Data Science Life Cycle: Step by Step Explanation [2024]

Data is the present, and it is already creating the future. Many Data Science concepts are clouded by confusion due to a lack of clarity. The general understanding of Data Science projects is usually covered in a haze of vagueness. Most people do not have a concrete comprehension of how the process progresses.

The article mentioned below will highlight the different stages in the  life cycle of data science. It will also talk about the importance of the life cycle of data science and the different people that are involved in such projects. 

What is Data Science? A Brief Introduction

Computer science and mathematics are combined to create this Data Science. It focuses on knowledge extraction from huge data sets. The way we use computer programmes to solve problems has been dramatically transformed by data science.

Organisations had to manage enormous amounts of data in the past, but they could only get a small amount of information—if any—that might be deemed relevant. As a result, many businesses were compelled to rely on their judgments on the patterns they had forecasted and the limited information they had managed to collect.

What is a Data Science Life Cycle?  

When asked to explain Data Science Life Cycle,  it is simply a series of activities you must repeatedly follow in order to finish work and provide it to your customers. Every company’s Data Science Life Cycle will be a little bit different, even though the data science projects and the teams participating in installing and upgrading the database will vary.

The Life Cycle of Data Science begins with the identification of an issue or difficulty and concludes with the offering of a solution. So now the question arises: Data Science Life Cycle has how many stages?

Right from the first step of obtaining data to analysis and result presentation, a Data Science Life Cycle is a definite procedure that has five important steps. Read on to gain a clear understanding of all of them, and the Data Science Life Cycle as a whole.

Let’s understand it more clearly by knowing what life cycle of data science with example exactly is.

Who are the different Individuals Involved in Data Science Projects?

Let’s look at some of the different people involved in the data science life cycle.

• Domain Expert- As the name suggests, they are usually those people with substantial experience in any particular domain or industry. 
• Business Analyst- Business Analysts are those who can understand the business needs of a particular domain or industry. Their main responsibilities include finding the right solutions and timeline for the said needs. 
• Machine Learning Engineer- They bear the responsibility of providing advice regarding the kind of model to be applied to generate the desired output. Furthermore, they are also required to come up with appropriate solutions for the correct and required output. 
• Data Engineer and Data Architect- Last but not least, Data Engineers and Data architects are considered the experts in data modeling. From visualization of data to storage and retrieval of data, all are done by these individuals. 

Why do we need to define the Life Cycle of a data science project?

A Data Science project typically has data as its primary component. With no data, we cannot perform any analysis or make any predictions because we are examining uncharted territory.

As a result, in any Life Cycle of Data Science example, before beginning the data science project that we have received from one of our customers or a partner, we must first comprehend the fundamental problem statement that was supplied. After comprehending the business issue, we must collect the necessary information to resolve the use case. However, many queries come up for newcomers, such as

• What format is the data needed in?
• How do I obtain the data?
• What should we do with the data?

There are many questions, yet there may be many different answers. So, the Data Science Life cycle makes all of it easy. If you are wondering Data Science Life Cycle has how many stages, then we have got that covered in the next paragraph.

Data Science Life Cycle

Answering the question, ‘Data Science Life Cycle has how many stages?’ let’s understand the life cycle of data science with example!

1. Understanding the Problem

One of the essential procedures at the start of any data science project is understanding the problem. It is important to be aware of the issue or query you are attempting to address before you can create goals for the project.

In certain situations, figuring out the issue is simple. Sometimes the customer will make a clear request, while others may ask you to solve a very broad problem. The first step in these situations is to identify clear objectives and concrete difficulties. 

The following phases of the Data Science Life Cycle will be built upon these objectives. You need to understand whether the customer requires to decrease credit loss and forecast the value of a product. 

2. Gathering Data

The second thing to be done is to gather useful information from the data sources available. The gathering of all the information accessible is required for this. If you engage with the company’s team, you may learn more about the data that is available, what data can be used to solve the problem and other details. The data must be described, together with their type, relevancy, and organisation. Visual charts are used to investigate the data. 

Technical skills, such as MySQL, are used to query databases. There are special packages to read data from specific sources, such as R or Python, right into the data science programs. You may find numerous kinds of databases, such as Oracle, PostgreSQL, and MongoDB. Yet another alternative is to obtain data through Web APIs and crawling data. Social media sites such as Twitter and Facebook let their users approach data by connecting with web servers.

The most conventional way of gathering data is straight from the files. It can be done by downloading from Kaggle or preexisting information stored in Tab Separated Values (TSV) or Comma Separated Value (CSV) format. Since these are flat text files, a specific Parser format is needed to read them.

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3. Cleaning Data

The next step is to clean the data, referring to the scrubbing and filtering of data. This procedure requires the conversion of data into a different format. It is necessary for processing and analyzing of information. If the files are web locked, then it is also needed to filter the lines of these files. Moreover, cleaning data also constitute withdrawing and replacing values. In case of missing data sets, the replacement must be done properly, since they could look like non-values. Additionally, columns are split, merged, and withdrawn as well. 

The data we use will determine our model’s reliability, so this phase is time-consuming but is also the most important. One can effortlessly use the data from this phase moving forward.

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4. Exploring Data

The data now has to be examined before it is ready for use. In business settings, it is completely up to the Data Scientist to transform the data that is available into something feasible in a corporate setting. This is why the first thing to be done is the exploration of data. The data and its characteristics require inspection. It is due to the fact that different data types, such as nominal and ordinal data, numerical data, and categorical data need different handling. 

After this, the descriptive statistics have to be computed. It is so that features can be extracted and important variables can be tested. The important variables are mostly inspected with correlation. It does not mean causation even if some of these variables are correlated. 

In Machine Learning, Feature is used. This helps the Data scientists pick out the properties that represent the concerned data. These may be things such as ‘name’, ‘gender’, and ‘age’. Furthermore, data visualization is utilized to highlight important trends and patterns in data. The significance of data can be adequately comprehended through simple aids such as bar and line charts.

For instance, knowing that particular columns are highly connected allows us to deduce that changing the value of one column would affect the other. However, even though two columns are found to correlate does not mean that somehow a growth in one value of the first column invariably causes the growth in the second. Correlation does not imply equal causation.

5. Modeling Data

After the essential stages of cleaning and exploring data, comes the phase of modeling. It is often considered the most interesting part of a Data Science Life Cycle. The first step to take while modeling data is to minimize the dimension of the data set. Every value and feature is not necessary for the prediction of the results. At this stage, the Data Scientist needs to choose the essential properties that will directly aid the prediction of the model.

Modeling comprises of quite a few tasks. For example, models can be trained to differentiate via classification, such as mails received as ‘Primary’ and ‘Promotion’ through logistic regressions. Forecasting is also possible through the use of linear regressions. Grouping data to comprehend the logic backing these sections is also an achievable feat. For instance, E-Commerce customers are grouped so that their behavior on a particular E-Commerce site can be understood. This is made possible with hierarchical clustering or with the aid of K-Means, and such clustering algorithms.

Prediction and regression are the main two devices used for classification and identification, forecasting values, and clustering groups.

There are actually various ways available in which you can model your data. Therefore, deciding on one particular method can be a very crucial step in the data science life cycle. There are mainly two steps involved in the evaluation of the model. They are Data drift analysis and model drift analysis. 

Data Drift Analysis

To put it simply, data drift basically refers to any changes in the input data. It is one of the most common phenomena in data science since based on the situation, it is inevitable that there will be some changes in your data. Analysis of the same is known as data drift analysis. The accuracy with which your chosen model will be able to handle this data drift ultimately plays a major role in your decision-making process. 

Model Drift Analysis

Apart from various machine learning techniques, you also have many sophisticated methods such as Page Hinkley, and Adaptive Windowing that you can use for discovering the data drift. You can also opt for incremental learning, wherein the model is exposed to the new data incrementally. 

Once you have chosen your model, it is now time to train the model. This is yet another crucial step. You can conduct the training in phases where the desired output can be generated by tuning the important parameters. During the production phase, the model is exposed to the actual data, and the production is then monitored. 

Once you have checked all these boxes, and the model has been properly trained with the actual data and the vital parameters, you can then deploy the model quite easily. It can be deployed as a web service, mobile application, or an embedded application on the edge. This is the step where the model gets exposed to the real world. 

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6. Interpreting Data

Interpreting data is the final and most important juncture of a Data Science Life Cycle. Interpretation of data and models is the last phase. Generalization ability is the crux of the power of any predictive model. The model explanation is dependent upon its capacity to generalize future data which is vague and unseen.

Data interpretation means the data presentation to the regular layman, someone who has no technical knowledge about data. Business questions posed at the beginning of the life cycle are answered in the form of delivered results. It is coupled along with the actionable insights discovered through the process of the Data Science Life Cycle.

Actionable insight is a crucial part of demonstrating how Data Science can furnish both predictive analytics and even prescriptive analytics. This allows one to know how to replicate a positive result and avoid a negative one. If you learn data science you will be able to understand Data Science Life Cycle properly.

Moreover, these findings need to be visualized appropriately. This is done by making sure the original corporate concerns back them. The biggest aspect of all of this is concisely representing all of this information, so that it is actually productive for the business concerned.

Hopefully, this has answered your question about the Data science life cycle has how many stages. When all these six different stages are followed properly, then the reports that are generated can actually play a crucial role in taking key decisions for your organization. They can not only result in business growth but also lead to better revenue generation as well. 

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Conclusion

To summarise, these are the five essential steps of a Data Science Life Cycle which every student of Data Science should be familiar with. However, it is not simply the basic data skills that get the job done. One of the most important skill sets to have is the ability to provide a lucid and actionable narrative.

The presentation of the data obtained and transformed must be succinct and clear enough for the audience to comprehend. Communication is the key to success here, as in most places. The heart of the Data Science Life Cycle is the interplay between the existing goals, data content, and analytical method.

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