Data Science

Data Science Life Cycle – Steps Simplified (Beginner Friendly)

Pinterest LinkedIn Tumblr

As we all know, data science is the most used buzzword today. It is a vital part of every company’s operations. The Data Science process has a lot of steps, but if you understand each one, you’ll be able to predict what’s going to happen next. Data is everything to data scientists. The goal is to clean, enrich, and transform the data to be used effectively. Each step of the Data science life cycle is important, from data exploration to drawing conclusions.

This article will walk you through the data science life cycle in detail and how you can keep track of it to ensure that you’re doing each of the steps properly.

What is Data Science?

Data science is the art and science of extracting knowledge and insights from data. This can range from analyzing data to gaining insights to more advanced tasks like predictive analytics and machine learning. Data science is crucial to practically every area of corporate operations. It assists firms in increasing operational efficiency, identifying new business prospects, and improving marketing and sales strategies.

Data science is a constantly evolving industry, and it can be challenging to keep up with it. Each phase has an associated toolset to get you started. These tools are vital for data science projects and can be learned in our data science course.

Now, let’s examine what goes on behind the scenes in the data science process. 

What is the data science life Cycle? 

The data science life cycle is a 7-step process that helps you move from data collection to analysis and decision making and determine how to make the most of your findings. It’s important for data scientists to understand the steps in this lifecycle because it helps them know how to proceed and ensures that they are starting with a fresh set of eyes when they start a new project.

Let’s explore each step in-depth so that you may gain a better understanding of what it takes to become a successful data scientist!

  1. Understanding business problems:

Of course, the complete cycle revolves around the goal of the organization. Hence, before collecting data and analyzing it, the first thing is to define and understand the real problem. You should be able to transform the business needs into questions followed by actionable insights.  

  •  Data collection:

The next step after understanding the problem is to collect the right set of data. This means gathering all of the information you need for your project— whether it’s as simple as tracking sales or as complex as building an artificial intelligence system. While collecting data, You should also consider security and privacy issues; if you have access to sensitive information such as customer credit card information or medical records, you must take extra precautions to ensure that it is not compromised!

  • Data preparation and data cleaning 

The next stage after gathering data comes data preparation. This includes choosing the right data, integrating by merging datasets, data cleaning, and processing it.

Data preparation is the most time-consuming but perhaps the most important step in the entire data science lifecycle. Your model will only be as accurate as the data you have available to you.

  • Exploratory data analysis (EDA)

The next step is to analyze the data and identify patterns, trends, and correlations within it. In this step, the data is analyzed using various statistical methods, and dependent and independent variables are identified. When data is analyzed thoroughly, it becomes clear which data or features are essential and how widely they are spread. Numerous plots are used to visualize data for better understanding. Tableau, PowerBI, and other visualization and data exploration tools are widely used in this step. 

  • Data modeling and evaluation 

The actual modeling of data takes place during the model development and evaluation phase. According to many data scientists, this is where the real magic happens. This stage involves determining which model type is most appropriate for the problem at hand, be it a classification, regression, or clustering problem. 

Finally, we evaluate the model by testing its precision and applicability. In addition to this effort, we must ensure that the produced model is free of bias and hits the right balance between specificity and generalizability.

  • Interpreting results

Following model building and evaluation, you must communicate the model results and report your findings to stakeholders. Management Executives aren’t bothered about the model’s complexity. They want to know how your model can help them grow their business. Thus, every data scientist must have strong presentation and data storytelling skills to demonstrate how a model addresses the business concerns explained in the initial step of the data science life cycle.

  • Model deployment 

After reporting the results, the next step is to deploy your model if the stakeholders are satisfied with the outcome. Also, Before deploying the model, you must ensure that you have selected the right solution following a thorough evaluation. It is then deployed on the specified channel and format. This is the final step of the data science life cycle.

Note: Each stage of the data science life cycle outlined above must be carefully executed. If any step is performed incorrectly, it will affect the next step, and the entire effort will be wasted. For instance, if data is no longer collected properly, records will be lost, and an ideal model cannot be built.

All of the processes explained above are equally applicable to both beginners and experienced data scientists. As a newbie, your job is first to learn the technique, then practice and deploy smaller data science projects. Once you’ve completed the data science life cycle, you’re ready to take the next step toward a career in this industry. 


To summarize, the data science life cycle is a linear, iterative process that is focused on the business’s specific problems, goals, and strategies. Overall, these are the seven elements of the data science life cycle.

They’re all interconnected, but they’re not the same thing. With that being said, working with each of them is a vital part of the data science process and will allow you to come to your own conclusions and solutions for whatever business you are working on.

As data becomes more available, you might consider making a career change to this exciting and innovative field. Simply check out the Data science course in Pune, and maybe you’ll discover an opportunity that will allow you to leverage one or more of these steps yourself. And that’s an opportunity worth considering.