Data Science

Data Science

1. What is Data Science?

   • It involves the combination of scientific, mathematical, statistical, and computational methods to gather data for the purpose of extracting knowledge and value.
     • "Data science is often considered the intersection between statistics and computer science because its history is rooted in these two fields, and they share many of the same principles." (Harvard SEAS, 2025)
       • Tools
       • Technologies
       • Methods
       • Processes

2. What is Data Analytics?

   • It is the process where data is examined and interpreted to draw conclusions, identify patterns and make decisions.
     • "There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process." (UCD Professional Academy, s.f.)
       • Techniques
       • Segmentation
       • Visualization

3. What is Big Data?

   • As its name suggests, this is a massive and often unstructured data set, for which the processing capabilities of traditional data management tools are inadequate.
     • "Is made up of various types (...) we can categorize these into three types: structured, semi-structured, and unstructured Big Data solutions, each encompassing a different facet of the Big Data world." (American Public University, 2023)
       • Volume
       • Velocity
       • Variety
       • Veracity
       • Value

4. The Big Short

   • The film analyzes complex information from the real estate and financial markets. At the same time, we see an example of how good data analysis can anticipate a crisis and produce advantages.
     • Advantages
     • Stability
     • Analysis

5. The Music Stops

   • Shows us how data science connects with the importance of data accuracy and correctly interpreting indicators to avoid bad decisions.
     • Real
     • Data accuracy
     • Decisions

6. CRISP DM Methodology

   • Business understanding
     • Evaluation of the situation
     • Definition of objectives for analytic data mining
     • Development of a working plan.
     • Identification of business understanding
   • Deployment
     • Planning and managing of monitoring
     • Report of final results
     • Review of final results
   • Understanding the data
     • Collecting data
     • Description of the data
     • Data exploration;
     • Data quality
     • Verification.
   • Evaluation
     • Evaluation of the results
     • Review the process
     • Determine next steps
   • Preparing the data
     • Data selection
     • Data cleaning
     • Construction of the data
     • Data integration
   • Data modeling
     • Selection of the modeling technique
     • Test design
     • Construction of the model
     • Evaluation of the model

Bilder und Dateien der Mindmap