer to output or does something that it wasn’t programmed to do.

Machine Learning vs Artificial Intelligence

“At its core, ML is simply a way of achieving AI.” ML is the type of AI that can include but isn’t limited to neural networks and deep learning.

AI and ML are often seemed to be used interchangeably. But, they are not quite the same. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Whereas, Machine Learning is a current application of AI based on the idea that we should really just be able to give machines access to data and let them learn for themselves.

Machine Learning vs Artificial Intelligence vs Deep Learning

While machine learning emphasizes on making predictions about the future, artificial intelligence typically concentrates on programming computers to make decisions. If you use an intelligent program that involves human-like behavior, it can be artificial intelligence. However, if the parameters are not automatically learned (or derived) from data, it’s not machine learning.

Machine Learning vs Data Science

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category.

For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.

Machine Learning and Data Science

Data science is much more than machine learning though. Data, in data science, may or may not come from a machine or mechanical process (survey data, clinical trials etc.). It might have nothing to do with learning as I have just discussed. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects.

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What is Deep Learning?

Deep learning, also known as the deep neural network, is one of the approaches to machine learning. Other major approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks.

Deep learning is a special type of machine learning. It involves the study of ANN (neural network) and ML related algorithms that contain more than one hidden layer.

Deep learning works around mathematical modeling, which can be thought of as a composition of simple blocks of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.

The word “deep” means that the composition has many of these blocks stacked on top of each other – in a hierarchy of increasing complexity. The output gets generated via something called Back-propagation inside of a larger process called Gradient descent which lets you change the parameters in a way that improves your model.

Traditional machine learning algorithms are linear. Deep learning algorithms are stacked in a hierarchy of increasing complexity.

Software programs use the deep learning approach in a similar manner. Deep learning is a way to automate predictive analytics. The only difference is that the baby might take weeks to learn something new and complex; a computer program could do that in few minutes.

Difference between Neural Network Deep Learning, Machine Learning, AI

What is Neural Networks?

Neural networks or Artificial Neural Networks (ANN) is a group of algorithms that are used for machine learning (or precisely deep learning). Alternatively, think like this –

ANN is a form of deep learning, which is a type of machine learning, and machine learning is a subfield of artificial intelligence.

A neural network, which is a special form of deep learning, is aimed to build predictive models for solving complex tasks by exposing a system to a large amount of data. The system is then allowed to learn on its own how to make the best predictions.

You can also put it in this way – deep learning is an advanced version of the neural network. Below is a flowchart explaining how ANN works.

How Neural Networks Work

Supervised Learning and Unsupervised Learning

Some neural nets use supervised learning, while others use unsupervised learning.

Supervised Learning is a type of machine learning algorithm that is used if one wants to discover known patterns on unknown data.

Say, if a machine learning algorithm is provided with some images of different objects with different types (animals or buildings). In the supervised learning, the algorithm will learn how to say which type of object is there in a particular image that was NOT presented to the algorithm during its training stage. Supervised learning is guided by labeled data fed to the machine.NLP-Deep-Learning-ML

Unsupervised Learning is the type of machine learning algorithm, used if one wants to discover unknown patterns in known data.

Suppose, if a supermarket has got a dataset with the shopping list of customers. The authorized person at the supermarket can use unsupervised learning to understand what kind of products the customers are likely to buy together. So, the supermarket staff could place products near each other that are likely to be bought together.

In few cases, ANN deploys Reinforcement Learning. In this case, ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time.

What is Natural Language Processing (NLP)?

One of the core goals of artificial intelligence is natural language processing (NLP). NLP is a field of computer science that is at the intersection of artificial intelligence and computational linguistics. NLP deals with programming computers to process large natural language corpora. In simple words, NLP involves intelligent analysis of written language.

What is the Internet of Things (IoT)?

The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.) embedded with electronics, software, sensors, actuators, and network connectivity that enables these “smart objects” to collect and exchange data. In other words, Internet of things is a global infrastructure for the information society. IoT allows advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.

IoT is expanding at an exponential rate. Like Big Data, IoT is creating new opportunities and providing a competitive advantage for businesses in current and new markets. The Internet of Things (IoT) is an ecosystem of ever-increasing complexity. It’s the next wave of innovation that is bound to humanize every object in our life, and it is the next level of automation for every object we use.

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IoT keeps adding more and more devices to the digital fold every day to improve process and growth. It touches everything—not just the data, but how, when, where and why you collect it. One of the ways to look at IoT is as multiple blocks – such as connected objects, gateways, network services, and cloud services. As mentioned earlier, security is of paramount importance.


With the increase in IoT-connected devices, the risk of cyber-attacks increases as well. With the IoT, sensors collect, communicate, analyze, and act on information, offering new ways for technology, media and telecommunications businesses to create value. But this also creates new opportunities for all that information to be compromised. Not only is more data being shared through the IoT, among many more participants, but more sensitive data is being shared. As a result, the risks are exponentially greater.

Given the current scenario of widespread attacks, data breaches and the ever-growing need for security, there’s no doubt that we will need a cyber-army in the future. Not just for an organization, but for the whole country as well. In the meantime, businesses should really go the extra mile in order to protect their business from the threat. That’s why going to could prove to be a very wise choice.

There is a multitude of certifications based on the category or field of security such as Web Application Security, Network Security, Ethical Hacking, Cloud Security, Secure Coding, Vulnerability Management, Security Monitoring, Incident Response, Digital Forensics, Web Application Security and many more.

Implementing Blockchain Technology for Cyber Security

Although originally invented for the cryptocurrencies (bitcoins), Blockchain technology can be very useful for tightening cybersecurity. Blockchain could reduce banks infrastructure costs by USD 15-20 billion per annum by 2022. Owing to their distributed nature, blockchains provide no ‘hackable’ entrance or a central point of failure and, thereby, provide more security when compared with various present database-driven transactional structures.

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Cybersecurity in the Banking and Financial Services Sector – IoT Threats, Potential Solutions, and Blockchain

What is Blockchain Technology?

The current IoT ecosystems rely on centralized communication models. All devices are identified, authenticated and connected to cloud servers that sport huge processing and storage capacities. The connection between devices needs to go through the internet. A decentralized approach to IoT networking would solve many of the security issues.

Here arrives the Blockchain technology. The blockchain is a database that maintains a continuously growing set of data records. It is distributed in nature; there is no master computer holding the entire chain. Instead, the participating nodes have a copy of the chain. It’s also ever-growing; data records are only being added to the chain. Blockchain is public. So, everyone participating can see the blocks and the transactions stored in the database.

Blockchain in Layman’s Terms

The blockchain is a virtual (digital) public ledger that records everything in a secure and transparent manner. The blockchain is the digital and decentralized ledger technology that records all transactions without the need for a financial intermediary like a bank.

The traditional financial institutions normally allow transactions in traditional currencies. But, the blockchain allows the free transfer of cryptocurrency through a decentralized environment. All the data is then held in an interlinked network of computers, owned and run by none other than the users themselves.

How does the Blockchain Technology work?

The blockchain technology is a continuously growing list of records, called blocks, which are linked and secured using cryptography. Each block typically contains a cryptographic hash of the previous block, a timestamp and transaction data.

How does the Blockchain Technology work?

Blockchain can also be seen as the Internet of Money. The Internet makes it possible to freely distribute data online. Similarly, blockchain does the same thing for money.

Blockchain technology is considered as the missing link to deal with scalability, security and reliability issues of IoT. Blockchain technology can be used in tracking billions of connected devices, enable the processing of transactions and coordination between devices; allow for significant savings to IoT industry. Blockchain development is being seen in a whole host of industries in order to secure distributed storage of assets and help prevent major cyber attacks through encrypted technology. A company may use a blockchain development company to help plan and implement major projects that can take their business to the next level.

According to the experts, the decentralized approach would eliminate single points of failure, creating a more resilient ecosystem for devices to run on. The cryptographic algorithms used by blockchain technology could make consumer data more private. One of the popular applications of blockchain technology is Bitcoin and Cryptocurrencies. However, there are several other applications of the blockchain technology beyond cryptocurrencies and financial services.

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Sources: 1, 2, 3, 4, 5, 6.

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