Data Science By DevTechToday June 15, 2022

A Dive into Data Science Life Cycle


Data Science is a field of study comprising various fields, as mentioned in the image below, such as Computer Science, Mathematics, and Domain Expertise.

Data Science life cycle:

The life cycle of data science includes the commonly followed steps to meet the required outcome. This model changes based on the availability of data, time, type of project, and the number of teams; however, the structure given below will remain standard. 

There are mainly 5 steps that are considered in the data science life cycle and they are as mentioned below.

1. Problem Definition
2. Data Investigation and Cleaning
3. Minimal Viable Model
4. Deployment and Enhancements
5. Data Science Ops

Let’s try to understand each step in detail:

1. Problem Definition

“WHY” is the question that needs to be answered first. The progress of the project relies on the outcome that is to be achieved.

➽ Why are we working on this problem?

➽ What is the requirement of the project? 

➽ What do we want to achieve? 

If similar questions arise in your conscious mind, you must seek the answers and after understanding the importance, you may move to the next step in data science. Only after understanding the problem can it be solved. Once the problem is identified, the team should be formed based on their expertise and experience.

You can also consider the following instructions to be conveyed in the first task:

  • Motivate the team members to stay aligned and focused
  • Aware of the team keeping the stakeholders aligned and how important is the project
  • Identify and make the aware team about the risks due to errors
  • Assess the resources you may need
  • Develop a clear plan and assign roles to team members

2. Data Investigation and Cleaning

The team needs data to work upon; the work cannot be started without the availability of the data. Identify how to avail the data if it is already available or needs to explore.

If the data is internally available, get access to the data. If the data can be collected from external or online sources, collect it or if it requires purchase, buy it.

Once the data is collected, the data scientists and analysts can start working on the following steps:

  • Documentation of the data quality 
  • Data cleaning
  • Combination of various data categories for easy understanding
  • Get the data loaded in a safe and accessible storage
  • Learning platforms
  • Data Visualisation and interpretation

After interpreting and presenting the data, stakeholders can share their valuable insights on statistics or reports. Even if this step takes more time than expected, do not skip it. Without proper investigation and cleaning, every other task will be vague. You may not achieve the planned outcome as all the Data Science Life cycle steps are interconnected. 

3. Minimum Viable Product

MVP is a development technique that emphasizes launching the latest product with limited features to get maximum understanding of the customers with comparatively less effort. In much simpler terms, it is advised not to start building a final product; instead, go with the product with limited features, gain customer feedback, and start working towards perfection.

Extending the concept considering the field of Data Science, the Minimum Viable Model is a new model, with basic features, built to gain the user’s understanding of the model and work upon it later.

The modeling approaches of Minimum Viable Product mainly fall into the following categories:

“Lab” Validation: 

Check if the model functions well in the controlled environment.

“In the Wild” Validation: 

Although the model is built within the computer of a data scientist does not mean that it may not have the real value in the real world; thus, check its effectiveness in the real world.

The following steps can also be considered as the next steps after the basic model is deployed and basic information or feedback from customers is received.

  • Rechecking for errors or unexpected consequences
  • Divert the focus on any specific topic
  • Add much cleaner data set or exclude an unnecessary set of data
  • Examine other learning algorithms 
  • Model Deployment and Enhancement

4. Deployment and Enhancement

 The deployment step generates the mechanism for delivery that you need for getting the model out to the users or another system. 

As mentioned in the Minimum Viable Model, Enhancement focuses on releasing a basic model and then improving it.

5. Data Science Operations

As data science matures into mainstream operations, Companies focus strongly on model and product that comprises planning and maintaining deployed systems. Data Science operations can fall into the following three categories.

  • Software Management 
  • Model and Data Management 
  • On-going Stakeholder Management

Key Take-Away:

There are several Data Science Life Cycle models available from various authors; they might be missing a step or might have an additional step, but all focus on the same thing, and that is to deliver an effective data science project. Data Mining Life Cycle and Modern Data Science Life Cycle are prime examples. Regardless of the life cycle you use, make sure it is well integrated with the process and team so that the entire team and stakeholders can work efficiently towards one absolute goal.

Good Luck with your journey ahead!