Big Data:
It is huge, large, or voluminous data, information, or the relevant statistics acquired by large organizations and ventures. Many software and data storages is created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology.
Advantages of Big Data:
- Able to handle and process large and complex data sets that cannot be easily managed with traditional database systems
- Provides a platform for advanced analytics and machine learning applications
- Enables organizations to gain insights and make data-driven decisions based on large amounts of data
- Offers potential for significant cost savings through efficient data management and analysis
Disadvantages of Big Data:
- Requires specialized skills and expertise in data engineering, data management, and big data tools and technologies
- Can be expensive to implement and maintain due to the need for specialized infrastructure and software
- May face privacy and security concerns when handling sensitive data
- Can be challenging to integrate with existing systems and processes
Data Science:
Data Science is a field or domain which includes and involves working with a huge amount of data and using it for building predictive, prescriptive, and prescriptive analytical models. It’s about digging, capturing, (building the model) analyzing(validating the model), and utilizing the data(deploying the best model). It is an intersection of Data and computing. It is a blend of the field of Computer Science, Business, and Statistics together.
Advantages of Data Science:
- Provides a framework for extracting insights and knowledge from data through statistical analysis, machine learning, and
- data visualization techniques
- Offers a wide range of applications in various fields such as finance, healthcare, and marketing
- Helps organizations make informed decisions by extracting meaningful insights from data
- Offers potential for significant cost savings through efficient data management and analysis
Disadvantages of Data Science:
- Requires specialized skills and expertise in statistical analysis, machine learning, and data visualization
- Can be time-consuming and resource-intensive due to the need for data cleaning and preprocessing
- May face ethical concerns when dealing with sensitive data
- Can be challenging to integrate with existing systems and processes
Similarities between Big Data and Data Science:
- Both fields deal with large amounts of data and require specialized skills and expertise
- Both aim to extract insights and knowledge from data to inform decision-making
- Both have a wide range of applications in various industries
- Both can lead to significant cost savings and operational efficiencies when applied correctly
Below is a table of differences between Big Data and Data Science:
|
Data Science |
Big Data |
|---|---|
| Data Science is an area. | Big Data is a technique to collect, maintain and process huge information. |
| It is about the collection, processing, analyzing, and utilizing of data in various operations. It is more conceptual. | It is about extracting vital and valuable information from a huge amount of data. |
| It is a field of study just like Computer Science, Applied Statistics, or Applied Mathematics. | It is a technique for tracking and discovering trends in complex data sets. |
| The goal is to build data-dominant products for a venture. | The goal is to make data more vital and usable i.e. by extracting only important information from the huge data within existing traditional aspects. |
| Tools mainly used in Data Science include SAS, R, Python, etc | Tools mostly used in Big Data include Hadoop, Spark, Flink, etc. |
| It is a superset of Big Data as data science consists of Data scrapping, cleaning, visualization, statistics, and many more techniques. | It is a sub-set of Data Science as mining activities which is in a pipeline of Data science. |
| It is mainly used for scientific purposes. | It is mainly used for business purposes and customer satisfaction. |
| It broadly focuses on the science of the data. | It is more involved with the processes of handling voluminous data. |
conclusion:
while big data and data science are related fields that share many similarities, they differ in their areas of focus, data size, tools and technologies used, skills required, and application. Choosing between big data and data science ultimately depends on an individual’s interests, skills, and career goals.