Data Science Methodology

In a data-driven world, the data science methodology plays a vital role in gaining insight and making informed decisions. It’s the step-by-step guide to transforming raw data into valuable information, and following a structured framework allows for consistency and best practice on all requirements.

Let’s take a look at it…

The data science methodology is described as iterative, meaning it’s a repetitive and evolving process that allows for reviews and changes along the way to achieve a desired outcome.

There are 10 focal points in this methodology…

Business Understanding

The first step is to develop a comprehensive understand of the business objectives and the specific problem at hand. This involves engaging with stakeholders and business leaders to gain insight into the context, goals, and constraints of the project. This thorough understanding ensures the following steps align with the business and that maximum value can be delivered.

Analytical Approach

Once the problem has been defined, there are 4 approaches that can be applied to tackle the challenge…

1. Descriptive: The descriptive approach is for understanding historical through data trends, patterns, and behaviours. What has happened?
2. Diagnostic: The diagnostic approach is for identifying the source of a problem. Why has this happened?
3. Predictive: The predictive approached is for understanding current and historical data to make assumptions on future data. What could happen?
4. Prescriptive: The prescriptive approach is fixing a problem that has already occurred. How do we fix it?

Requirements

During this stage, the scientists will define the specific requirements and objectives for the project. This includes defining the scope of the project, establishing KPI’s against which success will be measured, and determine the resources needed to address the problem efficiently. These requirements build a solid foundation on which the rest of the project will be built.

Collection

The collection point is the process of gathering all the required data from a variety of source through databases, API’s, and external datasets. An evaluation of the data will need to be undertaken to ensure the data is complete, accurate, and representative of the problem at hand. This step also requires extraction, transformation, and loading (ETL) to clean and pre-process data, as well as address missing values and inconsistencies.

Preparation

Preparation is a vital step as it means transforming raw data into a suitable format for further analysis. By cleaning and handling inconsistencies, it ensures data quality and enables the data to be worked with for modelling.

Modelling

Machine learning algorithms and statistical techniques are applied to the prepared data to build models to solve the problem. This step involves training, testing, and validating the models to determine which returns the highest accuracy and can be applied as a solution. These models can range from decision trees and linear regression to more complex deep learning models dependent on what the problem is and what resources are available.

Deployment

Once the models have been evaluated thoroughly, the next step is to deploy them into a product environment where they will assist in decision-making or incorporate into existing systems. Delivery of these models are done in collaboration with stakeholders and engineering teams to ensure a seamless integration.

Feedback

This is the final step in the methodology. I mentioned earlier that this is an iterative process which means that feedback is vital in ensuring maximum value. Once in production, feedback will be given as to how the models have answered the original question and how accurate they are. Feedback is taken into consideration and used to make continuous adjustments and improvements.

Implementing a methodology such as this not only provides a structured guide into how a problem should be approach, but it also provides an organisation with a plethora of benefits that contribute to the success of any data science project including resource allocation, alignment with objectives, consistency, reproducibility, risk mitigation, and continuous improvement.

In conclusion, adopting a methodology in best practice enables an organisation unlock the power of its data to maximise project success and drive business value by enhancing decision-making, gaining a competitive advantage, and achieving business goals.