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Articles Home » Business Fundamentals for Data Science » Business Fundamentals for a Data Scientist

Business Fundamentals for a Data Scientist

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A data scientist is a multi-disciplinary person who uses math, programming/technology, domain expertise to give competitive edge to a business. The demand for data scientists has grown significantly in recent years as there is a large amount of data that can be analyzed to improve a company or business.


 


A data scientist is difficult to find as the role requires a multiple skillset. The responsibilities include collecting and exploring the data to find meaningful patterns and apply different modelling techniques to solve a particular business problem. The data scientist must be able to communicate his ideas and observations from the data to clients or business stakeholders and help them in business decisions.


 



 


Mathematics and Statistics



  • Math is one of the key discipline of all sciences, the same goes for data science as well

  • Basic matrix algebra, probability and statistics play a major role in data science


 


Technology



  • Data Science techniques can be implemented using different technologies. Python and R are the 2 most widely used programming languages for data science

  • Tensorflow, keras and pytorch are the major libraries used for deep learning


 


Business



  • Domain expertise or business knowledge is a key factor that allows one to get an idea of how data science knowledge can be used to solve specific business problems

  • Even before starting a data science project, business expertise can be used to formulate a problem statement which is helpful to lead the project in the right direction


 


 


The 4Ms of Data Scientist Skill Training



The success of a data scientist does not depend just on gaining expertise at programming languages like python or R, but understanding the data available and how to use it to meet the business targets by building the appropriate model increases  the value of a data scientist. The data scientist should be able to communicate with the team and also with the domain experts if necessary.


 


Business Requirement


The first step for a data scientist is to define the objective by discussing with customers or stakeholders to identify the business problems and define the target metric for the project.


Define objectives (Credits: microsoft.com)




  • A central objective of this step is to identify the key business variables that the analysis needs to predict. We refer to these variables as the model targets, and we use the metrics associated with them to determine the success of the project. Two examples of such targets are sales forecasts or the probability of an order being fraudulent.




  • Define the project goals by asking and refining "sharp" questions that are relevant, specific, and unambiguous. Data science is a process that uses names and numbers to answer such questions. You typically use data science or machine learning to answer five types of questions:



    • How much or how many? (regression)

    • Which category? (classification)

    • Which group? (clustering)

    • Is this weird? (anomaly detection)

    • Which option should be taken? (recommendation)


    Determine which of these questions you're asking and how answering it achieves your business goals.




  • Define the project team by specifying the roles and responsibilities of its members. Develop a high-level milestone plan that you iterate on as you discover more information.




  • Define the success metrics. For example, you might want to achieve a customer churn prediction. You need an accuracy rate of "x" percent by the end of this three-month project. With this data, you can offer customer promotions to reduce churn.