The digital environment we are living in, here data has become the most valuable and biggest element for many businesses. Data is playing a major role in transforming our lives and the way we are living and communicating with each other. But when it comes to data, there is always a conflict what is better between big data Hadoop and data science? While working with the data terminology, it is better to be clear about this.
- 1 Difference Between Data Science and Big Data
- 1.1 1. Working Concept
- 1.2 2. Responsibilities of Work
- 1.3 3. Needed Skills
- 1.4 4. Data Formation
- 1.5 5. Pay Scales (Salary)
- 1.6 6. Different Applications
- 1.7 7. Career Options
- 2 Final Verdict
Difference Between Data Science and Big Data
1. Working Concept
Data Science is an umbrella term that bounds most of the things relevant to data, right from the data generation to data visualizing, cleansing, mining to deals and analytics with both structured and raw data. The science encompasses programming, statistics, problem-solving, mathematics, etc.
Big Data Hadoop
Big data analytics is all about identifying raw data for holding up decision making in business intelligence. While applying algorithmic procedures, it will make inherent operational visions for business solutions with multi-purposes. In brief, it requires to be transformed, inspected, modelled and cleansed into information.
2. Responsibilities of Work
Exploratory analysis is the main responsibility of data science. The term science defines the analysis and exploration of data with a mixture of machine learning algorithms. This analysis can also forecast the results with the help of trends and anomalies, both obvious and hidden.
Big data technologies store data that is large more than one terabyte, and it is also unstructured. This data is stored from various sources, and future solutions are also based on the data and its structure. The structure and behaviour for future solutions and how they can be offered by applying various technologies, such as Hadoop, Spark, etc. dependent upon needs.
3. Needed Skills
Programming languages skills required in mathematics and statistics-
- Data Visualization
- Data wrangling skills
- Communication skills
- Machine learning skills
For this, you should have-
- Data management skills
- Analytical skills
- Technical skills
- Programming skills
- Database system’s sound knowledge
4. Data Formation
The high volume of internet traffic seizes this data. Also, user’s preferences and behavioural patterns are seized through AV feeds, electronic devices, online forums, and big data analytics tools along with other digital aspects. Collected organizational data from spreadsheets and emails and also from system logs can be detained as Big data.
In the data science field, scientific apps are used. And, these apps can be helpful for a data scientist to withdraw trends or information hidden in big data and others. This field is relevant to filtered data that is guided by forming it for monitoring. Tools and apps that are used to develop working solutions and models, and to filter patterns.
5. Pay Scales (Salary)
Big Data – Salary
A Big Data analytics professional can earn an average salary of Rs.7,24,280 per annum.
Data Science – Salary
A data scientist can earn up to Rs. 7,08,012 per annum.
6. Different Applications
The time when you search a query or a term in your browser, be it in normal mode or incognito mode. It will be surprising for you to know that search results in both the browser windows are different. The reason behind it is that we live in a kind of filter bubble, in which we are already logged into our accounts, depending on the account’s browsing history, the search results are also filtered.
Whenever you open a website, you will notice that it includes various advertisements that are relevant to browsing history. Algorithms of data science and machine learning are also used by each domain of digital marketing like Media.Net or Google AdSense.
As we described, various websites are developing and using lots of algorithms for making them a strong system. These kinds of websites usually cater to the preferences of the users.
Big data analytics technologies are being used in hospitals and healthcare services to store big data to identify for performing tasks, such as optimizing and tracking patient influx, tracking Equipment use, and use of medicines, arranging patient’s data, etc.
An online game’s single frame can need 100 MB of data just for rendering. Suppose how big the generated data will be from the server in an online single gaming session.
Travel service generates big data from the consumers to optimize the travel itineraries and services by multiple channels. Customer preferences are analyzed to provide them with experience or vacation options that are best suited to the interests that optimize changes.
7. Career Options
There are different options for people related to data science that you can explore-
- Data/Enterprise/Infrastructure Architects
- Data Handling
- Data Engineer/Analysts
- Data statisticians
Here are the career options for big data persons-
- Big Data engineers
- Big data analysts
- Big data scientists and statisticians
In this blog, we have represented seven differences between data science and big data. As we know, these both are hot domains, and you can do better in them if you have the right knowledge about top industry trends. To have big data Hadoop and data science solutions, connect with experts of big data.
I produce technical information such as instructions to help users get to grips with all kinds of technology. The material I write is designed to allow their audience to use a particular tech or understand a word of upcoming technology.