1/5/2023 0 Comments Unclutter consulting![]() This step aims to clean and structure them according to our needs. The data obtained in the previous step is raw and unstructured. These data are supposed to be identified from the first step. It is a question of getting the data on which we will have to work. At the end of this step, you should be able to know more or less which path to take in the whole project. It is a question of reformulating the problem in order to make it as clear as possible. Long live CRISP-DM (standard used for a data science project)! CRISP-DM methodĪ data science project can be seen as the succession of the following steps: We can therefore find in a team people with different profiles, each being in charge of a specific step. Indeed, a data science project is very often complex and composed of several stages. Of course, being a data scientist does not imply being an expert in all these areas (although the more knowledge you have in these areas, the better). It is important to understand concepts such as complexity.Ĭommon sense □: Which is by far what we need the most when faced with a complex problem. Machine learning: Machine learning techniques are increasingly used in data science.Īlgorithms: Mastering this science is essential since all modeling is in the form of algorithms. Since the data is digital, its acquisition, storage and all processing is done using computers. Unclutter consulting code#Indeed, problems are very often translated into mathematical models before being solved.Ĭomputer science: Computer science is the basis of data science in the sense that models are implemented with code and/or computer tools. Mathematics (Statistics, Probability, Linear Algebra, Analysis, etc.): Mathematics is heavily involved in data science. ![]() If we want to build a predictive model for traders based on past stock prices. This could be, for example, the stock market. The field of application: By field of application, we mean the sector (the environment) in which we want to create a data product or solve a problem. ![]() In general, data science involves the following disciplines: Indeed, the end justifying the means, we can do data science in various ways as long as we are in the context presented above. It should also be noted that the fields listed below do not represent an exhaustive list of disciplines involved in data science. That said, it is essential to have a very good knowledge of the field of application before embarking on the development of a model. It is important to understand that the end goal of data science is to solve a problem in a specific domain. This definition may seem vague but it comes from the fact that the discipline is broad and itself calls for several disciplines. Data Mining only consists of the exploitation of data, Data Science is broader since it takes into account the acquisition of data for example. If there is a difference between these two terms, it comes from the fact that Data Mining is a part of Data Science. The difference between Data Mining and Data Science, on the other hand, is a little less obvious to the point that some confuse the two. It therefore happens that we can use Big Data techniques in Data Science when the quantity of our data to be processed becomes very important. Big Data is the discipline of processing and exploiting a large amount of data, while in Data Science there is no constraint on the amount of data. The difference between Data Science and Big Data is immediate. Wikipedia – Data Science What is the difference between Data Science, Big Data and Data Mining? It employs techniques and theories drawn from several other broader areas of mathematics, primarily statistics, information theory and information technology, including signal processing, probabilistic models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and machine learning, visualization, predictive analytics, uncertainty modeling, data storage, data compression and computation high performance. What is this discipline?ĭata science is nothing more than a multidisciplinary field whose goal is to use (digital) data to solve real life problems or to bring a certain value called “Product Data”.ĭata science is the extraction of knowledge from data sets. ![]() ![]() We hear more and more about Data Science, it is the fashionable term in companies, on the web and in schools. What is the difference between Data science and Big Data? ![]()
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