The age of the table has expired. A Google search, a pass-scan, your online shopping history, a tweet. All contain data that can be collected, analyzed and monetized. With supercomputers and algorithms, we can understand more and more information in real time. In less than 10 years, processors should reach the processing power of the human brain.


With the rise of big data and fast computing, many CEOs, technical directors, and decision makers in the organization are considering how to innovate their business. If you would like to introduce a new product or service, you should turn to data analysis to get information about the market, demand, target audience, etc. Artificial intelligence and machine learning are quickly established in the company. This trend is only expected to increase.

What is Data Science?

A “data product” is a technical asset that: uses data as input and processes that data to return results generated by algorithms. The classic example of a data product is a recommendation engine that collects user data and makes custom recommendations based on that data. Here are some examples of data products:

Amazon recommendation engines suggest items you can buy. This is determined by their algorithms. Netflix recommends movies. Spotify recommends music.
The spam filter in Gmail is a data product. A background algorithm processes incoming e-mails and determines whether a message is undesirable or not.
Computer vision for autonomous cars is also a data product – machine learning algorithms can detect traffic lights, other cars on the road, pedestrians, and so on.
This section differs from the section above, “Detailed Data”, in which it may be advisable to advise a manager to make a smarter decision. In contrast, a data product is a technical feature that encapsulates an algorithm and can be integrated directly into the main applications. Examples of apps that include background data products include the Amazon homepage, Gmail inbox, and standalone driver software.

Data scientists play a central role in the development of data products. These include the development of algorithms as well as testing, refining, and engineering in production systems. In this sense, data scientists act as technical developers by building assets that can be used on a large scale.

What’s an AI?

Artificial intelligence is the general domain of “smart-looking algorithms” whose machine learning is currently the main limit.

Artificial intelligence is simply a computer that can mimic or simulate human thinking or behavior. In it is a subset called machine learning, which now forms the basis for the most exciting part of AI. As computers learn to solve problems themselves, machine learning has led to a series of breakthroughs that seemed almost impossible in the past. Because of this, computers can recognize a friend’s face in a photo or drive a car. This is why people are actively talking about the advent of artificial intelligence.

How does Data Science Intercross Machine Learning

Machine learning is a branch of artificial intelligence in which a class of data-driven algorithms allows software applications to predict results with great precision without resorting to explicit programming.
A prerequisite for this is the development of algorithms that can receive input data and use statistical models to predict outputs while updating them as new data becomes available.

The processes involved have a lot in common with predictive modeling and data mining. This is because both approaches require searching for data to identify patterns and adjust the program accordingly.


Most of us have already experimented with machine learning in one form or another. If you have shopped at Amazon or visited Netflix, these personalized recommendations (product or movie) are a machine learning in action.

However, in the field of data science, computer disciplines such as mathematics and statistics are used and techniques such as data mining, cluster analysis, visualization and – yes – machine learning are integrated.

The main difference between the two lies in the fact that data science as a general term focuses not only on algorithms and statistics but also supports the entire data processing methodology.
Machine learning is a subset of artificial intelligence. Although data science is an interdisciplinary field for extracting knowledge or ideas from data.

How is Data Science applied in AI?

IBM Watson is an artificial intelligence technology that allows physicians to quickly identify important information in a patient’s medical record to provide relevant evidence and examine treatment options. He records patients’ records and then provides his obvious and personalized recommendation, based on information from a collection of more than 300 newspapers, 200 manuals and over 15 pages of text, providing physicians with immediate access to a mine of personalized information contained in the patient’s treatment plan.

Blueberry. This robot can play an improve comedy after being fed by subtitles from hundreds of thousands of movies. Kory Mathewson, an artificial intelligence researcher at the University of Alberta in Edmonton, has developed an algorithm to direct him on stage. He has trained him to create lines of dialogue that can be used in improvisational performance, rewarding him when the dialogue makes sense and punishing him when he spits gibberish.

While Blueberry will no longer play in The Second City, this adorable robot sometimes hits the right note with fun lines. I end with a little clip of Blueberry in action.

Thus, getting trained in Data Science will help you pursue a career in Artificial Intelligence and this can be done by the course Data Science Training in Pune, so start today and pursue a career in the fastest growing technology of the 21st century.  

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