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Process of Data Science

The data science process, which is a structured framework used to complete a data science project, is something that virtually all professionals agree on, despite the fact that data scientists frequently disagree regarding the implications of a particular data set. There are numerous frameworks, some of which are better suited to business use cases and others to research use cases.

In this post, we’ll talk about the most widely used data science process frameworks, which ones are best for each use case, and the key components of each one.

What is the process of data science?

A methodical approach to resolving a data problem is known as the data science process. It gives you a well-organized structure for expressing your problem as a question, choosing a solution, and then presenting it to stakeholders.

Data Science Life Cycle

The data science life cycle is another name for the data science process. Both terms refer to a workflow process that begins with the collection of data and concludes with the deployment of a model that should provide answers to your inquiries. They are interchangeable. These are the steps:

Framing the Problem

The first step in the data science life cycle is understanding and framing the problem. You can construct an efficient model that will benefit your organization with the assistance of this framing.

Collecting Data 

The collection of the appropriate set of data is the next step. To get meaningful results, you need targeted, high-quality data and methods to collect it. You will probably need to extract the data and export it into a format that can be used, like a CSV or JSON file, because a lot of the roughly 2.5 quintillion bytes of data that are created every day are in unstructured formats.

Data Cleaning

The data science life cycle is another name for the data science process. Both terms refer to a workflow process that begins with the collection of data and concludes with the deployment of a model that should provide answers to your inquiries. They are interchangeable. These are the steps:

During the collection phase, the majority of the data you collect will be unstructured, irrelevant, and unfiltered. Your analysis’s accuracy and efficacy will be heavily influenced by the quality of your data because bad data leads to bad results.

Duplicate and null values, corrupt data, inconsistent data types, invalid entries, missing data, and improper formatting are all eliminated through data cleaning.

Despite the fact that this step requires the most time, correcting data errors is crucial to building effective models.

Exploratory Data Analysis (EDA) 

You can begin an exploratory data analysis (EDA) now that you have a lot of well-organized, high-quality data. Through effective EDA, you can discover useful insights for the subsequent phase of the data science lifecycle.

Model Construction and Application 

The actual data modeling will come next. This is where you’ll utilize AI, factual models, and calculations to extricate high-esteem experiences and forecasts.

Last but not least, you will present your findings to various stakeholders. To accomplish this, every data scientist must expand their visualization skillset.

The intricate back-end work that went into building your model often won’t matter to your stakeholders because they are mostly concerned with what your results mean for their company. Clearly and engagingly highlight the significance of your findings to strategic business planning and operation.

Steps and Framework for the Data Science Process 

There are a number of different data science process frameworks that you should be aware of. Even though they all want to show you how to create an efficient workflow, some are better for certain use cases.


Cross Industry Standard Process for Data Mining is spelled CRISP-DM. It is a methodology and process model that is used by the industry and is popular because it can be changed. It is also a tried and true strategy for project management in data mining. The data process life cycle is broken down into six stages in the CRISP-DM model. Those six stages are:

1. Understanding the Business 

The first step in the CRISP-DM procedure is to define the objectives of the business and focus on the data science project. The metric you want to alter should not be the only thing to clearly define the goal. Metrics cannot be altered by comprehensive analysis without action.

Data scientists meet with stakeholders, subject matter experts, and others who can shed light on the issue at hand to gain a deeper comprehension of the company. They may likewise do starter exploration to perceive how others have attempted to tackle comparable issues. In the end, they will have a plan for resolving the issue and a problem that is clearly defined.

2. Data Understanding 

Understanding your data is the next step in CRISP-DM. 

You’ll figure out what data you have, where you can get more of it, what your data includes, and how good it is during this phase. In addition, you will decide how and with what data collection tools you will begin. The format, quantity, and records or fields of your data sets—your initial data’s properties—will then be described.

You will be able to begin exploring your data if you collect it and describe it. After that, you can ask data science questions that can be answered with queries, visualization, or reporting to come up with your first hypothesis. Last but not least, you’ll check your data to see if there are any errors or missing values.

3. Preparation of the Data 

Preparation of the data typically consumes the most time, and you may need to revisit this step multiple times throughout the course of your project.

Data comes from a variety of sources and is typically inaccessible in its raw form due to missing or corrupted attributes, contradictory values, and outliers. These problems can be fixed with data preparation, which also improves the quality of your data so that it can be used effectively during the modeling phase.

There are numerous tasks involved in data preparation that can be carried out in a variety of ways. The most important steps in preparing data are:

  • Cleaning the data: fixing inaccurate or incomplete 
  • Data Integration of data: bringing together data from various sources 
  • Data transformation: formatting the data 
  • Reducing the data
  • Data discretization: reducing data to its simplest form simplifying data management by reducing the number of values 
  • Feature engineering: choosing and changing factors to work better with AI

4. Data modeling can be done in a variety of ways. 

Based on the business’s objectives, the variables involved, and the tools available, you will select the best option.

You will produce two reports after selecting your modeling method. The first one will explain the method you’ll use for modeling. The second will be a record of the assumptions that your modeling report relies on—for instance, if your model requires a particular kind of data distribution.

You will design tests to see how well your model works once you have chosen your modeling method. Your test design will be your deliverable for this step. This might involve dividing your information into preparing information and testing information to stay away from overfitting, which happens when you plan a model that impeccably fits one bunch of information yet doesn’t work with others. During this phase, it is essential to avoid introducing bias into your data.

Your model will be built next to address your specific business objectives. This will result in the delivery of three items:

A list of parameter settings

  • A description of the models;
  • The models themselves. 
  • Evaluating your models is the final step in the modeling phase. You’ll examine them from a business and technical perspective. It is possible for subject matter experts on your project team to review your models as well.

Your model review’s findings will be summarized in a model assessment, along with a ranking of the models you’ve created. You can modify your parameters and carry out a second round of modeling at this point. 

5. Evaluation During 

The evaluation phase, you will evaluate the model in light of your company’s objectives. After that, you’ll go over your work process, explain how your model will benefit the company, provide a summary of your findings, and make any necessary adjustments.

In the end, you’ll decide what to do next. Is your model prepared for organization? Is a new dependency project or a new iteration required?

6. Deployment

The CRISP-DM methodology’s deployment phase is the final one, but it is not always the end of your project. You will plan and document how you intend to deploy the model and present the results during the deployment phase. During the deployment phase, you will also need to keep an eye on the results and maintain the model.

Significance of Data Science Process

Your work will have structure and order if you follow the data science process. Your workflow can go off without a hitch if you stick to a tried-and-true method. You also won’t forget anything. Because it has been demonstrated to produce the most accurate results, a good data science process gives you confidence in your results.

Choosing a data science process will show you how to collect data, transform it into a high-quality input, build and evaluate models, interpret and share your results, and so on. If you are applying for a job in data science, you should show your knowledge by demonstrating projects that follow the data science process.

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