Process of Data Science

Data science as a field sound simple, there is data and science is used to find meaning to the data. But it is not that simple in practice. Data volume collected is not in its simplest form, usually unstructured and raw and the tools used need expert knowledge. One can say that the entire flow of data science into a data product is a technical and complex process which needs training and practice.


Data science is a domain which demands skills in mathematics, statistics, and computer software and programming. Data science use sophisticated models to find meaningfulful insights. It is a field which has entered every other industry at a rapid speed. Several data scientists are daily finding solutions to problems posed by the market, business environment, customers and clients. So why these businesses are in such a dire need of analytics and how do analytics help them.

  • Helps in knowing one's customer and their needs from selling to post-purchase satisfaction.
  • Helps in marketing and understanding marketing trends and opportunities.
  • Helps in optimizing production, operation, human resource etc to enhance the performance of the business unit.
  • Helps in branding and communicating with the outer world, and make one's business visible by digital marketing and social media marketing.
  • Helps in innovation and real-time experimentation, which in turn saves a lot of time and effort.

Therefore, one can say data science increases the business value and helps in competitiveness with other players effectively.


This is one of the major question asked, that what a data scientist do in their day?

  1. Frame a problem: to frame a problem one needs to understand the goals of the person whose project one is handling. What one wants to achieve and what are the hindrances. The problem should be clear and simple, and not compounded as it is the stepping stone and without a problem, one will have no direction.
  2. Collecting raw data: according to the problem framed, one needs to obtain all the data which includes the variables in question. Data can be collected from internal databases or can be bought from external datasets.
  3. Process the data for analysis : data collected are usually raw and unstructured, especially if they are not well maintained. To analyze the data one needs to make sure that all the errors and errors like missing values, data range errors, time zone differences and invalid entries are all cleaned and corrected.
  4. Explore the data: this is also called as exploratory data analysis (EDA), more like playing with the data. Analysts need to prioritize the questions they want to ask and search in the data. Data have many trends and patterns hidden in them, analyst job is to identify such patterns that can be turned into insight.
  5. Machine learning and algorithm building: this is the deep exploration and visualization step; here the data explored is put to use to create a story. Data are put through various mathematical and statistical tools and programs to find a meaning to it. Data are used as input for different algorithms for predictive analysis.
  6. Communicate results: insights that are collected has to be interpreted and communicated to the management professionals, it's like storytelling in such a way that non-technical people can understand. Proper presentation of results will lead to decision making and timely action.

Data scientists do have a challenging role, as they are now people who find problems and the means to their solutions too.

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