Data Science solutions development team: the creation of analytics, forecasting, data collection and processing systems. Data science as a service, Big Data, and Data Mining.
Retail networks collect your data, track the history of your purchases and store visits.
Cars know your preferences for the temperature in the cabin, the speed of movement on different parts of the road and at different times of the day.
Fitness apps get data from fitness trackers. Based on user’s activity and movement, they can count calorie consumption and make personal recommendations for training and nutrition.
Smart car can determine whether the driver had a good or unpleasant call after comparing the heart rate, body temperature, and calls from the phone. Moreover, assessing the weather conditions, traffic situation and speed - the car warns the driver about the endangered situation It suggests slowing down and concentrating on the road.
By nature, data does not carry value for a person who does not know how to use collected information. Just a knowledge about the number of customers, that visited your store during a day is not enough - you need to conduct a deep analysis of your buyers: who they are, what they buy, what influences their choices of goods and stores. And only after the right conclusions has been made up, you can use this information to improve your activity.
Data science solutions are not only responsible for analyzing large data amounts (big data analytics), but also determine the right approach in order to process, sort, sample, and search for new data.
The main value of data science and analytics is that it is often difficult for the human brain to discern patterns meanwhile they are easily found by a machine.
calculate bank card fraud
simulate risks for investment or lending
personalize marketing and increase its effectiveness
make financial forecasts
create recommendation systems for the most relevant offers to customers
Data Mining - is a process, the main aim of which is a search for new interpretations of previously known knowledge required for making decisions. When people process information, they do it from their own point of view. However, Data Mining does not have any frame and stereotype.
Big data is responsible for storing and managing data in hundreds of terabytes or petabytes. Where relational databases fail, a set of “big data” approaches and methods are applied. They can effectively organize workflow with rigorously structured information, such as texts, images, video, and more.
If we talk about computer data processing it’s important to take into consideration, that typical algorithms cannot be applied to extremely huge data sets. With big data, we apply a different approach - Machine learning.
In this case, a person inserts the computer input data sets, determines the method of learning the machine (determines the algorithms), but the machine learns "itself" and eventually gives a solution. In order for a machine to "solve" and "learn" something, we need accurately prepared data sets and perfectly chosen (in simple cases) or created mathematical models.
So, big data is responsible for organizing, structuring and storing large amounts of data, and data mining processes big data arrays to get causal relationships, non-trivial interpretations, classification, conclusions, and even predictions.
— Based on saved information about the last orders, the data science solution can predict wished products for a visitor with similar preferences, can be shown next time he visits the site.
The advantage of such systems is obvious. They can store a huge amount of information about all user visits, and draw such parallels which a human can't even think of.
As a result - the visitor makes a decision faster and finally buys something in the exact Internet Store that knows and understands his needs.
— Insurance companies, banks, travel operators, and many other companies use data science.
Insurance companies and banks can use in-depth data analysis to assess risks for granting a loan or life insurance. Travel operators, and other entertainment companies can use it for advertising and writing personal offers.
As a result strong> - risks are significantly reduced and resources are saved.
— All cards, questionnaires, surveys are large data sets that are regularly collected by many chain stores.
But it is not enough to collect information where data science solutions can efficiently process it and make accurate predictions about user needs.
As a result, strong> -of personalized marketing approach the buyer is incited to extra visit to the offline store through the instant messenger.
— Data science is used to estimate and forecast demand.
To decide which films/programs will be interesting for the target audience of a particular channel at a certain time, or what kind of music on the radio. In the cinema, the demand on a future film is predicted in order to decide whether filming will pay off.
As a result strong> - getting maximum ratings at lower cost.
Interested in data science development? Have an idea, but do not know how to implement it?
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