Data Scientist vs Data Analytics: what's the difference?
A data analyst and a data scientist both work with data, but their roles differ in scope and complexity. A data analyst focuses on collecting, cleaning, and interpreting data to generate reports, dashboards, and insights that support business decisions. Their work is mainly descriptive, answering “what happened” and “why.” A data scientist, by contrast, builds predictive models and uses advanced techniques like machine learning and statistical modeling to forecast trends, identify patterns, and solve complex problems. Their role is more technical, requiring programming, AI, and big data expertise. In short, data analysts explain the past, while data scientists predict the future.
Definitions and roles of each profession
What is a data analyst?
The data analyst focuses on descriptive data analysis to extract relevant insights and support strategic decision-making within the company. This professional masters specific tools to process and visualize complex information, thereby creating tangible added value. By analyzing numerical results and interpreting trends, they help answer decision-making questions and optimize processes. Thanks to their analytical mindset, data analysts provide a better understanding of data, delivering clear answers to the organization’s business needs.
What is a data scientist?
The data scientist, on the other hand, specializes in predictive modeling and the use of machine learning algorithms to solve complex problems. They apply advanced techniques to make predictions based on both structured and unstructured data. Their role is generally considered more technical than that of a Data Analyst.
Main responsibilities
The responsibilities of data analysts and data scientists are complementary, ensuring a complete and effective use of data.
Data analyst responsibilities
On a daily basis, a Data Analyst mainly:
- Works with managers to identify data analysis needs
- Collects data from various sources
- Cleans and organizes raw data
- Analyzes data to identify trends that can be translated into actionable business insights
- Creates dashboards
- Produces reports to support strategic decision-making
Data scientist responsibilities:
Day-to-day, a Data Scientist mainly:
- Processes raw data
- Builds predictive models
- Designs algorithms to mine large data sets
- Creates tools to monitor data consistency
- Develops programs to automate data processing
- Experiments with unstructured data
Required skills
Both data analysts and data scientists work with data daily, but each role requires distinct and complementary technical skills.
Technical skills of a data analyst:
Data analysts need a mix of technical, programming, and soft skills. They must master data cleaning, statistical analysis, and data visualization to transform raw data into actionable insights. Programming skills in languages like Python, R, or SQL are essential for handling and querying datasets efficiently. Strong business analytics knowledge helps them interpret results in a real-world context, while communication and problem-solving skills ensure insights are clearly shared with stakeholders.
Technical skills of a data scientist:
A data scientist must have deep knowledge of statistics and predictive analysis to interpret complex data and make accurate forecasts. They should be proficient in programming languages like R, Python, and SQL, particularly for object-oriented programming. Familiarity with specialized software and tools such as Hadoop, MySQL, and Spark is essential for managing and processing large datasets. Additionally, expertise in machine learning and the ability to handle massive volumes of data allow data scientists to develop predictive models and extract valuable insights from both structured and unstructured information.
Education and academic pathways
To become a Data Analyst or Data Scientist, several paths are possible. However, if you aim for an excellent career, programs offered by top business schools are the most suitable for your ambitions.
BSc in Data Science for Responsible Business
emlyon business school, in partnership with Ecole Centrale de Lyon, offers a Bachelor of Science in Data Science for Responsible Business, taught in English. This is a 4-year post-high school program, granting a state-recognized bachelor’s degree.
Open to high-achieving high school graduates, this program prepares students to become managers specialized in artificial intelligence and data science, with a focus on social and environmental responsibility. The hybrid teaching approach combines hands-on learning and collaboration with companies and research labs, equipping students to help businesses use big data responsibly.
Students also benefit from the strong ecosystems of both schools, with access to internships, career opportunities, and alumni networks. Upon graduation, you can enter the job market as a Data Analyst or Data Scientist, or continue your academic journey with a Master of Science in Data Science & Artificial Intelligence.
Brochure of the BSc in Data Science for Responsible Business
Master in Data Science & Artificial Intelligence
emlyon’s Master in Data Science & Artificial Intelligence is an advanced program designed to train highly qualified experts in AI and data science, opening doors to roles such as Data Scientist or AI Strategist.
Taught in English, this program lasts one or two years depending on the student’s prior degree level. It takes place in Paris, with two international immersion periods and an immersive seminar in Europe.
Recognized by the French state, this MSc (equivalent to five years of higher education) equips students with advanced knowledge in data science, AI, tools, strategies, and ethics. With international exposure through exchanges, internships, and seminars across five continents, students learn how different cultures use AI and data to shape the future of industry.
Graduates of this highly sought-after program gain access to prestigious companies and high-responsibility positions worldwide.
Brochure of the Master in Data Science & Artificial Intelligence
Salaries and career prospects
As with most specialized professions, salaries depend largely on qualifications, responsibilities, and the company. Here are some general trends.
Salary Comparison:
Overall, Data Scientists earn higher salaries than Data Analysts.
- The average salary of a Data Analyst is around $110,000 per year in the United States and about €60,000 in France.
- The average annual salary of a Data Scientist is around $140,000 in the United States and about €65,000 in France.
Specializing further in high-demand niches, such as AI architecture, can significantly increase salaries in both roles. Training at a globally recognized school also opens opportunities abroad in regions leading in new technologies, such as the United States, the Middle East, or Asia.
In France, while high-paying positions do exist, they tend to be rarer and require advanced qualifications. emlyon’s network of partner companies provides access to these niche opportunities.
Whether in France or abroad, career growth and salary progression come with experience and the ability to seize new opportunities in this fast-evolving sector. Staying up to date with trends, leveraging your school’s network, and engaging in industry events are key to staying competitive.
Career growth
With experience, Data Analysts take on more responsibilities and autonomy, potentially advancing to Lead Data Analyst.
If they wish to broaden their expertise, they can transition into Data Scientist roles, particularly if interested in managing larger data pipelines and developing machine learning models. With several years of experience, they may advance to Lead Data Scientist.
Both Data Analysts and Data Scientists can also pivot toward roles like Business Analyst, focusing on financial data analysis and managing projects to optimize investments and product launches.
Other career paths include Chief Data Officer (CDO) or Business Intelligence Consultant, for those seeking higher responsibility.
How to choose between the two careers?
Choosing between Data Analyst and Data Scientist can be challenging since the roles are related but not identical. Their differences are subtle and often clearer once you’ve gained hands-on experience, for example through an internship.
Profile of a Data Analyst:
If you are interested in analyzing current trends, excel at analytics, and are focused on reporting, you may thrive as a Data Analyst.
Profile of a Data Scientist:
If you are more passionate about modeling, innovation, mathematics, and programming, then Data Scientist might be the right path for you.
And if you’re unsure, don’t worry: the boundary between these careers is flexible. You can transition from one role to the other as opportunities arise and depending on your personal aspirations.
Conclusion
In today’s data-driven world, understanding the key differences between a data analyst role and a data scientist is critical for building a successful career in data. While data analysts focus on descriptive statistics, data visualization, and exploratory data analysis to support informed decisions, data scientists leverage predictive analytics, algorithms, and data modeling to anticipate trends and solve complex problems. Both positions require a strong background in math, coding, and analytics, often supported by a bachelor or advanced degree in data science or a related field. By developing the right skills in data collection, database management, and analytics tools such as Tableau or Python, professionals can grow within their organization, explore advanced career opportunities, and earn competitive salaries. Whether your goal is to excel in team-driven projects, mining large data sets, or guiding business strategy, mastering these roles ensures you stay at the forefront of the evolving field of analytics and data science.