A glimpse at the world of data science

Data science is surely re-defining the structures present in society. A day in the life of an expert data scientist is full of challenges, responsibilities, with equal moments of enjoyment. The person has to have a curious soul with a willingness to acquire information from sources. This is considered as one of the main traits of a great data scientist. Here is a broader look at how things work behind the scenes.

1.     Know Your Target

  • As a person who possesses enough knowledge in the field, he handles the problems that are a concern for the business.
  • In the process, the person has the liberty to ask as many questions as he wants from the party.
  • Depending on the response, the data scientist starts his work accordingly.

2.    Data Acquisition Begins

  • The process extends to its next level of resolution which is data acquisition from multiple sources such as:
      1. web servers
      2. logs
      3. databases
      4. API’s
      5. online repositories
  • It goes without saying that the person has to invest a respectable amount of time and effort behind it.

3.     Data Preparation

  • It is a mix of two steps, i.e., data cleaning & transformation in equal proportion.
  • Breaking it down, data cleaning implies removing the junk out, such as:
      1. Inconsistent data types
      2. Misspelled attributes
      3. Duplicate and missing values
  • Data transformation includes the use of special tools to convert the acquired data into an easy and simple data structure so that the rest of the team can grasp it better.

4.    Analysis of Data

  •  Another crucial step that experts perform with an utmost level of devotion is analyzing the data they get from their sources.
  • In this process, they define and refine the variables that will be in use for generating a new model.
  •  It is, in fact, the most crucial move that data scientists perform no matter what.

5.    Data Modeling

    1. The person has to implement various advanced machine learning techniques on the data, such as:
    2. KNN
    3. decision tree
    4. naive bayes
  • The data scientist identifies the model that best suits the purpose.
  • He then trains and models the dataset, using the best programming, which could be Python, SAS, etc.

Visualization & communications

  • Once data modelling is complete, engineers have to undergo the trickiest of all processes, i.e., meeting the clients and explaining the results of the analysis.
  • He then further proceeds to the final step.

Deployment and maintenance

  • The person tests the model, ensuring that it is worthy of passing to the production center.
  • He keeps real-time track of the arrangement by preparing statistical charts and graphs.

As you can see, the exciting work schedule of a data scientist has its share of challenges to it. They have to be hardworking and goal-driven to make a mark in this diverse industry. Various logistic companies are on a hunt to grab young skillful talents in this field.  Even airline companies and genomic data centers are equally interested in hiring.

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