Data Science Process: Understanding, Data Collection, Modeling, Deployment & Verification

Data Science projects in the industry are usually followed as a well-defined lifecycle that adds structure to the project & defines clear goals for each step. There are many such methodologies available like CRISP-DM, OSEMN, TDSP, etc. There are multiple stages in a Data Science Process pertaining to specific tasks that the different members of a team perform.

Whenever a Data Science problem comes in from the client, it needs to be solved and produced to the client in a structured way. This structure makes sure that the complete process goes on seamlessly as it involves multiple people working on their specific roles such as Solution Architect, Project Manager, Product Lead, Data Engineer, Data Scientist, DevOps Lead, etc. Following a Data Science Process also makes sure the quality of the end product is good and the projects are completed on-time.

By the end of this tutorial, you will know the following:

  • Business Understanding
  • Data Collection
  • Modeling
  • Deployment
  • Client Validation

Business Understanding

Having knowledge of business and data is of utmost importance. We need to decide what targets we need to predict in order to solve the problem at hand. We also need to understand what all sources can we get the data from and if new sources need to be built. 

The model targets can be house prices, customer age, sales forecast, etc. These targets need to be decided upon by working with the client who has complete knowledge of their product and problem. The second most important task is to know what type of prediction on the target is.

Whether it is Regression or Classification or Clustering or even recommendation. The roles of the members need to be decided and also what all and how many people will be needed to complete the project. Metrics for success are also decided to make sure the solution produces results that are at least acceptable.

The data sources need to be identified which can provide the data which is needed to predict the targets decided above. There can also be a need to build pipelines to gather data from specific sources which can be an important factor for the success of the project.

Data Collection

Once the data is identified, next we need systems to effectively ingest the data and use it for further processing and exploration by setting up pipelines. The first step is to identify the source type. If it is on-premise or on-cloud. We need to ingest this data into the analytic environment where we will be doing further processes on it.

Once the data is ingested, we move on to the most crucial step of the Data Science Process which is Exploratory Data Analysis (EDA). EDA is the process of analyzing and visualizing the data to see what all formatting issues and missing data are there.

All the discrepancies need to be normalized before proceeding with the exploration of data to find out patterns and other relevant information. This is an iterative process and also includes plotting various types of charts and graphs to see relations among the features and of the features with the target. 

Pipelines need to be set up to regularly stream new data into your environment and update the existing databases. Before setting up pipelines, other factors need to be checked. Such as whether the data has to be streamed batch-wise or online, whether it will be high frequency or low frequency.

Modelling & Evaluation

The modeling process is the core stage where Machine Learning takes place. The right set of features need to be decided and the model trained on them using the right algorithms. The trained model then needs to be evaluated to check its efficiency and performance on real data.

The first step is called Feature Engineering where we use the knowledge from the previous stage to determine the important features that make our model perform better. Feature engineering is the process of transforming features into new forms and even combining features to form new features.

It has to be carefully done in order to avoid using too many features which may deteriorate the performance rather than improve. Comparing the metrics if each model can help decide this factor along with feature importances with respect to the target.

Once the feature set is ready, the model needs to be trained on multiple types of algorithms to see which one performs the best. This is also called spot-checking algorithms. The best performing algorithms are then taken further to tune their parameters for even better performance. Metrics are compared for each algorithm and each parameter configuration to determine which model is the best of all.

Deployment

The model that is finalized after the previous stage now needs to be deployed in the production environment to become usable and test on real data. The model needs to be operationalized either in form of Mobile/Web Applications or dashboards or internal company software. 

The models can either be deployed on cloud (AWS, GCP, Azure) or on-premise servers depending upon the load expected and the applications. The model performance needs to be monitored continuously to make sure all issues are prevented.

The model also needs to be retrained on new data whenever it comes in via the pipelines set in an earlier stage. This retraining can be either offline or online. In offline mode, the application is taken down, the model is retrained, and then redeployed on the server. 

Different types of web frameworks are used to develop the backend application which takes in the data from the front end application and feeds it to the model on the server. This API then sends back the predictions from the model back to the front end application. Some examples of web frameworks are Flask, Django, and FastAPI.

Client Validation

This is the final stage of a Data Science Process where the project is finally handed over to the client for their use. The client has to be walked through the application, its details, and its parameters. It may also include an exit report which contains all the technical aspects of the model and its evaluation parameters. The client needs to confirm the acceptance of the performance and accuracy achieved by the model.

The most important point that has to be kept in mind is that the client or the customer might not have the technical knowledge of Data Science. Therefore, it is the duty of the team to provide them with all the details in a way and language which can be comprehended by the client easily.

Before You Go

The Data Science Process varies from one organization to another but can be generalized in the 5 main stages that we discussed. There can be more stages in between these stages to account for more specific tasks like Data Cleaning and reporting. Overall, any Data Science project must take care of these 5 stages and make sure to adhere to them for all the projects. Following this process is a major step in ensuring the success of all Data Science projects.

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