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Data science has gained popularity over the past decade, transforming industries and stimulating innovation. However, with its increase in importance, several false ideas have also emerged. One of the biggest false ideas on data science is that it is a question of creating complex automatic learning models.

Many people believe that the main role of a data scientist is to develop sophisticated automatic learning models. Although the development of models is an essential element of data science, it is only one piece of the puzzle. In reality, an important part of the time of a data scientist is devoted to the collection, cleaning and preparation of data – tasks which are often undervalued but essential to derive significant information.

A successful data science project requires a holistic approach, which includes:

1. Understand the commercial problem: Data science begins with a clear definition of the problem to ensure that information is aligned with commercial objectives.
2. Data collection and cleaning: Raw data is often messy, incomplete or inconsistent. Cleaning and structuring data correctly can take up to 80% of the time of a data scientist.
3. Analysis of exploratory data (EDA): Before applying models, it is crucial to explore and view the data to identify models, trends and anomalies.
4. Characteristics engineering: The selection and transformation of good features can have a significant impact on model performance.
5. Selection and training of the model: The choice of the right model is important, but it often follows the fundamental steps mentioned above.
6. Interpretation and communication: The ideas obtained must be communicated effectively to stakeholders in a way that informs decision -making.
7. Deployment and maintenance: Once a model is built, it must be deployed and monitored to ensure that it continues to provide value over time.

Focusing only on automatic learning models can lead to unrealistic expectations as to what data science can achieve. This can also lead to underinvestment in crucial steps such as data quality management and expertise in the field. Organizations that recognize data science as a complete process – and not just a model creation exercise – are more likely to make a real value from their data initiatives.

Data science is a multidisciplinary area that extends beyond the construction of models. Success in this area requires a balance of technical skills, business understanding and data intuition. By dissipating the false idea that data science concerns automatic learning models, organizational organizations and aspirants from data can better appreciate the complete scope of what this field implies and generate more significant results.

Data science has gained popularity over the past decade, transforming industries and stimulating innovation. However, with its increase in importance, several false ideas have also emerged. One of the biggest false ideas on data science is that it is a question of creating complex automatic learning models.

Many people believe that the main role of a data scientist is to develop sophisticated automatic learning models. Although the development of models is an essential element of data science, it is only one piece of the puzzle. In reality, an important part of the time of a data scientist is devoted to the collection, cleaning and preparation of data – tasks which are often undervalued but essential to derive significant information.

A successful data science project requires a holistic approach, which includes:

1. Understand the commercial problem: Data science begins with a clear definition of the problem to ensure that information is aligned with commercial objectives.
2. Data collection and cleaning: Raw data is often messy, incomplete or inconsistent. Cleaning and structuring data correctly can take up to 80% of the time of a data scientist.
3. Analysis of exploratory data (EDA): Before applying models, it is crucial to explore and view the data to identify models, trends and anomalies.
4. Characteristics engineering: The selection and transformation of good features can have a significant impact on model performance.
5. Selection and training of the model: The choice of the right model is important, but it often follows the fundamental steps mentioned above.
6. Interpretation and communication: The ideas obtained must be communicated effectively to stakeholders in a way that informs decision -making.
7. Deployment and maintenance: Once a model is built, it must be deployed and monitored to ensure that it continues to provide value over time.

Focusing only on automatic learning models can lead to unrealistic expectations as to what data science can achieve. This can also lead to underinvestment in crucial steps such as data quality management and expertise in the field. Organizations that recognize data science as a complete process – and not just a model creation exercise – are more likely to make a real value from their data initiatives.

Data science is a multidisciplinary area that extends beyond the construction of models. Success in this area requires a balance of technical skills, business understanding and data intuition. By dissipating the false idea that data science concerns automatic learning models, organizational organizations and aspirants from data can better appreciate the complete scope of what this field implies and generate more significant results.

Data science has gained popularity over the past decade, transforming industries and stimulating innovation. However, with its increase in importance, several false ideas have also emerged. One of the biggest false ideas on data science is that it is a question of creating complex automatic learning models.

Many people believe that the main role of a data scientist is to develop sophisticated automatic learning models. Although the development of models is an essential element of data science, it is only one piece of the puzzle. In reality, an important part of the time of a data scientist is devoted to the collection, cleaning and preparation of data – tasks which are often undervalued but essential to derive significant information.

A successful data science project requires a holistic approach, which includes:

1. Understand the commercial problem: Data science begins with a clear definition of the problem to ensure that information is aligned with commercial objectives.
2. Data collection and cleaning: Raw data is often messy, incomplete or inconsistent. Cleaning and structuring data correctly can take up to 80% of the time of a data scientist.
3. Analysis of exploratory data (EDA): Before applying models, it is crucial to explore and view the data to identify models, trends and anomalies.
4. Characteristics engineering: The selection and transformation of good features can have a significant impact on model performance.
5. Selection and training of the model: The choice of the right model is important, but it often follows the fundamental steps mentioned above.
6. Interpretation and communication: The ideas obtained must be communicated effectively to stakeholders in a way that informs decision -making.
7. Deployment and maintenance: Once a model is built, it must be deployed and monitored to ensure that it continues to provide value over time.

Focusing only on automatic learning models can lead to unrealistic expectations as to what data science can achieve. This can also lead to underinvestment in crucial steps such as data quality management and expertise in the field. Organizations that recognize data science as a complete process – and not just a model creation exercise – are more likely to make a real value from their data initiatives.

Data science is a multidisciplinary area that extends beyond the construction of models. Success in this area requires a balance of technical skills, business understanding and data intuition. By dissipating the false idea that data science concerns automatic learning models, organizational organizations and aspirants from data can better appreciate the complete scope of what this field implies and generate more significant results.