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Data Science and Machine Learning

 Basic Concepts of Data Science and Machine Learning 


Data Science is the extraction and analysis of the relevant information from Data.

Machine Learning is the part of Data Science, which enables the system to process datasets autonomously without any human interference by utilizing various algorithms to work on the massive volume of data generated and extracted from numerous sources. 


Benefits:

Data Science
  • Helps in finding and refining target viewers 
  • Ensure better communication between service providers and service utilizers.
  • Improved business value and better risk analysis. 

Machine Learning

  • Supportive in marketing and predicting accurate sales forecasts
  • Helpful inaccurate medical diagnoses
  • Supportive in the elimination of data duplication and erroneousness
  • Caring in spam detection
  • Provide appropriate product recommendation

Required Expertise

Data Science 

  • Programming Skills
  • Data Warehousing 
  • Statistics
  • Mathematics
  • Software Engineering
  • Data visualization and communication 

Machine Learning

  • Basic Programming Skills
  • Statistics
  • Mathematics
  • System Design
  • Software Engineering 
Applications:

Data Science

  • Recommender Systems
  • Internet Search Engine
  • Image recognition
  • Speech Recognition
  • Gaming
  • Airline Route Planning
  • Comparative analysis of Price 
  • Fraud and risk detection
  • Robotics
  • Self-driving cars

Machine Learning

  • Virtual Personal Assistant
  • Video Surveillance
  • Online Fraud detection
  • Social Media Services
  • Email Spam and Malware filtering 
  • Operational Client Support
  • Product recommendation

Top Tools

Data Science

  • Python
  • R (Statistics Language for computation and graphics)
  • Jupyter Notebook
  • Tableau
  • Keras

Machine Learning 

  • Python
  • C++
  • R (Statistics Language for computation and graphics)
  • Jupyter Notebook
  • Tableau
References:

Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900. https://doi.org/10.1080/00207543.2020.1793011
 
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415

Van Der Aalst, W. (2016). Data science in action. In Process mining (pp. 3-23). Springer, Berlin, Heidelberg.

Cielen, D., & Meysman, A. (2016). Introducing data science: big data, machine learning, and more, using Python tools. Simon and Schuster. 


Any comments or suggestions are welcome.

Thanks for reading this blog. 


 

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