Data analysts and data scientists are two highly sought-after professions in the field of data-driven roles. They are in the same league as AI and machine learning specialists and digital transformation specialists, according to the World Economic Forum’s Future of Jobs Report 2023 [1]. However, what sets them apart? In this article, we will explore the distinctions between data analysts and data scientists.
Data Analysts and Data Scientists: What do They Do?
The primary difference lies in how data analysts and data scientists handle data.
Data analysts primarily work with structured data to address tangible business challenges. They utilize tools like SQL, R or Python programming languages, data visualization software, and statistical analysis. Some typical tasks for data analysts include:
- Collaborating with organizational leaders to identify data needs
- Collecting data from primary and secondary sources
- Cleaning and organizing data for analysis
- Analyzing data sets to identify trends and patterns for actionable insights
- Presenting findings in a clear and understandable manner to support data-driven decision-making
Read more: What Does a Data Analyst Do? A Career Guide
Data scientists, on the other hand, venture into the unknown by employing advanced data techniques to predict future outcomes. They may automate their machine learning algorithms or design predictive modeling processes capable of handling both structured and unstructured data. This role is often regarded as an advanced version of a data analyst. Some of the day-to-day responsibilities of data scientists include:
- Collecting, cleaning, and processing raw data
- Designing predictive models and machine learning algorithms to analyze extensive data sets
- Developing tools and processes to ensure data accuracy
- Creating data visualization tools, dashboards, and reports
- Writing programs to automate data collection and processing
Read more: What is a Data Scientist? Salary, Skills, and How to Become One
Data Science vs. Analytics: Educational Requirements
Most data analyst positions require at least a bachelor’s degree in fields such as mathematics, statistics, computer science, or finance. Data scientists, as well as advanced data analysts, typically hold master’s or doctoral degrees in data science, information technology, mathematics, or statistics.
While a degree has traditionally been the main route to a data career, alternative options are emerging for those without a degree or prior experience. By obtaining a Professional Certificate in data analytics from Google or IBM, available on Coursera, you can acquire the skills necessary for an entry-level data analyst role in less than six months. Upon completing the Google Certificate, you also gain access to a hiring consortium of over 130 companies.
Starting out as a data analyst can be an excellent stepping stone for aspiring data scientists.
Data Skills for Scientists and Analysts
Both data scientists and data analysts work with data, but they rely on slightly different skill sets and tools. Many data science skills build upon those used by data analysts. Here’s a comparison of their skill sets:
Get Started with Coursera
To embark on a successful career in data science, take the first step by earning a Data Analyst Professional Certificate from IBM or Google. For more information on the journey from data analyst to data scientist, including recommendations for skills, courses, and guided projects, check out our Data Science Career Learning Path.
Häufig gestellte Fragen (FAQ)
- What is the difference between a data analyst and a data scientist?
- What educational background is required for data science roles?
- Can I become a data analyst without a degree?
- How can I transition from a data analyst to a data scientist?
- What are the essential skills for data scientists and data analysts?
[1] World Economic Forum Future of Jobs Report 2023
