As recently as ten years ago, there were floppy disks with storage capacities of 128MB and pen drives that could store 1 GB of data. These were seen as huge quantities back then. However, today certain enterprises are storing, sourcing and collecting data that is more than 1 zettabyte. To put it in perspective, 1 zettabyte is made up of 1 billion TB.

How can organisations hope to derive any sort of value from such high volumes of data? They require the necessary tools and expertise for analysing this data and identifying valuable insights and patterns in an infinite pool of information. 

For raw data to make sense, businesses need to combine Artificial Intelligence, machine learning and data science. Individually, these three technologies have become the most valuable tools for any organisation regardless of the industry or size of the business. 

However, most business owners and managers are still blurry on what these technologies are. Understanding the nature and demands of AI vs machine learning vs data science is not a one-night assignment. These technologies are complex and help meet varying objectives for your business. To get a clearer picture of what these technologies can do for your data and your business, you need to know how to define each of them.

What is Data Science?

Data science combines a variety of applications, principles, algorithms and tools that help in analysing random datasets. Almost every organisation today generates enormous volumes of data. Monitoring and properly storing this data is not easy. Data science focuses on modelling this data as well as data warehousing. It tracks this exponentially growing data and derives insights through it so that businesses can derive valuable insights. These insights guide their business strategies, processes and organisational goals.

Scope of data science:

Among other domains in an organisation, data science directly impacts business intelligence. That being said, there are several functions specific to these roles. A data scientist primarily works with large datasets and volumes of data to identify trends and patterns. Such applications help in formulating reports that help businesses draw inferences. The role of a BI professional starts where the data scientist’s role ends. They use reports that data scientists prepare for understanding data trends across specific business fields. Based on the inferences drawn, they present business forecasts and a future plan of action. 

Additionally, the Business Analyst domain also uses BI applications along with data analytics and data science. This role combines both these fields and helps businesses with data-driven decision-making. 

Data Scientists analyse data based on specific requirements using several formats. These are:

• Predictive Causal Analytics: This model is used for deriving business forecasts. This model of analytics showcases the results of different business executions using measurable metrics. This proves to be effective for organisations that wish to understand the future of their business moves. 
• Prescriptive Analytics: Prescriptive analysis enables businesses to define objectives through the prescription of those actions which have the highest chances of success. This model uses inferences drawn using the predictive analytics model and empowers businesses through guidance towards the steps that can help in achieving those goals. 

Data science uses a wide range of data-focused technologies such as R, Python, Hadoop and SQL. It also extensively uses data visualisation, statistical analysis and distributed architecture, among other things, to extract insights from raw data. A data scientist is an expert professional. This expertise enables them to switch roles rapidly during several points in a data science project lifecycle. They can work expertly on both AI and machine learning and often switch between the two. 

There are several applications of a data scientist’s machine learning skills, such as:

• Predictive reporting: A data scientist uses ML algorithms for studying transactional data and making valuable predictions. Implementing this model suggests for businesses the most effective plans of action. 
• Pattern discovery: Using machine learning for discovering patterns is vital for businesses to establish metrics in data reports. The right way to do this is by using machine learning. This is essentially unsupervised machine learning with no predetermined parameters. Clustering is the most widely used pattern discovery algorithm. 

Also Read: Popular Data Science Interview Questions & Answers

What is Artificial Intelligence?

Thanks to pop culture, science fiction and robotic future fantasies, Artificial Intelligence has become widely associated with metallic and futuristic machines and robots. In the real world, AI and its applications extend far beyond that. 

In simple words, AI as a technology aims to equip machines for the execution and replication of human intelligence. The core objective of Artificial Intelligence processes is teaching machines through human experiences. Therefore, feeding accurate data and enabling self-correction is critical. An AI expert depends on NLP (natural language processing) and deep learning for machines to identify inferences and patterns. 

Scope of Artificial Intelligence:

• AI makes automation easier: AI allows businesses to automate tasks and operations that are labour-intensive, repetitive and high-volume. It helps set up a reliable system that can run frequent operations. 
• You can create intelligent products: Artificial Intelligence can transform traditional products into smart assets. When combined with a conversational platform, chatbot or other types of smart machines, AI apps can significantly improve technologies, outcomes and productivity.
• Improved progressive learning: An AI algorithm trains machines to carry out desired tasks. These algorithms function as classifiers and predictors. 
• Analysing data: Machines learn commands from data that human operators feed them. This makes it vital to identify and analyse accurate datasets. With neural networking, training machines becomes easier. 

Additional Read: Top Real-Life Artificial Intelligence (AI) Applications Must Aware of

What is Machine Learning?

Machine learning is one of the subsets of Artificial Intelligence. It’s the technology that helps devices and systems automatically learn and evolve through their experiences. Machine learning aims to equip devices and systems with independent techniques of learning so that minimal human intervention is required. This is the key difference between AI vs machine learning.

Machine learning includes studying and observing experiences and data so that patterns emerge. This helps in setting up a system of reasoning based on the results. There are several components of machine learning.

• Supervised machine learning: Supervised ML understands behaviour and creates forecasts using historical data. This type of ML algorithm analyses any training dataset for drawing inferences that can then be applied to output values. The parameters of supervised training are vital to map the input-output combination. 
• Unsupervised machine learning: Unsupervised ML algorithms don’t use labelled or classified parameters. They focus on identifying hidden data structures from unclassified data so that systems can properly infer functions. An unsupervised learning algorithm uses both generative learning and retrieval-based approaches. 
• Semi-supervised machine learning: The semi-supervised ML model integrates elements from the supervised as well as unsupervised models but isn’t a version of either of the two models. Semi-supervised models are cost-effective solutions for instances where data labelling becomes expensive. 
• Reinforcement machine learning: This model of machine learning doesn’t use answer keys to guide any function’s execution. Due to training data shortages, algorithms are forced to learn through experience. Trial and error methods are used and typically yield long-term benefits. 

ML ensures accurate results are delivered by analysing large volumes of data. Applying cognitive AI functions to machine learning technologies can ensure effective information and data processing. 

Must Read: 10 Machine Learning Algorithms You Need to Know

Jobs for Data Science vs AI vs Machine Learning:

All three technologies you have read about are lucrative as professional career paths. While these technologies have their differences, data science, artificial intelligence and machine learning are not mutually exclusive. When you look at the skills required for each of these technologies, you will notice a significant overlap. 

Skills required for roles associated with Data Science:

• Risk analysis
• Programming knowledge
• Data reporting and visualisation 
• Statistical analysis and maths
• Data structuring and warehousing
• Understanding machine learning techniques

Skills required for roles associated with AI and Machine Learning:

• Data evaluation and modelling
• Programming language knowledge (Java, Python, C++ etc)
• Understanding machine learning algorithms
• Statistics and probability
• Distributed computing

If you take up any training course for data science, AI or machine learning, these individual courses generally include knowledge about each of these specialisations.

There are multiple similarities as well as multiple differences between AI, data science and machine learning technologies. Despite their similar skill requirements, their fundamental objectives remain different from each other. In the world of data science, the market is rapidly opening up to reveal new product and service opportunities. This has created new avenues for professionals and leaders in this sector. If you wish to leverage these opportunities, start learning by enrolling in a professional certification program today.

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