Data Science Vs Big Data: Key Differences

Data Science vs BigData: The key difference is in areas of focus, data size, tools, technologies used, and applications

Data Science and Big data are two interrelated concepts that have gained significant importance in recent years. Data science vs Big data is a trending topic. In the data analytics field, both play a vital role in leveraging data for decision-making, innovation, and gaining a competitive edge in today's data-driven world.

The growth trend in the data segment of the industry suggests that data science and Big data analytics are the future. Data Science and Big data are two related but distinct concepts in the data analytics field. Data Science focuses on the application of statistical and machine learning techniques to extract insights from data and solve complex problems. It encompasses data acquisition, cleaning, exploration, and interpretation. Whereas, Big data refers to large, complex datasets that exceed the capacity of traditional data processing methods. Applications are in real-time processing and analysis fields like fraud detection, sentiment analysis, internet traffic analysis, etc.

Let's delve into the key differences between Data Science and Big Data:

Key Concept and Characteristics

Data Science is a multidisciplinary field combining scientific methods, algorithms, and systems for extracting valuable insights from structured and unstructured data. It emphasizes the use of data as the primary resource for analysis, decision-making, etc. To do so, they employ statistical techniques and ML algorithms. These data analysis techniques aim to solve real-world problems.

Big data encompasses structured, semi-structured, and unstructured data from various sources that exceed the capacity of traditional data processing systems. They process and analyze the massive volume of data that has been generated at high speed and is processed and analyzed in real time. The diverse format of data includes structured like databases, semi-structured like XML, and unstructured like text or images. These data are noisy, inconsistent, or incomplete that require advanced cleaning and preprocessing for accuracy.

Scope and Methodology

Data science includes statistical analysis, ML, data visualization, and exploratory data analysis. These are employed to understand the patterns of data, make predictions and solve problems.

In big data, large datasets are handled using technologies and infrastructure. It involves distributed storage and processing frameworks like Hadoop and Spark. To manage vast volumes and high velocities of data, it enables parallel processing, scalability, etc.

Objectives

The primary goal of data science is to gain insights, extract valuable knowledge, and solve complex problems using data.

The main objective of big data is to store, process and analyze massive volumes of data efficiently.

Applications

Data Science is extensively used in business intelligence to analyze customer behavior, market trends, and sales data. In healthcare, it plays a crucial role in analyzing patient data for diagnosing diseases and treatment outcome prediction. It also aids in clinical decision support, personalized medicine, and identifying patterns for disease outbreaks. Data science is utilized in financial institutions for fraud detection, risk modeling, algorithm trading, and making informed investment decisions. They are applied to analyze the human language that enables applications like chatbots, voice assistants, and machine translation.

Big data analyze customer preference, behavior, and purchasing patterns to improve product recommendation, inventory management, pricing strategies, and personalized marketing campaigns. It handles massive amounts of data generated by IoT devices such as wearables and sensors. These technologies are employed to analyze social media data including user interactions, sentiment analysis, and trending topics.

Advantages

Data science helps organizations to make informed decisions by extracting meaningful insights from data. This is done through statistical analysis, ML techniques, and data visualization techniques. The wide range of applications including in finance, healthcare, business, etc. Efficient data management and analysis in data science offer significant cost savings.

Big data handle complex data enabling organizations to gain insights and make data-driven decisions. It provides a platform for advanced analytics and ML applications.

Disadvantages

Data science requires skilled professionals in the field. Due to the need for preprocessing and data cleaning, this technique is time-consuming and needs more resources. Since it deals with sensitive data, ethical concerns may be a problem.

Big data need skill and expertise in the field. Security and privacy are a concern when handling sensitive data. It can sometimes be expensive due to the need for specialized infrastructure and software.

Tools

Data science uses tools like Apche Hadoop, DataRovit, Tableau, QlikView, Microsoft HD Insights, TensorFlow, Jupyter Notebooks to effectively handle and analyze huge data.

Big data uses Pache Storm, Spark, Apache Flink, Splunk, Clouders, Apache Cassandra to perform complex analyses.