What is data science

Data Science, AI and ML
#1

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights

The most important things to learn in Data Science are:Mathematical concepts such as linear algebra, probabilities, and distributions. Statistical concepts such as descriptive and inferential statistics. Programming languages such as python, R, and SAS.

The four types of data analysis are:

  • Descriptive Analysis. What is it ?
  • Diagnostic Analysis. What happened?
  • Predictive Analysis. What will happen?
  • Prescriptive Analysis. How to make it happen

The four components of Data Science include:

  • Data Strategy.
  • Data Engineering.
  • Data Analysis and Models.
  • Data Visualization and Operationalization.

Six Qualities of a Great Data Scientist

  • Statistical thinking. Data scientists are professionals who turn data into information, so statistical know-how is at the forefront of our toolkit

  • Technical acumen. …

  • Multi-modal communication skills. …

  • Curiosity. …

  • Creativity. …

  • Grit.

#2

Data science is a method to extracting usable insights from today’s huge and ever-increasing volumes of data in today’s enterprises.

Data science entails preparing data for analysis and processing, doing advanced data analysis, and presenting the results to reveal trends and allow stakeholders to make informed decisions.

Cleaning, aggregating, and modifying data to prepare it for various processing methods are all examples of data preparation.

Algorithms, analytics, and AI models must be created and applied to analyze. It’s powered by software that sifts through data for patterns then transforms those patterns into projections to aid organizations in making better decisions. These applications are powered by a variety of algorithms and models that can predict outcomes, allowing users to make more informed decisions.

Carefully prepared tests and experiments must confirm these forecasts’ accuracy, and the findings should be disseminated through the effective use of data visualization tools that allow anyone to detect patterns and recognize trends.