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El Data Science has come to transform every area of our lives. From finance, education, health, shopping and even sports. This is how, the Data Science project has become so necessary in business organizations, in order to solve problems, answer questions and provide a broad view of the business.
Therefore, data professionals build models that can predict results and reveal patterns, using methodologies that allow them to carry out a Data Science project.
Below, we describe step by step the project process that a Master Data Science must be able to dominate.
The data science phases, in general, are the following:
On the other hand, in this phase, the benefits to the company, the resources and data available are determined, hypotheses are formulated, and its viability is clarified.
That is, those false values are eliminated and the inconsistencies of the sources are identified. Likewise, information is combined and data is transformed.
Thus, the relationships between variables are explored and an algorithm base is established.
Each data science project is executed by data scientists, who specialize in: Master in Data Science, Master in Data Governance and, Master in Artificial Intelligence, among others.
Talking about the life cycle refers to the methodologies and processes carried out for the design, implementation and feedback of a data science project. It seeks to collect and analyze large amounts of data to create a model using algorithms that predict results and benefit business decision-making.
Thus, the life cycle of the data science project ranges from the beginning, exploration, objectives, action planning, execution and the closure or completion of the set of processes that contain it.
The purpose of the life cycle is to advance a data project that leads to the end point of interaction in a defined way. Applying research, detection and communication of tasks with the work team and the client.
All organizations deserve data science project designs that generate performance improvements; no institution can be on the sidelines of technological evolution or will become obsolete and its capabilities, utility and profits will be inefficient.
For example, in the sports field, a coach or team managers always have to make decisions about tactics and strategies related to their players. This cannot be done just by considering the coach's intuition, as decisions would be biased and ineffective.
A data science project design is the best solution. This is how, there is a need for Sports Science and the demand for qualified professionals in the management of Big Data.
Then, by combining a passion for sports, technical knowledge and the management of Big Data, it will allow professionals to be able to predict results in sports and ensure potentially effective decision-making.
It is developed in sets of processes that are combined with specific tasks and activities to achieve the scope of the Data Project.
It should be noted that the development of the project consists of several stages or life cycles; for example: locating a problem that can be solved with data analysis; collecting, analyzing and preparing the data; creating a model suitable for the data, which can predict a good result; and implementing the model already evaluated to achieve the established objectives.
There are different methodologies for the development of a data science project; look at these three models:
Let's start with the Knowledge Discovery in Databases (KDD) methodology; it consists of 5 data science phases. These are selection, processing, transformation, data mining and interpretation including evaluation.
Next, the SEMMA methodology: Sample (data sampling), Explore (data exploration), Modify (create, identify and select variables), Model (modeling) and Access (evaluation of utility and viability).
Another methodology is Cross-Industry Standard Process for Data Mining (CRISP-DM); it is considered one of the best. For this reason, it is the most used process in the development of a data project.
The data science phases they are: Business Understanding; Data Understanding; Data preparation; Modeling; Evaluation and Deployment. One of the advantages is that it allows the data scientist to return to any phase where the data does not meet the objective of the project.
Many Data Science Masters manage to adjust the development phases of a data project and design their own process and life cycle, adjusted to the data and their viability experiences.
If you want to master the Big Data and project design, we invite you to train in our online Master's Degrees, Madrid with the best data science programs
El Data Science has come to transform every area of our lives. From finance, education, health, shopping and even sports. This is how, the Data Science project has become so necessary in business organizations, in order to solve problems, answer questions and provide a broad view of the business.
Therefore, data professionals build models that can predict results and reveal patterns, using methodologies that allow them to carry out a Data Science project.
Below, we describe step by step the project process that a Master Data Science must be able to dominate.
The data science phases, in general, are the following:
On the other hand, in this phase, the benefits to the company, the resources and data available are determined, hypotheses are formulated, and its viability is clarified.
That is, those false values are eliminated and the inconsistencies of the sources are identified. Likewise, information is combined and data is transformed.
Thus, the relationships between variables are explored and an algorithm base is established.
Each data science project is executed by data scientists, who specialize in: Master in Data Science, Master in Data Governance and, Master in Artificial Intelligence, among others.
Talking about the life cycle refers to the methodologies and processes carried out for the design, implementation and feedback of a data science project. It seeks to collect and analyze large amounts of data to create a model using algorithms that predict results and benefit business decision-making.
Thus, the life cycle of the data science project ranges from the beginning, exploration, objectives, action planning, execution and the closure or completion of the set of processes that contain it.
The purpose of the life cycle is to advance a data project that leads to the end point of interaction in a defined way. Applying research, detection and communication of tasks with the work team and the client.
All organizations deserve data science project designs that generate performance improvements; no institution can be on the sidelines of technological evolution or will become obsolete and its capabilities, utility and profits will be inefficient.
For example, in the sports field, a coach or team managers always have to make decisions about tactics and strategies related to their players. This cannot be done just by considering the coach's intuition, as decisions would be biased and ineffective.
A data science project design is the best solution. This is how, there is a need for Sports Science and the demand for qualified professionals in the management of Big Data.
Then, by combining a passion for sports, technical knowledge and the management of Big Data, it will allow professionals to be able to predict results in sports and ensure potentially effective decision-making.
It is developed in sets of processes that are combined with specific tasks and activities to achieve the scope of the Data Project.
It should be noted that the development of the project consists of several stages or life cycles; for example: locating a problem that can be solved with data analysis; collecting, analyzing and preparing the data; creating a model suitable for the data, which can predict a good result; and implementing the model already evaluated to achieve the established objectives.
There are different methodologies for the development of a data science project; look at these three models:
Let's start with the Knowledge Discovery in Databases (KDD) methodology; it consists of 5 data science phases. These are selection, processing, transformation, data mining and interpretation including evaluation.
Next, the SEMMA methodology: Sample (data sampling), Explore (data exploration), Modify (create, identify and select variables), Model (modeling) and Access (evaluation of utility and viability).
Another methodology is Cross-Industry Standard Process for Data Mining (CRISP-DM); it is considered one of the best. For this reason, it is the most used process in the development of a data project.
The data science phases they are: Business Understanding; Data Understanding; Data preparation; Modeling; Evaluation and Deployment. One of the advantages is that it allows the data scientist to return to any phase where the data does not meet the objective of the project.
Many Data Science Masters manage to adjust the development phases of a data project and design their own process and life cycle, adjusted to the data and their viability experiences.
If you want to master the Big Data and project design, we invite you to train in our online Master's Degrees, Madrid with the best data science programs
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