The increasing volume and complexity of enterprise data, as well as its central role in decision-making and strategic planning, are driving organizations to invest in the people, processes, and technologies required to gain valuable business insights from their data assets. This includes a wide range of tools commonly used in Data Science applications.
A Data Science Pipeline is a collection of processes that transform raw data into actionable business answers. Data Science Pipelines automate the flow of data from source to destination, providing you with insights to help you make business decisions.
Here’s a list of top Data Science Pipeline tools that may be able to help you with your analytics, listed with details on their features and capabilities – as well as some potential benefits.
Table of Contents
What is Data Science?
Data Science is the study of massive amounts of data using sophisticated tools and methodologies to uncover patterns, derive relevant information, and make business decisions.
In a nutshell, Data Science is the science of data, which means that you use specific tools and technologies to study and analyze data, understand data, and generate useful insights from data. Data Science is an interdisciplinary field that includes Statistics, Machine Learning, and Algorithms.
A Data Scientist employs problem-solving skills and examines the data from various perspectives before arriving at a solution. A Data Scientist employs exploratory data analysis (EDA) and advanced machine learning techniques to forecast the occurrence of a given event in the future.
A Data Scientist examines business data in order to glean useful insights from the data. In order to solve business problems, a Data Scientist must also follow a set of procedures, such as:
- Inquiring about a situation in order to better understand it.
- Obtaining information from a variety of sources, including company data, public data, and others.
- Taking raw data and converting it into a format that can be analyzed.
- Creating models using Machine Learning algorithms or statistical methods based on data fed into the Analytic System.
- Conveying and preparing a report to share data and insights with appropriate stakeholders, such as Business Analysts.
What are Data Science Pipelines?
The Data Science Pipeline refers to the process and tools used to collect raw data from various sources, analyze it, and present the results in a Comprehensible Format. Companies use the process to answer specific business questions and generate actionable insights from real-world data. To find this information, all available Datasets, both External and Internal, are analyzed.
For example, your Sales Team would like to set realistic goals for the coming quarter. They can collect data from customer surveys or feedback, historical purchase orders, industry trends, and other sources using the data science pipeline. Robust data analysis tools are then used to thoroughly analyze the data and identify key trends and patterns. Teams can then set specific, data-driven goals to boost sales.
Key Features of Data Science Pipelines
Here is a list of key features of the Data Science Pipeline:
- Continuous and Scalable Data Processing
- Cloud-based Elasticity and Agility.
- Data Processing Resources that are Self-Contained and Isolated.
- Access to a Large Amount of Data and the ability to self-serve.
- Disaster Recovery and High Availability
- Allow users to Delve into Insights at a Finer Level.
- Removes Data silos and Bottlenecks that cause Delays and Waste of Resources.
How does a Data Science Pipeline Work?
It is critical to have specific questions you want data to answer before moving raw data through the pipeline. This allows users to focus on the right data in order to uncover the right insights.
The Data Science Pipeline is divided into several stages, which are as follows:
1) Obtaining Information
This is the location where data from internal, external, and third-party sources is collected and converted into a usable format (XML, JSON, .csv, etc.).
2) Data Cleansing
This is the most time-consuming step. Anomalies in data, such as duplicate parameters, missing values, or irrelevant information, must be cleaned before creating a data visualization.
3) Data Exploration and Modeling
After thoroughly cleaning the data, it can be used to find patterns and values using data visualization tools and charts. This is where machine learning tools can help.
4) Data Interpretation
The goal of this step is to identify insights and then correlate them to your data findings. You can then use charts, dashboards, or reports to present your findings to business leaders or colleagues.
5) Data Revision
As business requirements change or more data becomes available, it’s critical to revisit your model and make any necessary changes. This is shown in the below image.
The constant influx of raw data from 150+ sources pumping through data pipelines attempting to satisfy shifting expectations can make Data Science a messy endeavor. It can be a tiresome task especially if you need to set up a Manual solution. Automated tools help ease this process by reconfiguring the schemas to ensure that your data is correctly matched when you set up a connection. Hevo Data, an Automated No Code Data Pipeline is one such solution that leverages the process in a seamless manner.
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What are the Best Tools for Data Science?
The most effective Data science tools combine Machine Learning, Data Analysis, and statistics to produce rich, Detailed Data Visualization. Users (regardless of technical skill) can identify trends and patterns and make smarter decisions that accelerate business growth and revenue with the right tools in place.
As Data Science teams build their portfolios of enabling technologies, they have a wide range of tools and platforms to choose from. Here are the top 5 data science tools that may be able to help you with your analytics, with details on their features and capabilities.
1) Statistical Analysis System (SAS)
The SAS Institute created SAS, a statistical and complex analytics tool. It is one of the oldest data analysis tools, designed primarily for statistical operations. SAS is popular among professionals and organizations that rely heavily on advanced analytics and complex statistical operations. This dependable commercial software offers a variety of statistical libraries and tools for modeling and organizing the given data.
These Data Science tools have the following key features and applications:
- It is simple to learn because it comes with plenty of tutorials and dedicated technical support.
- A straightforward graphical user interface that generates powerful reports
- carries out textual content analysis, including typo detection
- Offers a well-managed suite of tools for data mining, clinical trial analysis, statistical analysis, business intelligence applications, econometrics, and time-series analysis.
2) Apache Hadoop
Apache Hadoop is an open-source framework that aids in the distributed processing and computation of large datasets across a cluster of thousands of computers, allowing it to store and manage massive amounts of data. It is an excellent tool for dealing with large amounts of data and high-level computations.
The following are some of Hadoop’s key features and applications:
- Scales large amounts of data efficiently across thousands of Hadoop clusters.
- Hadoop Distributed File System (HDFS) is used for data storage and parallel computing.
- Even in unfavorable conditions, it provides fault tolerance and high availability.
- Integrates with other data processing modules such as Hadoop YARN, Hadoop MapReduce, and many others.
BigML is a scalable machine learning platform that enables users to leverage and automate techniques like classification, regression, cluster analysis, time series, anomaly detection, forecasting, and other well-known machine learning methods in a single framework. BigML provides a fully interchangeable, cloud-based GUI environment for processing machine learning algorithms, with the goal of reducing platform dependencies. It also provides customized software for using cloud computing to meet the needs and requirements of organizations.
BigML’s main features and applications are as follows:
- Aids in the processing of machine learning algorithms
- It is simple to create and visualize machine learning models.
- For supervised learning, methods such as regression (linear regression, trees, etc.), classification, and time-series forecasting are used.
- Unsupervised learning is accomplished through the use of cluster analysis, association discovery, anomaly detection, and other techniques.
The following are some of D3.js’s key features:
- Emphasizes the use of web standards in order to fully utilize the capabilities of modern browsers.
- Combines powerful visualization modules and a data-driven process to manipulate the document object model (DOM).
- Aids in the application of data-driven transformations to documents following the binding of data to DOM.
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- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision-making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 150+ data sources that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Matrix Laboratory (MATLAB) is a multi-paradigm programming language that aids in the creation of a numerical computing environment for the processing of mathematical expressions. The most important feature of this language is that it assists users with algorithmic implementation, matrix functions, and statistical data modeling; it is widely used in a variety of scientific disciplines.
MATLAB is used in the following ways:
- Aids in the development of algorithms and models
- For iterative analysis and design processes, it combines the desktop environment with a programming language.
- Provides an interface comprised of interactive apps for testing how various algorithms perform when applied to the data at hand.
- Aids in the automation and replication of work by automatically generating a MATLAB program.
- Scales up the analysis process to run on clusters, the cloud, or GPUs.
How do Various Industries make use of the Data Science Pipeline?
Regardless of industry, the Data Science Pipeline benefits teams. Here are some examples of how different teams have used the process:
1) Data Science Pipeline for Risk Analysis
Risk Analysis is a process used by financial institutions to make sense of large amounts of unstructured data in order to determine where potential risks from competitors, the market, or customers exist and how they can be avoided.
Furthermore, organizations have used Domo’s DSML tools and model insights to perform proactive risk management and risk mitigation.
2) Data Science Pipeline in Medical Field
Medical professionals rely on data science to help them conduct research. One study uses machine learning algorithms to help with research into how to improve image quality in MRIs and x-rays.
Companies outside of the medical field have had success using Domo’s Natural Language Processing and DSML to predict how specific actions will impact the customer experience. This allows them to anticipate risks and maintain a positive experience.
3) Data Science Pipeline for Forecasting
The Transportation industry employs data science pipelines to forecast the impact of construction or other road projects on traffic. This also aids professionals in developing effective responses.
Other business teams have had success forecasting future product demand using Domo’s DSML solutions. The platform includes SKU-level multivariate time series modeling, allowing them to properly plan across the supply chain and beyond.
Benefits of Data Science Pipeline
Listed below are some benefits of the Data Science Pipeline:
- Increases Responsiveness to Changing Business needs and Customer Preferences.
- Access to Company and Customer Insights is made easier.
- It Expedites the Decision-Making process.
- Allow users to delve into Insights at a finer level.
- Removes Data Silos and Bottlenecks that cause delays and waste Resources.
- Simplifies and Accelerates Data Analysis.
In today’s data-driven world, data is critical to any organization’s survival in this competitive era. Data Scientists use data to provide impactful insights to key decision-makers in organizations. This is nearly impossible to imagine without the use of the powerful Data Science tools listed above.
The Data Science Pipeline is the key to releasing insights that have been locked away in increasingly large and complex datasets. With the volume of data available to businesses expected to increase, teams must rely on a process that breaks down datasets and presents actionable insights in real time.
The Agility and Speed of the Data Science Pipeline will only improve as new technology emerges. The process will become smarter, more agile, and more accommodating, allowing teams to delve into data in greater depth than ever before.
To become more efficient in managing your databases, it is preferable to integrate them with a solution that can perform Data Integration and Management procedures for you without much difficulty, which is where Hevo Data, a Cloud-based ETL Tool, comes in.
To become more efficient in handling your Databases, it is preferable to integrate them with a solution that can carry out Data Integration and Management procedures for you without much ado and that is where Hevo Data, a Cloud-based ETL Tool, comes in. Hevo Data supports 150+ Data Sources and helps you transfer your data from these sources to Data Warehouses in a matter of minutes, all without writing any code!
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