CookieCutter In Data Science

Cookiecutter is a useful Data Science concept which will come in handy for any data science for beginners’ course. Also, there has been an increase in the number of data science certification and data analytics certification exploring this concept. Let us explore more about Cookiecutter:

Learning Of Blog

  • What is Data Science?
  • What is Cookiecutter?
  • Benefits of Cookiecutter
  • How to use it?
  • Conclusion
  • Benefits of Global Tech Certifications


What is Data Science?


Data science is a culmination of multiple inter-disciplinary fields making use of processes, methods, algorithms to extract meaningful insights from a host of structured and unstructured data.


Data science is the science of extracting useful data utilizing data mining and big data. It aims to consolidate the practices of statistics, data analysis, machine learning, etc. to better understand the underlying facts and thus be able to make useful inferences. While there are various courses available online, the best data science programs online consist of all the relevant and associated practices included in it.


Data science lifecycle consists of the below phases:


  1. Data acquisition
  2. Data mining 
  3. Data preparation and cleaning
  4. Data modeling
  5. Hypothesis 
  6. Data evaluation and predictions
  7. Data deployment and visualization

data science lifecycle

Data Analysis


Data analysis, as a part of the data science, is the process of inspecting the data, cleansing the data, transforming the data, and modeling the data. Aim of data analysis is to derive useful and helpful insights and information that helps in making data-backed decisions.


Data mining is one such useful data analysis technique that focuses on predictive Analysis than descriptive one utilizing statistical modeling and information discovery.


Statistical Analysis includes the use of descriptive statistics, exploratory data analysis, and confirmatory data analysis.


About Machine Learning


Machine learning is the process of continuous improvement in overtime with experience. Machine learning is a subset of Artificial Intelligence. Machine learning helps build a model for predicting or making future assumptions backed by data.


Its uses include computational statistics, computer vision, and email filtering, etc.


What is Cookiecutter? 


Cookiecutter is a Command Line Interface used to create the project template to keep the data in an organized and standardized manner. It is also used to customize folder and file names.


Cookiecutter helps a data scientist to manage and keep data in a structured and predefined format, thus making it more straightforward for any new data scientist to understand the structure and start the Analysis. Cookiecutter helps make a default project structure that is universal and logical, thus making it easy to find the various moving parts that are involved quickly.


Benefits of Cookiecutter


Cookiecutter works by copying the source directory into the new project. Cookiecutter also replaces all the names that it finds, which are surrounded by templating tags with titles available in the file cookiecutter.json. Cookiecutter allows the data scientist to bootstrap a new project from a standard form, thus making sure that all the usual mistakes are avoided while starting any new project.


Cookiecutter is built-in Python, and an individual must have Python installed in their system.

Another benefit of Cookiecutter is that it helps an individual to understand and decipher the code that has been written many months or, in that case, maybe many years ago. Thus, a good project structure helps and supports practices that make it easier to comprehend and decipher a work that has been done many months or years before.


How To Use Cookiecutter?


However, one must have the required specifications to be able to run a cookiecutter. The requirements are as mentioned below:


  1. Python 2.7 or 3.5
  2. Cookiecutter Python package; 1.4.0; pip install Cookiecutter



To run the cookiecutter python package, one needs to run the code $ pip install Cookiecutter.

Next in the process is to create a directory on the local system or the computer. The name of the directory should be the name of the Cookiecutter template. However, it is not a constraint since the project which gets generated does not need to use the Cookiecutter template name.


Now, inside this directory, one can create the directory tree that will be copied in the project just generated. Also, anything inside the templating tags can be placed inside a namespace. To finish the process, the individual needs to create the cookiecutter.json file so that Cookiecutter can look for all the templated items.


All done, the project has been created.




It is highly important to have clear understanding of Cookiecutter in order to keep the data in an organized and customized fashion. This is done by predefining the format and do analysis after insertion of the data in that universal format. This is also logical to know this command line to learn Machine Learning. All you need is installing Python.