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If you are a data science enthusiast, then your curiosity about the life cycle of data science projects is quite understandable. Knowing such important processes is essential in developing a better understanding of the overall subject. Data Science has come a long way since it was first introduced and is constantly evolving with time. Data Science works on data as the main subject, and all the studies and researches are conducted to derive more from the available data.
To feed all the inquisitive data scientists with the information they need, we have covered the life cycle of data science projects in great detail in this blog. Keep reading to find out about the steps involved in the life cycle.
What is a Data Science Life Cycle?
You may think of a project's data science life cycle as recurring stages that are required to be completed, and its deliverance to the client is dependent upon the successful completion of each step. Even though the life cycle contains similar steps, each company or organization follows a different approach. Data science projects require collaboration and are unsuccessful without a proper team effort. Different deployment and development teams come together on one platform to work on the given data and study it to derive various solutions and their analysis.
The data science life cycle encompasses all stages of data, from the moment it is obtained for research to when it is distributed and reused. The data lifecycle begins when a researcher or analyst comes forward with an idea or a concept. Once the concept for the study is accepted, then begins the process of collecting the relevant data. Data is stored after it is collected by the research team and is made available to other researchers to be used in the future. Once data has reached the distribution point, it is stored where other researchers can access it.
Why Do We Need Data Science?
Not too long ago, we didn't have enormous quantities of data, and it was readily available in a well-structured form to be easily stored in documents and sheets. However, as the data size increased with time, keeping big data and maintaining it became quite an obstacle and required extra effort. Companies dealing with gigantic data sizes can not rely on Excel sheets or a few folders for their storage; they want an improvised solution.
The need for maintaining and analyzing the vast data amounts gave birth to the idea of Data Science, which solves this problem using its complex algorithm, and robust technology. Data science is necessary to process, analyze, and interpret data safely. It helps the organizations better plan, set realistic goals, get a proper understanding of their current data, and focus on growth. The prominence of data science in the past few years has caused a spike in demand for data scientists throughout the world.
Five Stages of the Data Science Life Cycle
Data Science has come a long way since it emerged almost three decades back. Problems like these require a proper set of steps to tackle the issues correctly. Over the years, data scientists have developed a life cycle for data science projects and adhere to the process while working on data science problems. We all love shortcuts without realizing the damage they can provide. Some organizations prefer to jump towards the methods to solve the problem directly, without going through the proper steps. Sometimes these shortcuts solve your problem, but they almost always prove detrimental in the long run. Following the data science, life cycle steps ensure that the problem is being tackled to its core and provide a much better and more detailed analysis. The data science life cycle is divided into five steps, and we have listed the steps below along with their brief overview.
1. Business Understanding
Before you start working on your client's model, learn about the obstacles they're facing to apprehend their needs. Most people skip the pivotal step of understanding the actual problem and directly jump to the next phase and often end up in a failure or not fulfilling their client's demands. Understanding your client's issues is essential to building an efficient business model. Conduct thorough research to learn more about your client's business and ask them their expectations. Don't be reluctant to spend your time on the understanding phase, take help from the relevant people, conduct multiple meetings, and do whatever is required until you have understood the existing problems and issues. Business analysts are normally given the duty to collect customer information and send it to the data scientists team for analysis. Identifying and analyzing the objectives with the utmost accuracy is crucial, as even a tiny mistake can result in a project's failure.
2. Data Collection
Data science is non-existent without data, so collecting data is one of the most crucial life cycle stages for data science projects. When you have clearly understood your client's requirements and have analyzed the existing system and its problems, it's time to map down how to collect the required data. Consult your client, conduct team meetings, and do proper research to develop your data requirements and the methods to obtain them. Seasoned data scientists have their own ways to source, collect, and extract data to meet clients' expectations. Usually, the data analyst team is assigned to obtain the data, and they either source data via web scraping or with third-party APIs.
3. Data Preparation
Data is primarily obtained in a raw form, and the proper alignment of the scattered form is required to perceive it as information. It has to go through a cleaning process and be arranged in a proper format to be understood and used in an analytical step. The process of refining data is called data cleaning and is the core of data preparation. Once the data is presented in a structured form and is free from useless information, it helps you devise a strategy much better. Multiple sources are used for extraction during the data collection process, but they have to be compiled together in an understandable form for proper analysis. When data is typically acquired from various places, it sometimes is incomplete or has many gaps to make any sense for analysis. Data scientists have designed multiple methods to extract the missing piece and help structure the data. They also take the help of the exploratory data analysis (EDA), which identifies the important process of conducting initial research on data to find patterns, detect anomalies, and test hypotheses using statistical results and graphical representations.
4. Data Modelling
Data modeling is perhaps the core of the data science life cycle. In this step, the data scientist has to choose the appropriate model depending upon the problem. Using structured data as input, a model then outputs the desired result. Once the model family has been decided, the data scientist has to choose the right algorithm depending upon the model family that would give the best results and implements them effectively. Data scientists use the modeling stage to find data patterns and derive insights. The modeling stage marks the start of the entire data science system's analysis and allows you to measure the accuracy and relevance of your data.
5. Model Deployment
The final step of the life cycle of a data science project is the deployment phase. The step focuses on developing a delivery procedure to deliver the model to the users or a machine. The complexity of the deployment step depends upon the nature of the project. At times, it would require you to display your model output, and sometimes it would need you to scale your model to the cloud to thousands of users. Normally this step is taken care of by the application developers, SQA team, data engineers, machine engineers, and cloud engineers.
FAQs
Q. What is the life cycle of a data science project?
Ans: The life cycle of a data science project comprises the five stages that lead to the project's completion. The five stages are listed as follows:
- Business Understanding
- Data Collection
- Data Preparation
- Data Modelling
- Model Deployment
Q. What is the first step in the data science life cycle?
Ans: The first step in the data science life cycle is business understanding. Data scientists should start with understanding their client's requirements first before jumping on to the next steps.
Q. What are the final stages of data science methodology?
Ans: The final stages of data science methodology include structuring the data, choosing the appropriate model, and then deploying the model.
Final Thoughts
Data science is the field that revolves over statistical methods, innovative technologies, and scientific thinking. We have tried to cover the data science life cycle in this blog and have tried to explain every step concisely and clearly. Still, if you are unclear about anything, don't hesitate to comment, and we will answer your queries ASAP!
