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What is Data Science ? Difference between Data Science, Machine Learning, Artificial Intelligence ?

What is Data Science?

Information is a key resource of the modern world, a source of development of existing and emergence of new businesses. 90% of digital data in the world is generated over the past two years. Each year the volume of data increases by 50%. How can we cope with this multi-terabyte wave, capable of covering us with a head? This question can be answered by Data Science.

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Data Science,the science of working with data, is not just a new fashion word in the IT world. This is something that will change the world of programming, business and even consumers, no less than in its time it changed the invention of a steam engine and the personal computer. In fact, Data Science is already changing it, evidence of this – a lot of startups in the field of large data and artificial intelligence.

Let’s Understand What is Data Science:

Data Science is the field of computer science, which studies the problems of analysis, processing, and presentation of data in digital form. Simply put, this is the science of methods for processing large amounts of data and extracting valuable information from them so that decisions can be made more efficiently. All this became possible because of the emergence of cloud services for data storage, the growth of the computing capabilities of computers, the development of machine learning technologies and neural networks.

Generally speaking, Data Science is a set of specific disciplines from different directions, responsible for data analysis and search for optimal solutions based on them. Previously, only mathematical statistics were involved in this, then they started using machine learning and artificial intelligence, which, as methods of analyzing data, added optimization to computer science.

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During the mass dissemination of technology, people generated a huge amount of data which is unable to process and visualize.

At all times before computers got new opportunities through programming – a person created the machine understandable algorithms of work that led to the expected result. This approach is obsolete.

To work effectively with large data, we need another technology called machine learning. In this case, the person only gives some introductory information to computer, but the results of the operation of such an algorithm are not determined by the person. The person determines the way of learning the machine and analyzes the information. It’s like how we learn from you. Machine learning is not just artificial intelligence. This area includes genetic and evolutionary algorithms, and simpler problems associated with cluster analysis.

Finally, Cognitive Science. It is an interdisciplinary science that studies the mechanisms of cognition and thinking. The results of such studies are primarily the basis for the development of various approaches to the creation of artificial intelligence.

Difference between Data Science, Machine Learning and Artificial Intelligence

Data Science, machine learning, and AI are different in the sense that:

Data science generates insight:

Data science is very different from the other two because its goal is also a human goal: to gain insight and understanding.

Machine learning makes prediction:

Machine learning belongs to the field of prediction: “given instance X with a specific feature, speculative Y”. Machine learning saves the programmer from having to explain in detail to the computer how to solve the problem. Instead, the computer is taught to find a solution on its own. In fact, machine learning is a very complex application of statistics to search for patterns in data and to create the necessary forecasts based on them.

Artificial intelligence generates behavior:

Artificial intelligence is a science and technology within the framework of which the problems of hardware or software modeling of those types of human activity that are traditionally considered intellectual are put and are being solved

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How Data Science Enhances Business Intelligence:

Data science is certainly different from traditional BI in three main areas: data diversity and quantity, predictive power, and visualization platform. In advanced business intelligence systems, users have encountered “data discovery tools,” but these tools are often limited by the quality and quantity of data they process.

Data Science breaks the “data” of glass ceilings and allows for the collection, cleaning and preparation of any type of structured, unstructured or semi-structured data for analysis.

While business intelligence teams have always provided decision support to executives or managers, data science has enabled these managers and managers to become self-empowering analytics experts.

In an ideal business environment, business intelligence teams should manage operational analytics, and data scientists (if any) should spend more time refining existing analytics and business intelligence footprints and automating the system as much as possible for everyday business users. They can do their work easily and accurately.

The life cycle Data Science:

The data science is engaged in extracting knowledge and meaningful information from large and complex data sets. Extracting information displays hidden patterns of data, and allows you to better understand the data. This is the significance of the science of data. It connects knowledge with reality. It converts online knowledge into offline. It changes our life, business strategies, approach to choice and helps other sciences, technology, and sociological research.

In data science, we use a large number of different data sets to make conclusions about the world. This involves the following process:

  1. Discovery
  2. Data Preparation
  3. Model Planning
  4. Model Building
  5. Operationalize
  6. Communicate Results

After the final step of the process, there are usually more problems, so we can iteratively execute this process to discover new features of our world. This positive feedback loop is critical to our work, which we call the data science life cycle.

Why is Data Science important?

Data Science and the technology of artificial intelligence allows you to learn more about what a person prefers (by collecting and analyzing data), to get closer to him, creating more personalized interfaces (for example, selecting proposals in accordance with what was previously interesting to the user, sending personalized mailings ) etc.

For the IT industry, the ability to work with data is such a big leap that new startups cannot be imagined without the use of this technology – it’s like continuing to use horses for transportation in the heyday of cars. But the very term IT-startup implies innovation.

Automation, the introduction of new personalisation capabilities allows increasing the margin of the business. And if you do not do it yourself, more technologically advanced competitors will simply squeeze you out of the market.

Why was Data Science particularly focused now?

As technology advances, mankind has constantly expanded its raw material base from animal skins to rare elements, but for all its variety, raw materials have always been natural and material, but at the end of the 20th century, new quality raw materials appeared – artificially created and intangible. Data.

This shift of the paradigm is not yet fully understood, the necessary terminology has not yet been worked out, the first fear has not yet passed, hence panic names like Data Science, Data Science Analytics and like comes. Strange as it may seem, the data mining and text mining that existed before are much more accurate than the new ones, they accurately reflect the “raw” approach to what is happening.

Application of Data Science: 

How Data Science technologies are applied where they originated:

Big IT companies – the place where the science of data was born, so their interior cuisine in this area is the most interesting. The Google campaign, the birthplace of the Mapreduce paradigm, has created a whole unit within itself , the only purpose of which is to train its programmers in machine learning technologies. And this is their competitive advantage: after gaining new knowledge, employees will implement new methods in those Google projects where they constantly work.

Application of Large Data in Medicine:

The implementation of Data Science technologies in the medical field allows doctors to study the disease more thoroughly and choose an effective treatment course for a particular case. By analyzing information, it becomes easier for health workers to predict relapse and take preventive measures. As a result – a more accurate diagnosis and improved treatment methods.

The new methodology allowed to look at patients’ problems on the other hand, which led to the discovery of previously unknown sources of the problem. For example, some races are genetically more predisposed to heart disease than representatives of other ethnic groups. Now that the patient complains of a certain disease, doctors take into account the data on the representatives of his race who complained about the same problem.

The collection and analysis of data allows you to learn about patients much more: from eating habits and lifestyle to the genetic structure of DNA and metabolites of cells, tissues, organs. Thus, the Center for Pediatric Genomic Medicine in Kansas City uses data analysis technology for rapid DNA decoding of patients and analysis of genetic code mutations that cause cancer. An individual approach to each patient will raise the effectiveness of treatment to a qualitatively different level.

Data Science has already become the backbone of retail trade:

Understanding user requests and targeting is one of the largest and most widely publicized areas for using Data science tools. Large Data helps to analyze client habits in order to better understand the needs of consumers in the future. Companies are trying to expand the traditional data set with information from social networks and the history of browser search in order to form the most complete client picture. Sometimes large organizations choose to create their own predictive model as a global goal.

The engine of progress in marketing and sales:

In marketing, Data Science tools allow you to identify which promotion of ideas at one or another stage of the sales cycle is most effective. Using data analysis, it is determined how investments can improve the customer relationship management system, what strategy should be chosen to increase the conversion rate and how to optimize the life cycle of the client. In business related to cloud technologies, Big Data algorithms are used to figure out how to minimize the cost of attracting a customer and increase its life cycle.

Analysis of data on a global scale:

No less curious is how these technologies are used to reduce the human influence on the Earth . It is possible that machine learning will ultimately be the only force capable of maintaining a delicate balance. The topic of human influence on global warming still causes a lot of controversy, therefore only reliable predictive models based on analysis of a large amount of data can give an exact answer. Ultimately, reducing emissions will help us all: we will spend less on energy.

Now Data Science is not an abstract concept, which, perhaps, will find its application in a couple of years. It is quite a working set of technologies that can benefit practically in all spheres of human activity: from medicine and protection of public order to marketing and sales. The stage of active integration of  Data Science into our daily life has just begun, and who knows what the role of Data Science will be in a few years?

New and old professions in Data Science:

Any new field of activity generates new professions. The data specialist, Data Scientist, and the machine learning specialist are the new most enviable future specialists. They are not programmers. These are excellent mathematicians with large cross-disciplinary knowledge and super-ability to analyze, reinforced by persistence – because the chances of finding the ideal formula for learning artificial intelligence are close to zero. They must find among all existing algorithms that which one is better suited for solving project problems and understand when something goes wrong, what exactly is going wrong.

Data Scientist is a specialist in the processing, analysis, and storage of large data sets – in the modern world is considered one of the most promising, relevant and highly paid.

The data scientist understands that what kind of data the computer needs, and its tasks are to provide them. An indispensable assistant Data Scientist is a machine learning specialist who chooses architecture and learning algorithms to work with these data.

Characteristics required by data scientists:

Curiosity – Data scientists tend to look at the world around them by exploring data.
Problem splitting capabilities — Turn large amounts of scattered data into structured data for analysis, find rich data sources, integrate other potentially incomplete data sources, and clean up the resulting data sets.
Fast Learning Capabilities – In a new competitive environment, challenges are constantly changing, new data is constantly flowing in, and data scientists need to help decision makers navigate through a variety of analyses, from ad hoc data analysis to ongoing data interaction analysis.
Problem-transforming capabilities —Data scientists face technical bottlenecks, but they can find novel solutions.
Business Mastery – When they discover something, they communicate their findings and suggest new business directions.
Performance communication skills – they are creative in demonstrating visual information and making the patterns found clear and convincing.

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Fortunately, there are people who are looking at data problems more than the organizers of the Data and according to them Data is not useful until they are turned into information, the data itself can not become an object of consumption. Data is raw for information. Only information can be used in the decision-making process, so it is critically important to understand how information from the data can be generated. That’s what Data Science really is about.

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