Agility meets data science: promising approaches for DS projects
Posted: Wed Jan 29, 2025 4:12 am
Artificial intelligence (AI), machine learning (ML) and deep learning (DL), data analytics or data analysis? And what is agility? This blog post provides clarity. Agility and data science have one thing in common: both disciplines have gained enormous importance in recent years. The use of agile methods and frameworks can be a decisive success factor for companies. In this and the following blog posts, we will focus on the question of whether and how agile approaches can be integrated into the world of data science. Before that, however, we will give you an overview of the important fundamentals around data science and agility.
Differentiating data science from software development
Data Science should not be equated with pure software development. Software development involves building applications or systems that meet specific requirements. It involves programming code that runs facebook data and is maintained on different platforms. Data science, on the other hand, focuses on analyzing data to gain insights and model patterns. This requires an experimental way of working because assumptions cannot be made in advance about the data and possible outcomes. The deployment of computer programs and machine learning models should also be considered differently, as computer programs are largely static, while machine learning models must continually change and learn new data. Ultimately, data science and data mining are closer to research and development than engineering. This can also be seen in CRISP-DM, as it is based on exploring new approaches rather than designing computer programs. If you are now wondering what exactly CRISP-DM is, you can wait for the next paragraphs.
Delimiting the terms of DS
Before we dive into the details of agile software development in Data Science, it is important to outline the different terms. Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics, and Data Analysis are closely related, but each has its own characteristics and applications. Therefore, we will briefly review each of these disciplines and show you how to understand them within Data Science.
Data Science
Data science is defined as an interdisciplinary applied science. The goal is to gain insights from data to support decision-making processes or optimize business processes. Data science is defined as the scientific study of creating, validating, and transforming data to derive knowledge from it. Furthermore, data science uses scientific principles to generate meaning from data and applies machine learning and algorithms to extract and manage information from large data sets.
According to the definition, a Data Scientist uses scientific methods from the areas of mathematics, statistics, stochastics and computer science. Among their objectives, in addition to generating knowledge from data, are the derivation of recommendations for action, support for decision-making and the optimization and automation of business processes. In addition, the creation of forecasts and predictions of future events is achieved.
Artificial intelligence
The field of artificial intelligence (AI) is very diverse and highly interdisciplinary. There are different definitions of the term AI (or often called AI as well). What the definitions have in common is that AI is about the development of computer programs or machines whose behavior can be described as "intelligent."
There is a strong link between the terms Data Science and AI. Data science is considered an interdisciplinary field of research that uses various processes and methods to generate new insights from data. The processes include data preparation, analysis, visualization, and prediction. The difference is that AI focuses on creating models. These models can be used in data science projects to answer specific questions.
Differentiating data science from software development
Data Science should not be equated with pure software development. Software development involves building applications or systems that meet specific requirements. It involves programming code that runs facebook data and is maintained on different platforms. Data science, on the other hand, focuses on analyzing data to gain insights and model patterns. This requires an experimental way of working because assumptions cannot be made in advance about the data and possible outcomes. The deployment of computer programs and machine learning models should also be considered differently, as computer programs are largely static, while machine learning models must continually change and learn new data. Ultimately, data science and data mining are closer to research and development than engineering. This can also be seen in CRISP-DM, as it is based on exploring new approaches rather than designing computer programs. If you are now wondering what exactly CRISP-DM is, you can wait for the next paragraphs.
Delimiting the terms of DS
Before we dive into the details of agile software development in Data Science, it is important to outline the different terms. Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics, and Data Analysis are closely related, but each has its own characteristics and applications. Therefore, we will briefly review each of these disciplines and show you how to understand them within Data Science.
Data Science
Data science is defined as an interdisciplinary applied science. The goal is to gain insights from data to support decision-making processes or optimize business processes. Data science is defined as the scientific study of creating, validating, and transforming data to derive knowledge from it. Furthermore, data science uses scientific principles to generate meaning from data and applies machine learning and algorithms to extract and manage information from large data sets.
According to the definition, a Data Scientist uses scientific methods from the areas of mathematics, statistics, stochastics and computer science. Among their objectives, in addition to generating knowledge from data, are the derivation of recommendations for action, support for decision-making and the optimization and automation of business processes. In addition, the creation of forecasts and predictions of future events is achieved.
Artificial intelligence
The field of artificial intelligence (AI) is very diverse and highly interdisciplinary. There are different definitions of the term AI (or often called AI as well). What the definitions have in common is that AI is about the development of computer programs or machines whose behavior can be described as "intelligent."
There is a strong link between the terms Data Science and AI. Data science is considered an interdisciplinary field of research that uses various processes and methods to generate new insights from data. The processes include data preparation, analysis, visualization, and prediction. The difference is that AI focuses on creating models. These models can be used in data science projects to answer specific questions.