[Spectator@simileoluwa]

Questions are very important and an integral part of Data Science, they are the ingredients that shape what direction a Data Science project embarks on. The extent to which data can be used is limited majorly by the kinds of questions asked such that the more questions you ask, the more insights you find. It doesn’t mean that you will always find answers to all questions, however, you will always find new patterns, trends arising. Asking the right question is how data yields knowledge hidden, and this knowledge embodies the potential to transform your business.

“Good Data Science is more about the questions you pose of the data rather than data munging and analysis.” — Riley Newman

The Data Science Pipeline can be regarded as procedures one embarks in search of the answers to the proposed questions which gives insight into a business process. Data Science is not just about modeling: building some machine or deep learning model, rather it is a combination of various procedures that are crucial to the successful integration of the resulting model into a business model. The Data Science Pipeline answers the question of how to undergo a Data Science project from the beginning to the end which the successful deployment of a model. Data Science Pipeline refers to the sequence of processes and analytical procedures steps applied to data for a specific purpose. They’re valuable underway tasks, and they can likewise help address repetitive business problems, to save money on configuration time and coding. For example, one could automate the procedures of data preprocessing and modeling on a dataset that is pulled every week for real-time insights.

There are several defined processes (Pipelines) a Data Science projects undertake from beginning to completion. Before discussing these processes, we will discuss briefly the properties of an ideal Pipeline:

1. Low Latency: This means that data from any source should be queried easily by the expert within seconds or minutes and exported to the collection point. This is to address business processes that involve real-time knowledge e.g. The Stock Market.
2. Scalability: A Data Pipeline must be able to retain data from products, especially when the product usage widens. This involves the ability to retain millions to billions of data generated from usage.
3. Collaborative Querying: A robust pipeline should have should be flexible enough for both long-running batch queries and lighter queries enabling experts to discover tables and comprehend the data schema.
4. Versioning: One should be able to make customizations and performance optimization procedures without damaging the framework.
5. Monitoring: Data tracking and monitoring are important to check if the data is dispatched properly.
6. Testing: After a successful implementation of a Data Science Pipeline, there must be a testing procedure that enables how well the product performs, however, the data gotten from the test processes shouldn’t end up in your data warehouse.

Since we have examined the properties of a Data Science Pipeline, we now begin to see the various components of a Data Science Pipeline:

1. Understanding the Business Problem: The essence of Data Science in its entirety is to solve a problem using Data, therefore, understanding the problem we’re trying to solve is very important. It is the understanding of the business problem that guides every other procedure in the Pipeline. It is the expert’s responsibility to dig in for information from the clients, consumers to gain the domain knowledge for asking the right questions. This may involve slicing the problem into smaller pieces that can be tackled more effectively.
2. Data Acquisition: Once the domain knowledge has been properly formed, it helps to guide the data collection process from a database. Data is usually stored in various places such as relational databases, flat files, websites, documents, etc. The Data Acquisition process is extremely important such that if the right data is not acquired, even though we ask the right questions, it would be impossible to gain insights. It is more like looking for orange on a mango tree. This involves using tools such as SQL, Programming languages such as Python, R, etc. for extracting the right information
3. Exploration and understanding: Another procedure required is to take the necessary steps in understanding the data that you are about to use and how it was collected; this often requires significant exploration.
4. Data Wrangling and Manipulation: This phase is usually the most tedious procedure in any Data Science workflow. Most of the time data come with a lot of anomalies like outliers, missing parameters, duplicate values, unnecessary features, etc. This is where the Domain knowledge comes into a critical play to understand the effects of any feature. This helps to decide what to leave, add or remove, it, therefore, turns out to be significant that we do a cleanup exercise and take just the data which is imperative to the issue be sought because the outcomes and yield of your AI model are just comparable to what you put into it.
5. Exploratory Data Analysis: Using majorly visual methods and some statistical components, this process in the Pipeline attempts to uncover patterns in a dataset. At this phase, the dataset starts to reveal secrets that will guide further questions. Exploratory Data Analysis is regarded as an art and thus, there is no one-size-fits-all.
6. Modeling: This is where Machine or Deep Learning algorithms are applied. Here we attempt to apply various models such as Linear Regression, Neural Networks, Random Forest Classifier, etc. Usually, the best performing model is chosen. This part of the Pipeline is usually fun as the expert uses various tools for in-depth analytics and solve with predictive algorithms problems related to prediction and classification.’
7. Interpreting/Communication: After every procedure has been implemented results must be presented to stakeholders to ensure that proper measures are implemented to improve the business enterprise. All findings must be broken down for comprehension of the stakeholders, else all efforts may be rendered unfruitful if the key decision-makers do not understand the essence of findings and how it affects the business process. Tools such as Power BI, Tableau which are considered Business Intelligence Tools are key to dashboarding of results for appealing visualization and dashboarding of Key Performance Indicators.
8. Model Updating: Once a Business Model has been successfully built and deployed, this doesn’t imply that one is completely done. There is a need to make updates on the model once in a while as there are certain conditions and external factors that may interfere with the model. For example, a transport model must take into account unusual spikes and hikes in fuel prices, weather, etc. Thus, this renders a need to tweak the model sometimes.

Conclusion

We have discussed the properties of a good Data Science Pipeline and its components. These steps should not be overlooked as they are very crucial to the successful implementation of Data Science in a Business Model. The goal of any business is to increase profit and reduce running costs. If Data Science will be a means of achieving this kind of effective business system, then the processes must be strictly adhered to. Each of the above-discussed components allows flexibility and improvisations in their usage that enables a successful Data Science Project.

[Author]

Posts