The data science lifecycle revolves around using machine learning and different analytical strategies to produce insights and predictions from information in order to achieve a commercial enterprise objective. The complete process includes a number of steps like data cleaning, preparation, modeling, model evaluation, etc. It can take quite a few months to complete a lengthy procedure. It's really important to have a generic structure to follow for any problem you're trying to solve. The globally-recognized structure for solving analytical problems is known as the Cross Industry Standard Process for Data Mining, or CRISP-DM framework.

What is the Need for Data Science?

Data used to be less accessible and generally came in a less structured form. This made it difficult to save and process efficiently. However, Business Intelligence tools have made it much easier to access and process data. Today, we deal with large amounts of data. For example, 3.0 quintals bytes of records are produced every day. This lays a foundation for the explosion of data. According to recent research, it is estimated that 1.9 MB of data and records are created every second by a single individual. Any organization faces a big challenge when dealing with massive amounts of data generated every second. To handle and evaluate this data, we need some very powerful, complex algorithms and technologies. This is where data science comes in.

What is a Data Science Life Cycle?

Any concept taken into account, there always is a life cycle. Most data science projects go through the same basic life cycle of steps, though every project and team is different so every life cycle is unique. Here's a look at the typical data science life cycle. Some data science life cycles focus on just the data, modeling, and assessment steps. The Data Science Life Cycle is simply a series of activities that you must repeatedly follow in order to finish work and provide it to your customers. Even though every company's Data Science Life Cycle will be a little bit different, the data science projects and teams participating in installing and upgrading the database will vary. Others are more comprehensive and include business understanding and deployment.

And the next one we'll go through is even more comprehensive and includes operations. It also emphasizes agility more than other life cycles.

There are five steps in the Life Cycle:

i) Problem Definition

ii) Gathering of Data

iii) Cleaning of Data

iv) Deployment and Enhancements

v) Data Science Ops

i) Problem Definition

It's important to understand the problem you're trying to solve at the beginning of any data science project. If the customer has made a clear request, this is easy to do. However, if the customer has asked you to solve a very broad problem, you'll need to identify clear objectives and concrete difficulties.

ii) Gathering of Data

The second step is to collect useful information from available data sources. It's important to collect all relevant data in order to solve the problem. Speaking with the company's team can help you learn more about the data that's available, what data can be used to solve the problem, and other details. The data should be described, along with their type, relevancy, and organization. Visual charts can be used to investigate the data.

iii) Cleaning of Data

The next step is to clean the data, which refers to the scrubbing and filtering of data.This procedure requires converting data into a different format, which is necessary for processing and analyzing information. If the files are web locked, then it is also needed to filter the lines of these files. Moreover, cleaning data also constitutes withdrawing and replacing values.If data sets are missing, they must be replaced carefully so they don't look like non-values.

iv) Data Exploration

Now that we have the data, we need to examine it before we can use it. In business settings, it's up to the Data Scientist to transform the available data into something that can be used in a corporate setting. Before we jump into analyzing our data, we need to first explore it and understand its characteristics. This is important because different data types (e.g., nominal, ordinal, numerical, categorical) require different approaches.

v) Modeling of Data

Modeling can involve a few different tasks. For example, you can train models to differentiate between things like ‘Primary’ and ‘Promotion’ emails through logistic regressions. Forecasting is also possible through the use of linear regressions. This method can help you predict future events by looking at past trends. For instance, you can group E-Commerce customers so that you can better understand their behavior on a particular site.

vi) Interpreting of Data

Interpreting data means presenting it in a way that is accessible to people who don't have any technical background in data. Business questions that are posed at the beginning of a project are answered through the results that are delivered. This is combined with the actionable insights that are discovered through the Data Science Life Cycle.

Conclusion

In this article, the cycle of Data Science has been explained along with the definitions of Data Science. A candidate must have an in-depth knowledge of Data Science to fetch a role of Machine Learning Engineer or a Data Scientist. How does a candidate get equipped with the concepts of Data Science? At Skillslash, candidates are educated with the concepts of Data Science, and make them industry ready. Skillslash also offers Data Science Course In Bangalore, Data analytics course in Hyderabad and Best Full Stack Developer Course In Hyderabad. They are made to work on live projects, and offer a guaranteed job-referral program. Get in touch with the student support team to know more.