Life Cycle of Data Science | Intellipaat

The steps in the data science lifecycle are listed below:

• Making a Business Problem

Any problem with data science will start with the formulation of a business problem. A business problem explains the issues that could be resolved with the knowledge found in a successful Data Science solution. For a retail store, you have sales information going back a year. This is a simple illustration of a business issue. Using machine learning techniques, you must predict or forecast the store's sales over the upcoming three months in order to assist the retailer in building an inventory that will cut down on the wastage of products with shorter shelf lives than other products.

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• Extraction, transformation, and loading of data

The next stage in the data science life cycle is the development of a data pipeline. In order to start the machine learning pipeline or programme, the relevant data must first be extracted from the source and converted into a machine-readable format. In order to forecast the sales for the aforementioned example, we will need information from the store that will be useful in building an efficient machine learning model. As a result, we would produce various data points that might or might not be affecting the sales for that particular store.

• Preprocessing of Data

The third step is where the magic happens. By using statistical analysis, exploratory analysis, data wrangling, and data manipulation, we will produce meaningful data. Preprocessing is done to assess the various data points and formulate hypotheses that most effectively explain the relationship between the various data features. For instance, the data must be in a time series format in order to forecast store sales. Through hypothesis testing, the series' stationarity will be determined. Subsequent calculations will then show the data's various trends, seasonality, and other relationship patterns.

• Data Modeling

In this step, advanced machine learning techniques are used to choose features, transform features, normalise the data, and more. By deciding on the best algorithms in light of the results from the aforementioned steps, you can create a model that will successfully generate a forecast for the months that are mentioned in the example above. We can apply the time series forecasting method, for example, to a business problem where there may be high dimensional data. To forecast the sales for the upcoming quarter, we will create a forecasting model using an AR, MA, or ARIMA model and various dimensionality reduction techniques.

• Gathering Actionable Insights

  Getting insights from the aforementioned problem statement is the last stage of the data science life cycle. From the entire process, we derive conclusions and findings that would most effectively explain the business issue. For instance, we can obtain the upcoming three months' worth of monthly or weekly sales from the time series model mentioned above. The experts will then be able to develop a strategy plan using these insights to solve the current issue.

• Solutions For the Business Problem

  The only things that will solve the business problem are practical insights supported by data are actionable insights. As an illustration, our forecast based on the time series model will provide a reliable estimation of the store sales over the following three months. The store can plan its inventory using those insights to minimise the loss of perishable goods.