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The importance of data science is very clear as it is termed as the sexiest job of the 21st century. Enterprises are deploying AI projects for multiple numerous across different industries. All data science project deployments are built on a clear understanding of the business problem with AI/ML algorithms applied to the problem, which leads to a data science model, addressing the business needs.
One thing to remember when building a data science business model is that nothing is perfect and it is all about trial and error. Data scientists constantly tweak algorithms and models to achieve the highest level of precision. Nonetheless, building a data science model is a long process with numerous steps. Here's how you can build an effective data science model.
Although this is not to be considered one of the steps of building a data science model, experts believe that if data scientists don't know the business problem, on what foundation will they be building a data science model? One should know what is the problem data scientists are trying to solve.
Understand the data science process model and the ultimate objective of building a data science business model. Further, establishing specific, quantifiable goals will help data scientists to measure the ROI from the data science project instead of just deploying it as a proof of concept that will be later kept aside.
Once data scientists know the problem they are trying to solve, the next step is to collect data. Data collection is gathering relevant data that includes both structured and unstructured data. Some well-known data repositories are Dataset Search Engines, Kaggle, NCBI, UCI ML Repository, etc. Data scientists make sure that they are collecting data that is relevant to the business problem, otherwise, most of the time goes into sorting data.
Once data scientists have relevant data, they need to shape that data in order to train the data science model. Preparing data consists of data cleansing, aggregation, labeling, transformation, etc. Procedures for preparing data involves
Remember that cleaning and preparing data is a time-consuming affair. But, it is also one of the important steps of building data science models. The amount of time spent cleaning data definitely gives notable results.
After cleaning data, data scientists have valuable and useful data for model building in data science. The next step is to identify patterns and trends in data. Tools like Micro strategy and Tableau help a lot at this stage. Data scientists have to build an intuitive dashboard and check for significant patterns in data.
Data scientists would know the driving factors of business problems. For example, if it is about pricing features, they would know all the details about it – whether the price is fluctuating, why, when, etc.
Data scientists have good quality data along with information on the trends and patterns in data, it's time to train the model with data by applying different algorithms and techniques. This involves model technique selection and application, model training, model hyperparameter setting and adjustment, model validation, gathering model development and testing, algorithm selection, and model optimization.
Data scientists should select the right algorithm taking into consideration data requirements. Further, they should also realize whether model explainability or interpretability is needed, test diverse model versions, etc. The model so developed can be tested for its functionality.
Model approval and evaluation during training is a significant phase assessing various metrics for deciding whether a data scientist has a successful supervised data science model. Model planning and evaluation is a crucial stage, since it manages the decision of learning strategy or model, and gives a performance measure of the quality of the eventually picked model. Techniques like ROC curve or cross-validation are used that perform great for generalizing the model output for new data. If the model is giving fruitful results, data scientists can go ahead and put it into production.
This phase means testing how well the model can perform in the real world. This step is also known as "operationalizing" the model. Data scientists should deploy the model and constantly measure its performance as well as alter different features to improve the overall performance of the model. Depending on the business requirements, model operationalization can vary from just generating a report to a more complex, multi-endpoint deployment. However, data scientists should ensure continuous improvements and iterations as technology capabilities as well as business requirements change quite often.
