Data Analyst vs. Data Scientist – What’s the Difference?

Data Analyst vs. Data Scientist – What’s the Difference?

1. Data Analyst:
      – Role: Focuses on interpreting and analyzing data to help businesses make informed decisions.
      – Skills: Proficiency in SQL, Excel, data visualization tools (Tableau, Power BI), and basic statistical analysis.
      – Responsibilities: Data cleaning, performing EDA, creating reports and dashboards, and communicating insights to stakeholders.
2. Data Scientist:
      – Role: Involves building predictive models, applying machine learning algorithms, and deriving deeper insights from data.
      – Skills: Strong programming skills (Python, R), machine learning, advanced statistics, and knowledge of big data technologies (Hadoop, Spark).
      – Responsibilities: Data modelling, developing machine learning models, performing advanced analytics, and deploying models into production.
3. Key Differences:
      – Focus: Data Analysts are more focused on interpreting existing data, while Data Scientists are involved in creating new data-driven solutions.
      – Tools: Analysts typically use SQL, Excel, and BI tools, while Data Scientists work with programming languages, machine learning frameworks, and big data tools.
      – Outcomes: Analysts provide insights and recommendations, whereas Scientists build models that predict future trends and automate decisions.