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Data Science Consultancy company in Delhi

Data science is a multidisciplinary field that combines techniques from mathematics, statistics, computer science, and domain-specific knowledge to extract valuable insights and knowledge from data. It involves the collection, cleaning, analysis, and interpretation of data to solve complex problems and make informed decisions.

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Which material types can you work with ?

  • Description: Data that is organized and formatted in a specific way, often in tables with rows and columns.
  • Examples: Relational databases, CSV files, Excel spreadsheets.
  • Tools: SQL, Pandas (Python library).

Unstructured Data:

  • Description: Data that lacks a predefined structure or schema.
  • Examples: Text data, images, audio, video.
  • Tools: Natural Language Processing (NLP) libraries (NLTK, SpaCy), Computer Vision libraries (OpenCV), Audio processing libraries.

Semi-Structured Data:

  • Description: Data that is partially structured but doesn't conform to the strict structure of traditional relational databases.
  • Examples: JSON, XML.
  • Tools: JSON and XML parsers, Pandas for semi-structured formats.

Time Series Data:

  • Description: Data collected over time intervals, where the order and timing of observations are crucial.
  • Examples: Stock prices, temperature readings, sensor data.
  • Tools: Pandas, Time Series analysis libraries (statsmodels, Prophet).

Spatial Data:

  • Description: Data associated with geographic locations.
  • Examples:GPS data, GIS data, maps.
  • Tools: GeoPandas, Folium, Shapely.

Text Data:

  • Description: Data consisting of textual information.
  • Examples:Documents, articles, social media posts.
  • Tools:NLP libraries (NLTK, SpaCy), Text mining tools.

Categorical Data:

  • Description:Data that represents categories or labels.
  • Examples:Colors, genres, product categories.
  • Tools:Encoding techniques, One-Hot Encoding.

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