Data science involves analyzing large volumes of unstructured data to extract meaningful insights. It is a field that encompasses several disciplines, including data engineering, data preparation, predictive analytics, machine learning and data visualization. It is mainly done by skilled data scientists, although it can also be performed by business intelligence (BI) professionals, data analysts and data-savvy business users, as well as software programming experts.
Learning how to be a data scientist includes mastering many technical concepts and programming languages. As a beginner, it is recommended to focus on understanding the fundamentals of each concept before diving into advanced topics such as machine learning algorithms. For example, if you have never programmed in Python before, it is better to start with an introductory course that introduces the programming language. Similarly, it is important to understand the underlying mathematics and statistics that are involved in machine learning before taking a course on linear regression.
Ultimately, the best way to learn data science as a beginner is to work on projects and practice your skills. Getting experience by working on real-world data sets and applying your knowledge will help you to develop your skills and build your portfolio. It is also recommended to network with other data scientists and participate in online communities to learn from others.
The most successful learners of data science are those who take the time to find a mentor and engage in peer-to-peer learning. They also focus on creating a narrative around their data analysis and storytelling skills to communicate their findings to business leaders and other stakeholders.