Data analyst vs Data scientist vs Data Engineer

Although precisely how these roles are defined can vary from company to company, there are big differences between what you might be doing each day as a data analyst, data scientist, or data engineer.

Data analyst:

Data analyst performs various task in the form of,

• Data cleaning, performing analysis and creating data visualizations.
•  Cleaning and organizing raw data.
•  Using descriptive statistics to get a big-picture view of their data.
•  Analyzing interesting trends found in the data.
•  Creating visualizations and dashboards to help the company interpret and make decisions with the data.
• Presenting the results of a technical analysis to business clients or internal teams.
• Proficiency in tools like Excel, SQL, and data cleaning techniques to work with and clean data.
• Ability to create clear and effective data visualizations using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn).
•  basic statistical concepts and hypothesis testing.

Depending on the industry, the data analyst could go by a different title (e.g. Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst).

. The data analyst must be an effective bridge between different teams by analyzing new data, combining different reports, and translating the outcomes. In turn, this is what allows the organization to maintain an accurate pulse check on its growth.

Salary: $70k /year

Data scientist:

A data scientist still needs to be able to clean, analyze, and visualize data, just like a data analyst. However, a data scientist will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models.

• Leveraging their knowledge of advanced statistics and algorithms.
• If the analyst focuses on understanding data from the past and present perspectives, then the scientist focuses on producing reliable predictions for the future.

• Evaluating statistical models to determine the validity of analyses.
• Using machine learning to build better predictive algorithms.
• Testing and continuously improving the accuracy of machine learning models.
• Building data visualizations to summarize the conclusion of an advanced analysis.
•  Proficiency in machine learning algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch) for predictive modeling, clustering, and recommendation systems.
•  In-depth statistical knowledge for model evaluation and hypothesis testing.
• Familiarity with big data technologies like Hadoop and Spark can be valuable in certain roles.

Skillset: Proficiency in programming (e.g., Python, R), machine learning, statistical analysis, and domain expertise is important for data scientists.

Data scientists bring an entirely new approach and perspective to understanding data. While an analyst may be able to describe trends and translate those results into business terms, the scientist will raise new questions and be able to build models to make predictions based on new data.

Salary: $100k  /year

Data engineer:

Data engineers build and optimize the systems that allow data scientists and analysts to perform their work.

• Building APIs for data consumption.
• Integrating external or new datasets into existing data pipelines.
• Applying feature transformations for machine learning models on new data.
• Continuously monitoring and testing the system to ensure optimized performance.
• Leveraging data tools, maintaining databases, and creating and managing data pipelines.
• Unlike the previous two career paths, data engineering leans a lot more toward a software development skill set.
• Cloud Services: Familiarity with cloud platforms like AWS, Azure, or Google Cloud for data storage and processing.

Salary: $130k /year

Data analyst can transform to data scientist to data engineer

Conclusion:

These roles often overlap, and collaboration between them is essential for effective data-driven decision-making in organizations. Data engineers focus on the architecture and infrastructure required for data collection, storage, and processing. They design and build data pipelines, databases, and data warehouses to ensure data is collected, stored, and made accessible to analysts and scientists. Data engineers work on data integration, ETL (Extract, Transform, Load) processes, and data infrastructure maintenance.