The entire process of extracting useful information from unstructured data using ideas like statistical analysis, data analysis, machine learning techniques, data modelling, data preparation, etc., is known as data science. To gather more information about What is Data Science and its Lifecycle, Join Data Science Course in Chennai at FITA Academy, where our professional trainers will train you with real-time examples and case studies.
Lifecycle of Data Science:
Formulation of Business Model:
Any data science issue will begin with the formulation of a business issue. The challenges which generally resolved with knowledge obtained from a Data Science solution are explained by a business problem. You have sales data for a retail store going back a year.
This is a straightforward example of a business challenge. You must forecast the store’s sales over the next three months using machine learning techniques to help retailers to build an inventory that reduces the wastage of goods with shorter shelf life than other goods.
Data Extraction, Transformation, Loading:
Creating a data pipeline is the following data science life cycle phase. In this step, the pertinent data is taken from the source, translated into a machine-readable format, and then loaded into the programme or machine-learning pipeline to get things going.
For the scenario above, we will need data from the shop to help create an effective machine-learning model to estimate sales. In light of this, we would generate distinct data points that might or might not be influencing the sales for that specific store.
Data Preprocessing:
The magic happens in the third phase. We will produce relevant data using statistical analysis, exploratory data analysis, wrangling, and manipulation. Preprocessing is generally carried out to evaluate the many data points and create hypotheses explaining the various relationships between the many elements in the large data set instance. The data should be in a single time series format to forecast store sales. The series stationarity will be examined by hypothesis testing, and further calculations will reveal numerous trends, seasonality, and other related patterns in the data.
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Data Modelling:
Data Modelling, which includes Advanced machine learning techniques, is employed in this step to select particular features, convert features, standardise the data, normalise the data, etc. In the example above, you can build a model that will effectively produce a forecast for the specified months by selecting the best algorithms based on evidence from the phases above.
Using the time series forecasting approach for a business challenge where high dimensional data may be present. We will develop a forecasting model utilising an AR, MA, or ARIMA model and several dimensionality reduction approaches to predict several sales for the upcoming quarter.
Gathering Actionable Insight:
The last phase in the data science life cycle is getting insights from the problem above description. We derive conclusions and inferences from the procedure most effectively explains the business issue.
As an illustration, we may obtain the monthly or weekly sales for the following three months from the time series model mentioned above. The professionals will then be able to use these insights to develop a strategic plan to solve the current issue.
Solutions For a Business Model:
As mentioned above, getting insights from the problem denotes the last stage in the data science life cycle. We derive conclusions and results from the entire process that would most effectively explain the business issue.
For instance, we can obtain the upcoming three monthly or weekly sales from the time mentioned earlier series model. Using these insights, the experts will develop a strategic plan to solve the current issue.
Ending Note:
In this blog, you would have understood What data science is and its Lifecycle. Join Data Science Courses in Bangalore to learn more about Data Science.