Ergebnisse für Germany Data Scientist vs. Data Analyst | 275 Jobs | Seite 8

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    Die Freudenberg Gruppe ein globales Technologieunternehmen mit rund 50.000 Mitarbeitern in rund 60 Ländern entwickelt technisch führende Produkte exzellente Lösungen und Services für über 40 Marktsegmente und Tausende von Anwendungen. Genauso vielfältig und modern ist auch das Angebot an Ausbildungsberufen und DHBW-Studiengängen. Jährlich stellt Freudenberg in Weinheim Auszubildende und Studierende in einer Vielzahl von Berufsbildern und DHBW-Studiengängen ein. Wir suchen engagierte und

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    Entdecken auch Sie Ihre Formel für Zufriedenheit. Für unseren Standort in Gerlingen suchen wir Sie als Mensch und als Werkstudent Produktmanagement Optische Analysenmesstechnik (m/w/d) Endress+Hauser ist ein international führender Anbieter von Messgeräten Dienstleistungen und Lösungen für die industrielle Verfahrenstechnik. Auch mit weltweit über 14.000 Mitarbeitenden sind wir ein Familienunternehmen geblieben und stolz auf unser herzliches Arbeitsklima. So verbindet die Mitarbeit bei uns

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).