Importance of Data Science
Data is a valuable asset for many industries because it allows them to make more informed business decisions. Data science is capable of transforming raw data into meaningful insights.
An expert data scientist can extract meaningful information from whatever data is available to them. They steer organizations in the right direction by making sound data-driven decisions and making recommendations. We discuss the life cycle of Data Science.
Developing a Business Problem
Any data science problem will begin with the formulation of a business problem. A business problem describes the issues that can be resolved using insights obtained from an effective Data Science solution. A simple example of a business problem is if you have sales data from the previous year for a retail store. You must predict or forecast sales for the next three months using machine learning approaches. This will assist the store in creating an inventory that will aid in reducing the wastage of products that have a shorter shelf life than other products
Extraction, Transformation, and Loading of Data
The following step in the data science life cycle is to build a data pipeline in which relevant data is extracted from the source and transformed into a machine-readable format before being loaded into the program or machine learning pipeline to get things going.
Data Preparation
The magic happens in the third step. We will generate meaningful data using statistical analysis, exploratory data analysis, data wrangling, and manipulation. Preprocessing is performed to evaluate the various data points and develop hypotheses that best explain the relationship between the various features in the data.
Data Modelling
This step includes advanced machine learning concepts that will be used for feature selection, feature transformation, data standardization, data normalization, and other purposes. Choosing the best algorithms based on evidence from the preceding steps will assist you in creating a model that will efficiently create a forecast for the months.
Obtaining Useful Insights
Gathering insights from the problem statement is the final step in the data science life cycle. We draw conclusions and findings from the entire process to best explain the business problem.
Solutions to Business Issues
Business problem solutions are nothing more than actionable insights that will solve the problem using evidence-based information.
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