Data science vs. machine learning: How are they different?

 With the recent explosive growth of AI, two connected fields are seeing significant demand: data science and machine learning.

The global AI market's value is expected to reach nearly $2 trillion by 2030, and the need for skilled AI professionals is growing in pace. Data scientists and machine learning engineers play essential roles in building and working with AI systems and are behind some of the industry's most exciting developments.

Although the two disciplines are often conflated, data science and machine learning have distinct focuses and require different skills. For organizations developing an AI strategy, understanding these nuances is key to building effective teams. And for job seekers looking to work in the AI field, it's crucial to know what skills are necessary for each of these in-demand roles.

What is data science?

Data science is an interdisciplinary field that incorporates concepts and methods from data analytics, information science, machine learning and statistics.

Overall, data scientists aim to extract actionable insights from data to address a business or research problem. By identifying patterns and trends over time, data scientists help organizations make more informed decisions, improve efficiency and develop data-driven strategies.

Typically, a data science workflow involves the following stages:

Hypothesis generation. Before actually collecting or analyzing any data, data scientists identify a business or research question and develop a hypothesis to test.

Data collection. Based on the problem at hand, data scientists then obtain the necessary data from various internal or external sources.

Data preprocessing. In this often time-consuming step, data scientists clean and prepare data for analysis, addressing issues such as inconsistent formatting and missing values.

Exploratory data analysis. Initial analyses, such as collecting summary statistics and visualizing data with charts and heat maps, give data scientists a general sense of the overall data set and its characteristics.

Modeling and evaluation. Data scientists then evaluate the initial hypothesis using machine learning and statistical analysis, making sure to validate generated models' reliability and accuracy.

Reporting and visualization. Finally, data scientists convey their findings to stakeholders, such as business leaders or other technical teams, through presentations, written reports or data visualizations.