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What is The Data Science Life Cycle? Steps Explained in Detail

13 Jun 2023

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As the name shows, data science consists of two phrases- data and science. Data is any piece of relevant information, and science is studying the data in a systematic process. So, data science is analyzing and looking at the data to use it. This whole process is more challenging than it seems. The entire process consists of six main steps, and these steps are known as the data science life cycle. 

So if you're a data scientist aspirant or are even in any other technical field like machine learning, AI, etc., you need to have sound knowledge of the life cycle of data science. Let's start this guide blog with an introduction to the data science lifecycle. 

Introduction To Data Science Life Cycle   

As you already know, data science is the combined field of mathematics and computer science. These concepts have changed the way we look at business problems and the way we look for their solution. It has made the solution-finding process much easier, faster, and more efficient. This considerable benefit has led to companies shifting to depend on data-driven solutions.

But, despite data science being a vast concept with lots of data science techniques and tools, there is a set of processes that professional data scientists have to follow to reach desired results. This process is termed a data science process life cycle, and it involves six essential steps:

  • Framing the Problem  
  • Collecting Data  
  • Processing the Data  
  • Exploring the Data  
  • Analyzing the Data
  • Consolidating Results       

Let's take an easy example to make you understand the data science life cycle steps. Suppose a retail shop owner wants to improve inventory management. 

The first step would be to frame the problem to improve its inventory management by predicting demand for various products.

The second step would be collecting historical sales data, including product details, sales dates, quantities sold, and other relevant variables.

For the third step, the data is cleaned, missing values are handled, and features are engineered.  

In the fourth step, the performance of the models is evaluated using metrics.

While analyzing the data, various models are applied to reach the necessary conclusion. 

In the last step, the insights and predictions generated by the model are communicated to relevant stakeholders.

With the help of this simple example, you're clear about the data science life cycle. Let's look into data science life cycle steps in detail. 

The Six Stages Of The Data Science Life Cycle   

As we mentioned earlier, data science is a broad concept and process. This implies that the data science process life cycle is not a standard one. Multiple new steps might occur according to the projects or various actions overlapping. But, all in all, the six stages of the data science life cycle are explained below.

Step 1: Framing The Problem   

The first step in the life cycle of a data science project is the framing of the problem. For the professionals and analysts working on a project, it is essential to be fully aware of the situation they are working on. Along with framing and understanding the problem comes the responsibility of understanding the factors affecting the problem. These factors can be business and market trends, industry updates, etc. 

Since it is the first step of the process and the other steps would depend on this one, it is essential to identify the right problem. To check the accuracy, analysts can ask the clients a maximum number of questions and seek satisfactory answers. Only after identifying the problem can we move to the next stage. 

Step 2: Collecting Data   

The base for the remaining steps of the process is set by collecting data. The analysts must collect the data from multiple sources like social media and websites and in various formats like structured or unstructured data. It is essential to collect the relevant data for the next steps of the process. An organization's data is full of faults like duplicate values, missing values, etc. Also, due to multiple data sources, there might be a problem in combining the data. All this should be considered in advance by the analyst who is collecting the data. Once the data collection process is completed, we can move on to the following data science project life cycle step. 

Step 3: Processing The Data   

As mentioned in step 2, the data collected for processing is full of faults and mistakes and, therefore, can't be handed over for evaluation and exploration. All the issues in data, like differences in format, missing data, duplicate data, etc., are cleared in this step. Solving and managing data properly at this step might result in accurate solutions from the data, which will save time spent on the stages of the data science life cycle. This is the most lengthy but crucial phase because the data we utilize will determine how reliable our model is. The information from this stage can easily be applied moving forward.

Step 4: Exploring the Data   

 Data scientists examine the collected data for preconceptions, trends, ranges, and the spread of values during this phase. It is done to assess the databases' long-term viability and forecast how regression, machine learning, and deep learning algorithms will use them. 
One of the most crucial and lengthy phases of the data science life cycle is data exploration. We might examine data for anywhere between a day and several weeks. The data exploration stage aims to ensure that we can identify any patterns in our data that may help us resolve our issue.

More information about the data trends and patterns will be obtained in the next step, analyzing the data. 

Step 5: Analyzing The Data   

The next crucial step is to fully comprehend the data now that it is available and prepared in the necessary format. This knowledge was obtained by the study of data utilizing different statistical tools. In the analysis of data, a data engineer is essential. 

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Here, it's important to remember that your input impacts your outcome. The data prepared in the previous step will be further examined in this phase to look at the different characteristics and their associations, which will help with the better feature selection needed when using the data in the model.

Step 6: Consolidating Results   

 We now need to combine the outcomes so that stakeholders may analyze and comprehend them using the knowledge gained from all of the previous stages of our data science methodology. After we have produced visualizations, examined the data, and concluded, we must provide documentation that details the insights and visualizations to support our conclusions. These are the life cycle of a data science project. But have you ever wondered about the professionals and people involved in this process? Let's see who is required to perform these steps. 

Professionals Involved In Data Science Life Cycle

As we already said, data is generated at every second and every level of the organization. One data scientist can only manage some of the work related to the data. Henceforth, all the data-related work is divided between various job profiles. Let's see about the professionals involved in the data science life cycle. 

Data Analyst 

Cleaning, filtering, categorizing, and converting data as part of data analysis is a strategic process that produces insights that may be used for business and decision-making. Data analysts are those who carry out the process of data analysis. The data analyst is in charge of working with company leaders and decision-makers, presenting them with the data results, and making recommendations. 

Business Analysts 

Business analysts are the professionals who help organizations improve their processes and systems. They conduct studies and analyses to identify solutions to corporate problems and help businesses and their clients learn about these methods.

Data Scientists 

Technical experts in data analysis and data scientists can tackle complex problems. They gather, study, and assess enormous volumes of data while utilizing various principles from computer science, statistics, and mathematics. They are in charge of providing viewpoints that are distinct from statistical analyses.  

 Data Engineers

Data engineers concentrate on traditional data construction and procedure optimization. The professionals gather information from social media platforms, blogs, internet sites, and other inside and outside web sources and prepare it for further analysis. It will then be organized so that the data analyst can use it for further processing. 

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The positions mentioned above are only a few jobs in the tech and data field. There are many other job profiles in the area, like machine learning expert, data artistic, data mining expert, and much more. If you're interested in this career field, you can quickly start your career in the data science field by exploring some best data science courses. So, this was all about the data science life cycle. Let's proceed toward the conclusion of the blog. 


In conclusion, all data science learners must be familiar with the six fundamental life cycle steps. This guide helped you see the picture more clearly. It takes more than just having some statistical skills to succeed, and presenting a clear and actionable story is one of the most crucial skill sets.

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