Data scientist: What are his or her functions?
Data is taking on a crucial role in the decision making of companies, institutions and governments. To extract value from this immense volume of information, professionals highly trained in data analysis, processing and visualisation are needed: data scientists. This professional profile has gained great relevance in recent years and is considered one of the most in-demand globally. But what exactly is the job of a data scientist? What are their functions and why are they so important in today's world?
In this article, we will take a closer look at the role of a data scientist, the tasks they perform and their relevance in the digital age. In addition, we will explore a case study that illustrates how their work impacts the real world.
What is a data scientist?
A data scientist is a professional who combines skills in mathematics, statistics, programming and data analysis to extract valuable information from large volumes of data. These specialists are not only in charge of processing raw data, but also of interpreting it and providing insights that facilitate strategic decision making.
This professional profile requires a unique combination of technical and strategic skills. A data scientist not only needs to understand the numbers, but also the context in which this data is generated and how it can be useful to an organisation or sector.
The role of the data scientist is critical in many industries, from retail to banking to healthcare. Thanks to their work, companies can make more informed, data-driven decisions, which translates into improved efficiency, cost reduction and increased competitiveness.
Moreover, with the rise of Big Data and artificial intelligence, the demand for these professionals continues to increase. Data scientists are expected to continue to play a key role in the future of companies, helping them adapt to an increasingly digital and data-driven environment.
At UDIT, University of Design, Innovation and Technology, we have the most advanced Bachelor's Degree in Data Science and Artificial Intelligence in Spanish universities. Our curriculum will train you as a data scientist, one of the professional profiles most in demand by companies. You will be able to extract information from data through different types of analysis and algorithmswhile applying artificial intelligence to interpret, learn and make decisions based on the data collected.
5 roles of a data scientist
The roles of a data scientist are diverse and depend on the type of project or company they are working on. Here are 5 of the most important ones:
1. Collecting and processing data
Data can come from a variety of sources, such as corporate databases, transaction logs, social networks, IoT devices, among others. The data scientist must know the tools necessary to extract, cleanse and structure this data so that it is usable.
It is common for raw data to not be in the right format or to contain errors, inconsistencies or null values. Therefore, a crucial part is data pre-processing, which includes cleaning, de-duplication, handling of missing values and normalisation of variables.
2. Exploratory data analysis (EDA)
A data scientist must also be able, through statistical techniques and visualisations, to identify patterns, relationships and anomalies that may be of interest to the project. This is known as exploratory analysis, which is essential to detect trends or correlations that may influence future decisions.
The most common tools are Python or R together with libraries such as pandas, matplotlib or seaborn, which allow the data scientist to graphically represent the most important features of the dataset.
3. Predictive modelling and machine learning
Predictive modelling is one of the most advanced and characteristic tasks of a data scientist's work. Using machine learning techniques, the data scientist builds models that can predict future behaviour based on historical data.
There are different types of models that are used depending on the type of problem being addressed. For example:
- Regression: To predict continuous values such as future sales or the price of a house.
- Classification: To classify objects into different categories, such as spam or non-spam emails.
- Clustering: To segment customers into different groups according to their purchasing characteristics.
Machine learning techniques, such as decision trees, neural networks or classification algorithms, allow automating data-driven decision making. A data scientist must be able to choose the right model, train it with the available data and optimise it to improve its accuracy.
4. Data visualisation and presentation
A key part of a data scientist's job is to communicate the results of their analysis clearly and effectively to non-technical stakeholders, such as executives or customers. To do this, he or she must master data visualisation tools such as Tableau, Power BI or Python visualisation libraries such as plotly.
The goal is to transform findings into visual stories that can be easily understood and used to make decisions.
5. Model optimisation and continuous improvement
Once implemented, predictive models must be continuously evaluated and improved. A data scientist's job does not end with the creation of a model; he or she is also responsible for optimising and tuning it, ensuring that it remains relevant and accurate as new data is collected.
In addition, he or she must perform cross-validation tests, check for errors and adjust parameters to avoid problems such as overfitting (when the model is too tightly tuned to the training data and does not generalise well to new data).
Data Science in our everyday life: supermarkets and stock
Let's imagine a supermarket chain wants to predict the demand for certain products to optimise its inventory and avoid both overstocking and understocking. What can a data scientist contribute to this day-to-day problem ?
Data collection: Data scientists start by collecting data from the chain's historical sales transactions, along with additional information such as promotions, seasonality, and external factors (weather, special events, etc.).
Exploratory analysis: They perform exploratory analysis to identify patterns in sales, such as which products sell more during certain times of the year or how promotions influence sales volume.
Predictive modelling: With this information, data scientists create a machine learning model using time series and regression algorithms to predict future demand. The model is trained on historical data and adjusted to predict how many products will be needed in each shop based on factors such as weather or upcoming events.
Visualisation and presentation: Once the model is ready, data scientists prepare interactive dashboards that allow managers at each shop to view the demand predictions. Through graphs and reports, they can easily understand when reordering or inventory reduction will be necessary.
Optimisation: After implementing the model, data scientists monitor its performance in real time and adjust parameters as needed to improve its accuracy over time.
Through such a simple example, we have shown what the working method of a data scientist would be. Do you want to become one of the professionals of the future? In UDIT's Bachelor's Degree in Data Science and AI, you will learn through examples and case studies like this one to solve real problems that help companies in their growth and digital transformation.
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