Decoding the Data Trinity: Data Analyst, Data Scientist, and Data Engineer
As a former data analyst, and now a proud owner of a data & analytics recruitment agency and head of operations for a data engineering bootcamp, I've seen firsthand the confusion surrounding the distinct roles within the data domain - I'm fairly certain there are about 15 definitions of a Data Analyst now! In this LinkedIn article, I'll be shedding light on the unique functions and skills of Data Analysts, Data Scientists, and Data Engineers with what I've discovered so far. Additionally, I'll delve into the technology stacks typically associated with each role.
🔬 Data Scientist:
Data Scientists are analytical problem solvers who develop, implement, and test hypotheses to create data-driven solutions for business challenges. They require robust technical and mathematical skills, such as machine learning, statistical modeling, and big data, as well as advanced programming expertise. Furthermore, effective communication and presentation skills are crucial for data scientists to narrate their findings from a business perspective. The end product for a Data Scientist within a business will often be a prediction model that can be applied to customers or safety outcomes.
Example technology stack:
Python or R or SAS for programming and data manipulation
TensorFlow or PyTorch for machine learning
SQL for database querying
Tableau or Power BI for data visualisation
📈 Data Analyst:
Data Analysts, a subset of data science, gather, review, and analyse data to identify insights that drive critical business decisions. Forbes emphasises the importance of both soft skills (business acumen, communication, stakeholder management, presentation) and hard skills (technical know-how, critical thinking, data visualisation) for data analysts.
Example technology stack:
SQL for database querying and data manipulation
Excel for spreadsheet analysis and visualisation
Python or R for statistical analysis
Tableau or Power BI for data visualisation and reporting
Google Analytics for web analytics
🛠️ Data Engineer:
Data Engineers focus on the infrastructure and architecture used in data generation. As the backbone of data science, they design and develop information systems. Key technical skills for data engineers include statistical analysis and modeling, data warehousing solutions, predictive modeling, and data mining. Data Engineers will often serve data to the Data Analysts and Data Scientists to analyse.
Example technology stack:
Python or Java for programming
SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB)
Apache Kafka for data streaming
Amazon Web Services (AWS) or Google Cloud Platform (GCP) or Microsoft Azure for cloud computing
As you pursue a career in these fields, it's essential to acquire the right skills and qualifications. Numerous online courses, certifications, and degree programs are available to help you sharpen your expertise and advance your career, providing you with a competitive edge in the data industry. There are typical pathways which included university degrees in Computer Science o
