A Quick Introduction To Data Science Pipeline And Its Importance

data science pipeline

The rising volume of organizational data and its importance in decision-making and strategic planning have encouraged firms to invest in the people and technology necessary to derive useful business insights from data assets. Many technologies and tools are often used in Data Science applications, and the data science pipeline is one.

Typically, a data science pipeline is a series of steps performed on raw data. The data science pipeline is what we call the series of steps that make up every data science project, from data collection to analysis and visualization. It will help you understand how each step in the data pipeline works and which tools you’ll use. 

What is a data science pipeline?

A data science pipeline is a set of data science tools/methods that should be followed while building a data-driven product. It is a process where data scientists use their expertise to understand complex information. They create a structured approach for 

  • Solving problems
  • Apply basic mathematical operations, and 
  • Design highly efficient algorithms and codes 

The above three actions combine to provide a platform for prediction. Furthermore, the data pipeline executes the tasks and provides you with results over time.

To simply put, Data Science Pipeline is a process in which data scientists:

  1. Collect the data they need to perform their analysis.
  2.  Clean the data to remove noise, outliers, and other sources of error.
  3.  Transform their data into a ready format for modeling and analysis.

For example, a company’s Sales Team wants realistic quarterly targets. The data science pipeline can collect consumer feedback, past purchase orders, industry trends, and more. Data analysis tools are then used to find trends and patterns. Teams can define data-driven sales targets and improve sales. 

One of the most important pieces of any data science pipeline is the ETL. It stands for Extract, Transform, and Load. It basically means it takes your data from all possible places, transforms it, and loads it into your infrastructure in a format that makes sense. After all, you can’t build predictive models with noisy or missing data.

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Importance of data science pipeline

  • Data science pipelines are essential to ensure that you don’t have any data errors or issues with how you’re handling data. If you have errors in your data, it could mean that something is wrong with the way you’re processing it—or, even more likely, your software isn’t working properly.
  • The best part of having a good data pipeline is that once you’ve got one, it’ll save you hours daily!
  • Using real-world data, businesses can utilize this method to find out answers to particular business problems and get actionable insights. All accessible datasets, both internal and external, are evaluated to locate this information.

Key features of data science pipeline: 

  • The ability to process massive data in real-time
  • Flexible and adaptable cloud computing
  • Having access to huge data and the capacity to self-serve
  • Eliminates bottlenecks and data silos that promote delays and waste
  • Amplify data for users
  • Isolated and self-contained data processing systems

How does a data science pipeline work?

The data science pipeline is divided into 5 phases: 

Obtaining information: 

This phase involves identifying and extracting useful data from the internet or internal/external databases. They are then transformed into useful formats (XML, .csv, etc.)

2. Cleansing the data: 

This is the most difficult and time-consuming stage. There are two phases in total:

  • Examining data which includes; Identifying errors, missing values, and corrupt records.
  • Cleaning data which means replacing or fixing errors/missing values

3. Data Exploration and modeling:

After the data has been meticulously cleaned, it can be utilized to discover patterns and values with data visualization tools like charts.

Data Modeling: Here, machine learning tools can be beneficial. We can build data models with the help of machine learning (ML). In a statistical sense, data models are nothing more than broad principles that can be applied to improve our organizational decision-making.

4. Data interpretation:

This step aims to discover insights and link them to your data findings. After that, you can then communicate your results in the form of charts, dashboards, or reports to leaders or team members. 

5. Data Revision 

New features will be introduced as the business evolves that might affect your existing models. As a result, regular reviews and updates are necessary from the perspectives of businesses and data scientists equally.

Though there are many tools and techniques for undertaking data science work, that does not mean that every data pipeline is the same. When creating a data pipeline for use in your workplace, it is important to note the dependencies between various components. Each organization will have its own unique pipeline based on its needs and focuses. Creating an encompassing pipeline that covers all aspects of your business analytics needs will help ensure success in your endeavors.

Benefits of data science pipeline:

The Data Science Pipeline has several advantages, including: 

  1. Improves the option to adapt to evolving business and customer demands.
  2. It Speeds up the Decision-Making Lifecycle
  3. Making it easy to have access to company and customer insights
  4. Facilitates and enables faster the process of data analysis 

Conclusion:

Overall, the data science pipeline is a very powerful and flexible tool. The data pipeline consists of multiple stages where important insights and decisions can be made. The later stages in the pipeline lead to a full-fledged actionable insight, where data scientists add the most value. It can be adapted to handle an enormous plethora of use cases. However, we must keep our goals in mind whenever we develop a pipeline and avoid letting automation drive us crazy. Because let’s be honest: no matter how good your pipeline is, there will always be things that drive you insane. 
The increase in workload is exceeding the rise in the number of data scientists. There are a lot of data scientists in corporations, but there aren’t enough favorable positions to support them. The problem might be that the skills and mindset expected for data science are rare among seasoned professionals. Thus, it is required for them to keep learning and upgrading their skills. Moreover, data science is a diverse field. Because of this, you can take many different paths to become a data analyst or data scientist. So, If you are considering a career in this exciting field or want to learn more, check out Learnbay’s data science course in Mumbai and become a certified data scientist!