What do data scientists do?

Data scientists are responsible for a wide range of tasks, but there are 15 core activities they typically perform on a regular basis.

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Let’s take a look

1.     Collecting and cleaning data: Data scientists devote significant time to collecting data from various sources and then cleaning and preprocessing that data so that it can be analysed.

2.     Exploratory data analysis: After cleaning and preprocessing the data, data scientists will conduct exploratory data analysis (EDA) to gain a better understanding of the data, identify patterns and trends, and identify any potential issues.

3.     Feature engineering: Data scientists will also devote time to developing new features or variables from existing data to improve the accuracy of their models.

4.     Modelling: Data scientists will then build predictive models and make predictions or classify data using a variety of machine-learning algorithms such as Logistic Regression, Decision trees, Random Forest, Gradient Boosting, k-Nearest Neighbors, Support Vector Machines, Naive Bayes, Neural Networks (Perceptron, MLP, CNN, RNN, K-Means etc.

5.     Model selection and evaluation: The best model will then be selected based on its performance, and will be evaluated using metrics such as accuracy, precision, recall, and F1 score.

6.     Deployment: Once a model has been chosen and evaluated, data scientists will deploy it into production so that it can be used by others.

7.     Communicating results: Data scientists must communicate their findings and results to a diverse group of stakeholders, which includes business leaders, technical teams, and other data scientists.

8.     Collaboration: To help inform decision-making and drive innovation, data scientists frequently collaborate with other teams such as engineering, product, and business teams.

9.     Technical skills: Data scientists must be technically proficient in a variety of areas, including programming (Python, R), database management (SQL), and machine learning.

10.  Business skills: Data scientists must also understand business requirements because they frequently work closely with business leaders and must understand how their work can be used to drive business outcomes.

11.  Problem-solving: Data scientists must be able to identify and solve problems, frequently relying on data and analytics to inform their decisions.

12.  Data visualization: Data scientists employ data visualisation tools to present their findings in an understandable and compelling manner.

13.  Project management: To deliver results on time and within budget, data scientists must be able to manage projects and collaborate with cross-functional teams.

14.  Continuous learning: Data science is a rapidly evolving field, and data scientists must be able to keep up with the most recent techniques and technologies.

15.  Ethics: Data scientists must be ethically conscious and have a solid understanding of data privacy and security.

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Conclusion

Data scientists oversee a wide range of tasks, from data collection and cleaning to model development and deployment. They collaborate with other teams and understand both technical and business concepts. They are able to communicate their findings in a clear and compelling manner, as well as keep up with the latest techniques and technologies.