July 15, 2025 (Updated April 19, 2026)Faithe Day/6 min read

Making the Move from Data Analyst to Data Scientist

Steps to the Analyst-to-Scientist Transition

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The rapid growth of data science, spurred by the rise of big data, machine learning, and artificial intelligence, has resulted in a significant demand for data scientists. This article delves into the transition from Data Analyst to Data Scientist, discussing the similarities and differences between the two fields, the transferrable skills, and ways to develop data science competencies.

With the rise of big data, machine learning, and artificial intelligence, data science has become one of the fastest-growing industries of the 21st century. The popularity of data science means that Data Scientists are now in high demand. However, there are not nearly enough professionals trained in data science to fill all of the data science careers and opportunities that are available at this time. Since many people are easily scared off by the knowledge and training required to pursue a career in data science, it is much easier for individuals who already have a background in data to make that career transition.

Many data professionals are looking for new ways to leverage their skills to break into the industry and build a career as a Data Scientist. Data analytics is one of the most closely related fields to data science, and there are many similarities between the two fields from training to research methods. Due to these similarities, it is easy for individuals with a background in data analysis to make the move from Data Analyst to Data Scientist. There are several ways that data analysts or students and professionals with a background in data analytics can transfer their skills to build a successful career as a Data Scientist.

Data Analysis Vs. Data Science

Before thinking about how to make the move from Data Analyst to Data Scientist, we must first discuss the similarities and differences between data analytics and data science. Data science is considered to be an umbrella term that includes data analytics, and both data analytics and data science can be used to draw conclusions or make predictions based on a collection of data. However, the two fields tend to differ in their methods of analyzing data, especially the type of tools that are used and the training that is required. While data analytics focuses on more general statistical analysis and databases, data science tends to be more focused on creating programs and models for a dataset.

Due to its focus on mathematics and statistics, data analytics is more commonly seen within the realm of research. By working with building up a database and analyzing a dataset, Data Analysts are able to work through the process of taking data from the collection to the visualization stage. Data Analysts tend to work in roles where they share insights about a dataset such as marketing or some type of archival or audience analysis. While Data Scientists also work through the data process, they tend to analyze the patterns that they find in data to apply those insights towards making predictions or forecasting. Making predictions also means that Data Scientists are expected to have a higher level of knowledge of not only how to analyze data, but also how to code and create programs that apply these data-driven insights to the future.

Data Analysts tend to use statistical analysis tools and software which focus on the basics of data processing. For example, within the realm of data analytics, it is common to use programs such as Microsoft Excel and SQL that focus more on the organization and sorting of data. In contrast, Data Scientists tend to incorporate higher-level processing software to make their predictions such as programming languages and machine learning tools like Python. The primary difference between data analytics and data science is most apparent in their names and reflects a difference in research methods and approaches to data. While data analytics is focused on examining data, data science is focused on creating computational systems and programs for a dataset.

Transferrable Data Analysis Skills

Data Analysts already have multiple skills that are transferable, or easily integrated, into a career as a Data Scientist. Similar to Data Scientists, Data Analysts have advanced knowledge in statistics and statistical analysis software. One of the most transferable skills that you will have as a Data Analyst is your knowledge of statistics, mathematics, and analyzing data through those processes. In addition, many Data Analysts have advanced knowledge of not only how to visualize data, but also how to organize it in a way that is easily searchable. Any training that you have in the collection and storage of data is essential to working on a team of Data Scientists.

A Data Analyst can take their background in statistics and mathematical reasoning into work as a Data Scientist by looking for positions that are less focused on engineering and coding and more focused on the analysis or interpretation of data. While many Data Scientists tend to focus on numerical computation and programming, there is not always as much time spent finding creative or innovative ways of visualizing and communicating that data to an audience through the creation of charts and graphs. If you have knowledge of visualization software like Tableau, you can use your understanding of data visualization and storytelling skills to stand out on the job market.

Developing Data Science Skills

In addition to learning how to apply the transferable skills that you already have when it comes to understanding and analyzing data, you can also develop new skills that are especially relevant within the realm of data science. While many data analysts tend to focus on smaller data and working with statistical or functional understandings of data, making the move to a career in data science requires more in-depth knowledge of building predictive models and automation. Developing data science skills requires more advanced knowledge in data science tools such as programming languages and algorithmic design.

One of the primary ways to develop these data science skills is through taking part in a class, bootcamp, or certificate program. Through pursuing additional instruction or certification, you can ensure that you are learning all of the latest programs and data science tools. Many of these courses can give you access to industry professionals, mentorships, and other professional resources that can assist you in turning your data analytics skills into a career as a Data Scientist.

Ready to Make the Move from Data Analyst to Data Scientist?

If you have a background in data analysis, or if you have taken any of Noble Desktop’s data analytics bootcamps, then taking one of Noble Desktop’s data science courses is a must for turning your data analysis skills into a career as a Data Scientist. In addition to in-person data science bootcamps and immersives, you can take a live online data science class through Noble Desktop or one of its affiliate schools from the comfort of your own home or office. You can also find a data science class near you for multiple classes and certificate programs.