Data Types Used in Data Science

Data Types Used in Data Science
4 min read
26 October 2023

Data Science is a rapidly evolving technological concept. It deals with extracting, processing, and analyzing raw data to gather meaningful information. The concept of Data Science is used in internet searches, gaming, AR/VR, healthcare, image and speech recognition, fraud detection, etc. Data Science is a growing career prospect. Therefore, aspiring professionals are suggested to join Data Science Training in Gurgaon to learn more about this field. Training in Data Science helps one build a secure career as a Data Science professional. Such training helps professionals get hired in positions like Data Scientists, Data Analysts, Data Engineers, Data Architects, ML Engineers, etc.

This article explains the types of data used in Data Science procedures and the major Data science processes. Read on to know more.

Data Types Used in Data Science

Data Types Used In Data Science

Data science processes deal with large volumes of data categorized under different types. These data can be extracted from various sources and need different strategies to gather information.

Let us look at the different types of data used in Data Science in detail.

  1. Qualitative Data Type

Qualitative Data Types are also known as Categorical Data. These data types in Data Science describe an object under consideration using a finite set of discrete classes.

Qualitative Data Types can be categorized as Nominal Data and Ordinal Data.

  • Nominal Data: It refers to the set of values without any natural ordering. Nominal Data are not quantifiable, i.e., not measured in numerical units.
  • Ordinal Data: It refers to the values that have a natural ordering while maintaining their value class.
  • Quantitative Data Type

Quantitative Data Type quantifies various things by taking different numerical values to make the data countable.

Quantitative Data Type is further categorized as Discrete Data and Continuous Data.

  • Discrete Data: It refers to the numerical values that fall under integers or whole numbers.
  • Continuous Data:This refers to the fractional numbers that can break into smaller numbers and take any value.

Data Science Procedures

Data Science professionals use the OSEMN Data Science process to implement and execute Data Science strategies. OSEMN is the compilation of Data Science procedures. It includes Obtaining Data (O), Scrubbing Data (S), Exploring Data (E), Modelling Data (M), and Interpreting Results (N).

Let us look at this process in detail.

  1. Obtaining Data (O):It involves extracting newly acquired, pre-existing data or data repositories downloaded from the internet.
  2. Scrubbing Data (S): Scrubbing in Data Science involves changing the date values to a standard format, fixing spelling errors, mathematical inaccuracies, etc.
  3. Exploring Data (E): This process is used to plan further data modeling strategies. Exploring allows Data Science professionals to understand the data using.
  4. Modeling Data (M): This involves using Machine Learning techniques like classification, association, and clustering to get deeper insights and predict outcomes more accurately. 
  5. Interpreting Results (N): This step involves analyzing the results obtained from the procedures mentioned above by preparing charts, diagrams, graphs, etc.

Conclusion

To summarise, Data Science is an important and rapidly evolving technological concept. Today, Data science is widely used in internet searches, gaming, AR/VR, healthcare, image and speech recognition, fraud detection, etc. This technology deals with extracting, processing, and analyzing raw data to gather meaningful information. Being a widely used technology, the Best Data Science Courses with Placement enable aspiring professionals to make significant career progress. These training courses train individuals in the latest Data Science trends and help them get hired in reputable positions.

The data used in Data Science can be structured, unstructured, semi-structured, qualitative, and quantitative. Qualitative or Categorical Data Type is subdivided into Nominal Data and Ordinal Data. The Quantitative Data Type in Data Science is subdivided into Discrete and Continuous Data. Data Science professionals use the OSEMN Data Science process to implement and execute Data Science strategies. OSEMN is the compilation of Data Science procedures. It includes Obtaining Data (O), Scrubbing Data (S), Exploring Data (E), Modelling Data (M), and Interpreting Results (N).

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Satish kumar 0
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