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Importance Of Data Science in AI Transformation?
November 15, 2019

Artificial Intelligence (AI), Data Science, Data Analysis and related terms remain as jargons unless one has a clear understanding of how it all works. The importance of data science is growing with each passing day and not without reasons. Let’s explore it in clear terms.

 

What is data science & data science importance today?

Data Science is the study of the heap of exabyte data emerging every day from various sources to extract readable and actionable knowledge and create data products. The methods, techniques and theories used, span a wide range of fields from statistics and probability models to signal processing and high-end computing.

 

Importance of data science importance

Data science is dedicated to finding patterns within the massive data available all around and in all fields. Depending on industry verticals and product use, Data scientists analyze the type of data available and form constructive models.  It is done using statistical methods that analyze and scrutinize to come to comprehensive results. A data scientist takes the responsibility of making predictions based on the data analysis. Data science importance today lies in the fact that these models are then used to analyze data from warehouses or other sources to augment business strategies or better the use of products.

What is Artificial intelligence

Artificial Intelligence is the simulation of human intelligence processes by computers and machines. Algorithms and computing systems reason and classify decisions that are usually done by the human brain. This is where the importance of data science comes in. The mentioned processes include acquisition of information and how they can be used, and using the rules to come to definitive conclusions. Artificial Intelligence can range from the simple narrow AI of same and repeated task to the large and strong ability to emulate human cognitive skills.

Machine learning

Artificial Intelligence can be viewed as a universal set with Machine Learning (ML) as a subset that concerns enterprise and consumer applications. Deep Learning (DL) in turn is a subset of machine learning and a power tool for areas like image recognition, natural language processing, sentiment analysis and other areas containing high-quality data to achieve models of high accuracy.

Deep learning

Deep Learning is the specialized subset of AI embedded within Machine Learning where machines emulate the working of human brains, process data likewise and create patterns used in decision making. It comprises of networks much like neural lines that are capable of learning from unsupervised, unstructured and unlabeled data. This has also given it the name deep neural learning.

An example of deep learning can be that of feeding random images of cats to a system. It learns about the basic features of a cat like its size, shape, whiskers, eyes, ears and tail helping it identify a cat among other animals.

Basic steps in data processing leading to AI transformation

The entire process emulates the structure of a pyramid moving from the base to the apex. This is how it data leads to AI transformation:

Data collection: The rock-solid bottom of the pyramid is formed of the right data sets triggering the processes of AI and ML.  It is crucial that all factors for allowing the flowing in of the data are enabled.

Data analysis & treatment: Understanding the types of data, their value range, any data missing from the scene and the statistical analysis of all that is available helps making them comprehensible.

Data exploration & transformation:  Relationship between all the data available is now mapped, those irrelevant dropped and data that will have a bearing are derived. This step makes the pyramid stronger.

Data training: This is the stage when analytics or business/product intelligence is built. In fact, it is the base on which the ultimate and goal-specific Artificial Intelligence is built. Importance of data science here is all about knowing predictions and organizing the training data with precise labels.

Data experimentation: Experimentation is the final stage where all potential problems are mitigated. Changes are incorporated and their results assessed before they are passed on for final use.

Why is data science the future of AI?

The answer is that AI has developed over the years based on data science. Systems follow human-generated pre-programmed tasks and carry them out independently. From automated responses to customer-service desk assistance, identifying and reaching out potential customers to self-driving cars, virtual doctors to natural language processing and emulating emotions, the use of AI encompasses almost everything that humans is about.

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