What Is A Data Science Lifecycle?

In order to create a successful business model, it's critical to first recognize the client's business challenges. Assume he wants to forecast his retail business's client attrition rate.

You should first learn about his company, his requirements, business challenges, and what business goals he aims to achieve from the forecast. In such circumstances, it is critical to seek advice from domain specialists and, ultimately, to comprehend the system's core flaws. A Business Analyst is in charge of acquiring the necessary information from the customer and delivering it to the data scientist team for further analysis. Even the tiniest mistake in identifying the problem and comprehending the need can have a significant impact on the project, thus it must be done with extreme care.

We go on to the following step, data collecting, after asking the necessary questions of the company's stakeholders or clients.

Here's where we'll need the correct metrics:

• KPI (Key Performance Indicator)

Assess your performance in relation to major business goals. Businesses use this to track their progress toward their goals and assess their overall health. It's critical to agree on the correct set of KPIs for each company and avoid the temptation of chasing vanity metrics that seem good but aren't useful.

• SLA (Service Level Agreement)

A Service-Degree Agreement (SLA) specifies the level of performance that a client expects from a provider, as well as the metrics that are used to assess that service. For example, suppose you're building a sophisticated pipeline with an SLA of 100 milliseconds and a tonne of transactions to process.

This initial phase should include the following:

• Clearly state the problem that needs to be solved and why it has to be solved.
• Encourage everyone engaged to work toward this goal.
• Define the project's potential benefit. Identify the project's dangers, including ethical issues.
• Determine who the important stakeholders are.
• Align the data science team with the stakeholders.
• High-level knowledge about the subject of research
• Determine how many resources (data science teams and infrastructure) you'll require.
• Create a high-level, flexible project strategy and explain it.
• Determine the sort of problem that has to be solved*
• Obtain support for the initiative.

2) Data Gathering

After we've clarified the issue statement, we'll need to gather relevant data in order to split the problem down into smaller parts. The data science project begins with the identification of multiple data sources, which might include web server logs, social media postings, data from digital libraries like the US Census databases, data obtained via APIs, web scraping, or information already available in an excel spreadsheet.

Data collecting comprises gathering information from both known internal and external sources that might help solve the problem. The data analyst team is usually in charge of gathering the information.

To acquire the necessary outcomes, they must find out how to properly gather and collect data.

The data can be obtained in two ways:

• Using Python to scrape the web
• Using APIs from outside parties to extract data

3) Preparation of data

We must proceed to data preparation after acquiring data from appropriate sources. This stage, unlike the previous step, aids with the comprehension of the data and prepares it for subsequent analysis. This step is often referred to as Data Wrangling or Data Cleaning.

Selecting relevant data, merging it by mixing data sets, cleaning it, dealing with missing values by either eliminating them or imputing them with relevant data, dealing with inaccurate data by removing it, and checking for and dealing with outliers are all procedures involved.

You may build new data and extract new features from current data by utilizing feature selection. Remove any extraneous columns or functions from the data and format it according to the required structure.

The most time-consuming phase, accounting for up to 90% of the total project length, is data preparation, which is also the most important stage throughout the whole life cycle.

At this stage, exploratory data analysis (EDA) is crucial since summarising clean data allows you to identify the data's structure, outliers, anomalies, and patterns. These findings can assist in the selection of the best collection of characteristics, the development of a model-creation algorithm, and the building of the model.

4) Modeling of data

In most situations of data analysis, data modeling is considered the most crucial step. A data scientist first builds a baseline model that has been successful in the past and then customizes it to fit the needs of the company. We use the prepared data as the input in this data modeling procedure, and we strive to produce the required model output with it. Whether the challenge is a regression, classification, or clustering-based problem, we first prefer to pick the suitable type of model that will be used to obtain results. We determine the optimum method for the machine learning model based on the type of data we get. After that, we must modify the hyperparameters of the predictive models to get a desirable result.

Finally, we assess the model by determining its correctness and relevance. In addition to this endeavor, we must ensure that there is a proper balance of specificity and generalizability and that the model generated is unbiased.

5) Model evaluation

Here, the model is assessed to see if it is ready for deployment. The model is tested on previously unknown data and assessed using a carefully developed set of assessment measures. In addition, we must ensure that the model is accurate. If the evaluation does not yield a satisfactory outcome, we must repeat the entire modeling approach until the desired level of metrics is reached. Any data science solution, such as a machine learning model, must develop, be able to improve itself with fresh data, and adapt to a new assessment measure. We can create several models for a given occurrence, but many of them will be flawed.

• Data Drift Analysis: We compare the incoming data distribution from the actual world with the one that has been scored, and if there is a difference, we must choose a threshold.

If the discrepancy is significant, the incoming data will be skewed. In this scenario, we'll have to go back and look at the differences again.

• Model Drift Analysis: we compare the difference between the score of the real-world model distribution and the scored model distribution, and this information is fed into a system that reports and outputs to the business user on a regular basis.

It would state, for example, how many consumers the company gained, how much money company made in dollars, what this means in terms of income streams, and so on.

Data drift can be caused by a variety of factors, including:

• Alters in upstream processes, such as the replacement of a sensor that changes the units of measurement, or a damaged sensor that always reads 0.
• A natural variation in the data, such as the change in mean temperature with the seasons.
• Changes in the nature of the relationship between characteristics.
• What can you do to help?
• Erroneous labeling should be removed.
• Model retraining/calibration
• Early detection and correction of upstream data issues

In scenarios when your machine learning model(s) become stale due to concept or data drift, as seen in the graph below on the left, we may prevent model staleness over time by refreshing the model over time.

6) Deployment of the model

We need to be sure we've chosen the proper answer after a thorough examination before we deploy a particular type of model. It's then distributed in the specified channel and format. Deployment of a particular type of model is, of course, the final phase in the data science project life cycle. To avoid unintended mistakes, please exercise additional caution before completing each phase in the life cycle.

For example, if you use the incorrect machine learning method for data modeling, you will not attain the needed accuracy and will have a tough time obtaining stakeholder support for the project. If your data isn't adequately cleaned, you'll have to deal with missing values or noise in the dataset afterward.

As a result, comprehensive testing will be required at each phase of deploying a type of model to monitor model performance and to ensure that it is effectively deployed and approved in the actual world as an ideal use case. All of the processes outlined above are equally relevant to both novice and experienced data scientists. As a newbie, your task is to first understand the technique, then practice and launch smaller analytics projects such as the false news detector and the Titanic dataset.

Your first task is to thoroughly complete all elements of the data science life cycle, and once you have completed the process and deployment, you are ready to take the next step toward a career in this sector.

Python and R are the two most extensively used languages in data science applications. An application developer should be comfortable coding in these languages in addition to having a clear understanding of the process. You must be proficient in all areas, from process comprehension to programming language expertise.

Summing up

Choose the optimal solution for your problem by analyzing specific use cases and selecting the appropriate metrics, starting with the business perspective: KPIs and SLAs will aid in determining the best path from development to production deployment of machine learning or artificial intelligence models for predictive models, as choosing the way to deploy predictive models into the production is a challenging task. 

Machine learning is not the answer to every problem; if you can handle a problem in a more straightforward manner without using Ml, you should do so; the simpler the solution, the better.

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