Ergebnisse für Germany Data Scientist vs. Data Analyst | 268 Jobs | Seite 5

  1. SCHUFA Holding AG Logo
    bei SCHUFA Holding AG
    • Wiesbaden

    Bei der SCHUFA arbeiten doch nur Spießer. So oder so ähnlich lautet das Klischee über uns. Das wissen wir und lachen auch selbst gerne darüber. Wer hinter die Kulissen schaut entdeckt ein hochmodernes Unternehmen am Puls der Zeit. Als Deutschlands führender Lösungsanbieter von Auskunftei- und Informationsdienstleistungen arbeiten wir mit 900 Mitarbeitern an zukunftsweisenden Lösungen für Unternehmen und Verbraucher. Wir sind immer auf der Suche nach motivierten Kolleginnen und Kollegen die

  2. Boehringer Ingelheim Logo
    bei Boehringer Ingelheim
    • Biberach
    • competitive salary
    • gym

    Laboratory Technican Germany (Baden-Württemberg) Biberach Job ID 205871 Job Level Experienced Functional area Research amp Development Schedule Full-Time OUR COMPANY At Boehringer Ingelheim we develop breakthrough therapies and innovative healthcare solutions in areas of unmet medical need for both humans and animals. As a family owned company we focus on long-term performance. We believe that if we have talented and ambitious people who are passionate about innovation

What is the difference between Data Scientist and Data Analyst?

Currently, there is confusion about what data scientists and data analysts are supposed to do in daily work and how they differ from each other.

Data scientists are usually given tasks related to departments of the company (e.g., marketing) where they are required to develop methods to maximize the benefits of the company, e.g., by increasing the revenue.  These tasks vary from developing algorithms to develop machine/deep models.
There is a common misunderstanding that a data scientist develops only machine/deep learning models. In fact, most of the data scientists time is spent on cleaning and preparing huge amounts of data (usually stored on Big Storage like Hadoop) in order to be used for data science tasks, e.g., training machine learning models.

Data Analysts are usually given tasks related to departments of the company (e.g., marketing) where they are required to analyze, visualize and communicate huge amounts of data in an easy and interpretable manner to the decision-makers. The data analyst role is an important role and without it, decision-makers cannot make easy their decisions.

What are the role requirements for the Data Scientist and Data Analyst?

Data scientists are usually required to have solid knowledge in:
  • math background and particularly in probability and discrete and continue math.   
  • scripting languages such as Python or R 
  • data analysis tools such as SQL 
  • machine/deep learning algorithms on theoretical and practical levels

Data Analysts are usually required to have:
  • A solid background in statistics   
  • Solid knowledge in the data analysis tools such as SQL 
  • Solid knowledge in dealing with big data e.g., Hadoop data by using Hive and Impala 
  • Knowledgeable in data visualization tools e.g., looker, D3.js to be able to visualize your data and results, e.g., by creating dashboards
  • Some knowledge in scripting languages such as Python or R 

The roles of data scientists and data analysts have similar requirements. However, since data scientists usually develop a machine learning model, they are required to know the math behind those models which are mostly about probability theory and discrete math.  On the other hand, data analysts are required to apply many statistical methods to interpret the data (e.g., obtain P-value for A/B tests).