Data science, also known as data science, is today one of the drivers of business development, medicine, astronomy, logistics, chemistry, sociology, and the entertainment industry. In order to apply such technologies, many often turn to a data science company.
What does this science do and why does it affect other areas so strongly?
Data vs information: data science – what it is and why
First, let’s look at the difference between these two seemingly similar concepts: information and data. Information, in simpler terms, is any data that is useful. And data is just a resource, of which there is a lot, and it is not clear how to use it.
For example, all records of readings of all thermometers in the world per day are data. But a color temperature map drawn from these values is already information: the map shows the warm and cold regions of the planet, various climatic zones, and many other useful things.
Looking at the weather areas, you can make some kind of inferences and logical constructions. The measurement table can only be blunt, cursing its size and monotony.
Data science is, in fact, just the science of how to turn heterogeneous data into useful information.
The first step to data science: data preparation
Data on its own cannot become information – it must be properly prepared:
* First, they are collected in scientifically correct ways. So, even simple temperature measurements with a thermometer can be spoiled: incorrect calibration of the device, measurement of too high a temperature over a surface heated by the sun, measurements at a random time of day instead of a strictly defined time – all this distorts the indicators and spoils the statistics.
* Then the data is cleared of garbage: for example, from values that appeared as a result of equipment failures or measurement errors.
* After all this, you need to understand how to get information from the raw material. That is, how you can benefit from the data.
If one and the same dataset is transformed in different ways, completely different types of information will be obtained, suitable for solving completely different types of problems. What methods to use to extract the necessary information – this also applies to data science.
How data scientists work
To turn a bunch of bytes into something you need modern data science techniques:
* Data storage means – different tables in Excel, relational and non-relational databases, distributed and decentralized storage. To work with them, you need to be able to correctly save data in a convenient format and then retrieve the necessary records from the storage. Reading procedures can be very tricky: sampling for different time periods, calculating averages / median values, cutting off suspicious numbers (too large or too small) – all this is used when obtaining records and preparing materials for the next steps.
* Statistics and Mathematics. All data in computers are numbers, and numbers live by the laws of mathematics. Processing input signals with classical algorithms, building models, searching for patterns, calculating averages – all these things are used by any data scientist every day. And then there are machine intelligence algorithms and neural networks, without the knowledge of which it is already quite difficult to find a job in this area. Of course, data centers do not write all the models and formulas themselves, they use ready-made solution libraries. To do this, you need to keep in mind the documentation on different software tools for data analysis and implementations of these tools in different programming languages.
* Programming and data processing facilities. After we came up with mathematical models and formulated statistical hypotheses in the previous step, they need to be turned into programs that other people can use. You cannot give other specialists a bunch of formulas – they will not understand them. Therefore, the decision is made in the form of a code, where other people can feed data and get answers to their questions. To do this, you need to be able to program at a good level so that your programs work quickly.
Summing up, it is important to say that a lot of people today use data science in their startups or businesses. And this is not surprising, because this is a very useful thing that can automate any process and significantly increase profits.