Life cycle of Data Science | Complete step-by-step guide
                                              Part 1

The Data Science Lifecycle is centred on the application of machine learning and various analytical methodologies to extract insights and predictions from data to achieve a commercial company goal. A lot of processes are included in the complete method, including data cleaning, preparation, modelling, model evaluation, and so on. It is a time-consuming technique that could take many months to finish. As a result, having a generic structure to follow for each problem is critical. A Cross Industry Standard Process for Data Mining, or CRISP-DM framework, is a widely mentioned design for solving any analytical problem.

Let us first examine why Data Science is required.

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Previously, data was significantly less abundant and accessible in a well-structured form, which we could quickly and readily put in Excel sheets, and data can now be rapidly examined with the help of Business Intelligence tools. However, we are now dealing with vast amounts of data, with around 3.0 quintals bytes of records being produced each day, culminating in a record and data explosion. Dealing with such a tremendous amount of data generated every second is a huge task for any firm. We needed really sophisticated, complex algorithms and tools to handle and evaluate this data, which is where data science comes in.

The following are some of the key reasons for utilising data science technology:
  • It aids in the conversion of substantial amounts of uncooked and unstructured data into meaningful insights.
  • It can help with specific forecasts such as surveys, elections, and so forth.
  • It also aids in the automation of transportation, such as the development of a self-driving automobile, which we might argue is the future of transportation.
  • Companies are changing their focus to data science and adopting this technology. Amazon, Netflix, and other companies that deal with substantial amounts of data use information science techniques to improve the customer experience.
The lifecycle of Data Science
  1. Business Understanding: The organization aim is at the centre of the entire cycle. What will you do if you do not have a specific problem anymore? Because the final purpose of the study will be to comprehend the business goal thoroughly, it is important to do so. Only after we have a desirable perspective we can design a precise evaluation goal that is in line with the enterprise goal. You must decide whether the consumer wants to reduce savings loss or prefers to estimate the rate of a commodity, for example.
  2. Data Understanding: After gaining an understanding of the enterprise, the next stage is to gain a comprehension of the data. It is a list of all the data that can be accessed. Here, you must work closely with the business group, since they are aware about what information is available, what facts should be used for this business challenge, and other related details. This stage entails identifying the data, its structure, its significance, and the type of records it contains. Graphical charts can be used to examine the data. Essentially, collecting any facts about the information that you can obtain by simply browsing the data.
  3. Preparation of Data: The data preparation stage follows. This includes actions such as selecting relevant data, integrating the data by merging data sets, cleaning it, addressing missing values by either deleting or imputing them, treating erroneous data by dropping it, and testing for outliers with box plots and dealing with them. Creating new data and deriving new elements from existing data. Format the data according to your preferences, removing any unnecessary sections and elements. The most time-consuming, but most important, step in the entire existence cycle is data preparation. Your model will be as accurate as the information you provide.
  4. Exploratory Data Analysis:Before building the true model, this step entails acquiring a general idea of the response and the factors that influence it. Bar graphs are used to visualise the distribution of data within different variables of a character. Relationships between distinctive features are recorded through graphical representations such as scatter plots and warmth maps. Many data visualisation methodologies are widely utilised to identify each characteristic separately and in combination with other characteristics.
  5. Data Modeling:The coronary heart of data analysis is data modelling. A model takes organised data as an input and outputs the desired result. Whether the problem is a classification problem, a regression challenge, or a clustering problem, this stage involves picking the appropriate model.Following agreeing on the framework and the amount of algorithms inside that family, we must hand pick the algorithms to implement and enforce. We also need to ensure that there is a good balance between overall performance and generalizability. We do not want the model to study the data and perform poorly on new data any longer.
  6. Model Evaluation: Here the model is examined for deciding if it is geared up to be deployed. The model is tested on previously unseen data and assessed using a carefully developed set of assessment measures. We also need to make sure that the model is accurate. If the evaluation does not yield a satisfactory outcome, we must repeat the entire modelling 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 evaluation measure, just like a human. We can create multiple models for a given occurrence, but many of them will be flawed.
  7. Model Deployment: After a thorough evaluation, the model is finally implemented in the selected structure and channel. The data science life cycle ends with this step. Each phase in the above-mentioned data science life cycle must be carefully considered. If one step is done incorrectly, it will influence the next stage, and the entire effort will be wasted. For example, if data is not gathered, you will lose records and will not be able to design an ideal model. The model will no longer work if the data is not cleansed. If the model is not correctly analyzed, it will fail in the physical world.

In the first part of our discussion about data science life cycles, we talked about the initial steps. We will continue with a comprehensive guide to data science in the following section of this topic.