In layman terms, data science is the art of analyzing available data and converting it into usable insights. However, as the application of data science grows, it becomes evident that this definition may not be as simple as it sounds. With the advancements in technology and computational analytical processes, the received data needs to be sorted, polished and presented in the right manner, for businesses to make the best use of it. Data science lifecycle, thus, is nothing but a proper step-by-step guidance that helps organizations to do this. It encompasses the journey that data undergoes, right from the problem-statement, to its final presentation.
In a broad sense, there are six different stages of data science lifecycle, though there might be minor differences observed from one company to another. Based on different aspects, one may consider five or seven steps, but the crux of the process remains the same.
Understanding the Problem
Just like any other process, data science lifecycle starts with a ‘why?’. Data scientists need to be inquisitive in nature and their first and foremost priority is asking the right questions, so that they can formulate their plan of action. Understanding what the exact business problem is and having a rock-solid base of how analysis needs to be carried out becomes stage one. This also enables data scientists to clearly define the scope of the project and its outcomes.
Data Mining and Acquisition
Once the scope of the project and objectives are fixed, the next stage is to start gathering the data from different sources. This can be through interviews, focus groups, or through use of different tools and AI. It may include techniques like webscraping, cold-calling, database queries, or buying readily available reports. Different data collection methods can be used, based on the data requirement and the target audience. However, one thing to note here is that the end result of the data science lifecycle will be dependent on the quality of data that is collected.
Data Cleaning and Preparation
Once all the raw data is collected, the most tedious task is to clean the data, and preparing it for use. Data scientists usually call this process as ‘feature engineering’ as well, which includes converting the numbers and variables into something that fits the chosen algorithm. Many times, one might have to go back to the data mining stage to find out missing variables and do more data mining, wherever they find that important links are missing. This entire process is the most time-consuming part of the data science lifecycle, taking almost 75 to 80% of the entire process.
Data Exploration
Now that the entire data is segregated and sorted out, it can be used to get started on analysis. In the data exploration stage, brainstorming is carried out on the accumulated data, to understand recurring patterns and bias. Potentially important variables and relationships across different factors can be identified and used to study the outcomes.
Predictive Modelling and Evaluation
Based on the outcomes of the data exploration stage, different types of predictive modelling is carried out, using machine learning and forecasting techniques. Again, the quality of data that is got from the previous stage determines the accuracy of the predictive models. A trusted way of getting more accurate results is by testing several models and the combined outputs of the same, in an iterative manner.
Data Visualization
The last stage of the data science lifecycle includes visualization, or presenting your findings and forecasting in the most simplified manner. Different tools and formats are used to showcase the findings, in a well-segregated manner, so that the end-user of the data can easily make sense of it.
Based on your research, there will be newer questions that arise out of the findings and that takes you back to the first stage of understanding and identifying a new problem. It may also happen that different parameters can change during the timeline of the project and data-scientists may have to revisit certain stages to accommodate these unforeseen changes in their findings.
Though this process might seem a tedious one, the final results of following this data science lifecycle immensely improves the accuracy of the results and helps you to optimize the use of your data.
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