Data Science


  • October 21, 2022

Motivation behind writing this article

When Data Science became more prevalent, more and more people started using it. Nowadays, most companies can make use of the large volumes of data they have accumulated from their customers, which help them make more informed decisions about the services they provide. Data Science has also helped in making models that allow them to make predictions, such as predicting expected sales turnover, classifying information, identifying if a customer will upgrade to the latest plan or leave the service. These new abilities have become so important to many companies that there has been a rapid demand for skilled Data Science professionals recently. As a Data Scientist, anyone would follow the below life cycle to complete any project.

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Let’s understand each stage in detail.

Business Understanding

  • Business Understanding is an important stage in the data science methodology because; it generates the data that will be used in the study.
  • It clearly defines the problem and the needs from a business perspective. It ensures that the work generates all possible solutions.
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Data Collection

  • The data science project starts with the identification of various data sources, which may include web server logs, social media posts, data from digital libraries.
  • Such as the US Census datasets, data accessed through sources on the internet via APIs, web scraping, or information that is already present in an excel spreadsheet.
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Data Understanding

  • Using data science, you analyze datasets that consist of cases, which are described by their variables. Dataset is represented by a table, where a case is a row and a variable is a column.
  • In R and Python, you use the data frame objects to represent datasets to analyze. Data frames look like tables; however, they are matrices. This means that you can access the data position.
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Data Preparation

  • This is process of preparing the raw data and making it suitable for a machine learning model. It is the first and crucial step while creating a machine learning model.
  • A real world data generally contains noise, missing values, and maybe in an unusable format that cannot be directly used for machine learning models. Data preprocessing is a required task for cleansing the data and making it suitable for a machine learning model which also increase the accuracy of machine leaning models.
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Model Building

  • In this process, data modeling focuses on developing models that are both formulating every step and gathering the techniques required to achieve the solution. These models are based on the analytics-approach which was taken either statistically-driven or machine learning-driven.
  • The model will perform its tasks more effectively after optimization of hyper parameter. Depends on the problem to be solved and the type of data, an appropriate algorithm will be chosen.
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Model Training

  • Training sets are used to fit and tune your models. Training a machine learning algorithm can take some time, but it usually involves running data sets through the algorithm numerous times.
  • Each round, the algorithm gets better at recognizing the patterns in the data and learning from them.
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Model Testing

  • The test set is a set of observations used to evaluate the performance of the model using same performance metric.
  • A set of examples used only to access the performance of a fully-trained classifier.
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Model Evaluation

  • Evaluation metrics helps us to measure and monitor the performance of the model during training and testing.
  • The quality of statistical model can be measured. It helps us to quantify the performance of the model.
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Model Deployment

  • Model deployment is the process of putting machine learning models into production. This makes the model’s predictions available to users, developers or systems.
  • So, they can make business decisions based on data, interact with their applications (like recognize a face in an image) and take further actions.
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The Data science life cycle is one of the basic concepts that should be covered and studied to understand the different phases of a data science project successfully. Hope this article helped you to understand how data science is useful in a project.

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Author:John Gabriel TJ

Managing Director || Sr. Data Science Trainer || Consultant || Made 150+ Career Transitions || Helping people to Make Career Transition with a Customized RoadMap based on their past experience into Data Science

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