Difference between Big Data and Data Science

Data changes the way people live. According to the survey, the data is evolving each year every year and each day 2.5 GB data is being generated which is more than the human birth rate. The digital economy has unveiled the doors of the huge landscape of Big Data. Several experts in fields such as data engineering, data mining, data analytics, data science are using it.

There is still the confusion between the concepts of Big Data and Data Science though they seem to be the same in terms of exchanging data, their role is different and the jobs roles are also totally different.

What’s the huge difference between Big Data and Data Science?

Data Science

In the simpler terms, it is the combination of several techniques used to big data and driving insights through that. Digging deeper details into this, this field comprises of doing everything to the data right from fetching data (Data Mining), preparation (Data Analysis), finding the best data (Data Cleansing) and dealing with structured, unstructured and semi-structured data.

Data Science combines mathematics, statistics, problem-solving skills, algorithms, capturing data in brilliant ways, and knowing how to look the data in whole different aspects of preparing and aligning.

 Big Data

Big Data is nothing but processing the extensive amount of data which is not possible through traditional techniques. It starts with the raw data which can be stored on a single computer. It is something that is used for analyzing insights which assist better conclusion of making business moves that are strategically planned.

It all depends on three V’s, volume, Velocity and Variety. The three V’s refers to the types and features the information and processing them for the future use. The process used here should be cost-effective and innovative and can allow enhanced decision making, insight and process automation.

Applications of Big Data and Data Science

Applications of Data Science

This is widely applied in various fields and aspects

 Recommended systems

Data science makes the system easier as it helps in finding the better prospects, displaying the relevant products and increasing the user experience. Most of the organizations use this recommender system to promote their products through suggesting them according to the user search relevance of information and demands, this recommendation depends on the previous search results of users.

Internet Search

Data science algorithms are used by many search engines so as to deliver the best results in just a split second.

Digital Adverts

The reason for the digital ads gets higher CTR than the conventional forms of advertisements is that the whole digital marketing ecosystem makes use of digital science algorithms spanning from display banners down to digital billboards.

Applications of Big Data

Retail

Understanding customer pace is the main thing in the business, rather than being competitive, for which the proper analysis is always required to segregate the data. This segregated data is widely used by the companies which include customer transaction data, weblogs, loyalty program data, social media and store-branded credit data.

Communications

Sales and Communications service is all about finding the customers, gaining new ones and expanding the current customer bases. That can be achieved only with the combining and analyzing lots of customer and machine-generated data.

Financial services

Everything regarding the financial sector such as retails banks, insurance firms, private wealth management advisories, institutional banks and venture funds makes use of this big data. The major challenge experienced by all of them is the large amounts of multi-structured data set in multiple systems which can be take care only by Big Data. This is used in various ways such as fraud analytics, consumer analysis, operational and compliance analytics.

 Tools used in Big Data and Data Science

Data Science is entirely dealt with R programming, Python coding, statistics and Machine Learning. Due to its extensive package repository around statistical and analytics applications, R is tremendously growing in popularity around the world and many firms are on the lookout for R programmers.

Big Data is something which deals with Hadoop, Hive, HBase , Spark and Scala. A basic knowledge of Java is always required for the MapReduce Programming, clustering and Tableau-Hadoop connectors.

Skills required for Big Data  Analysts and Data Scientists

Big Data

• Mathematics and statistical skills.
• Analytical skills
• Computer science
• Knowledge of working with new methods of mixing data together for a gathering, interpreting and analyzing data.
• Business skills

Data Science

• Working with unstructured data such as audio, social media or video feeds.
• Python coding.
• Deep knowledge of R and /or SAS; R is preferable in Data Science.
• SQL database/coding
• Statistics and Analytics techniques knowledge.
• Knowledge of Data Manipulation, preparation, exploration and visualization methods.

Job roles in Big Data and Data Science

A Person who works on Big Data are generally termed as Big Data Analyst and they have certain roles and responsibilities and their annual salary would be around 60,000$ per annum. They would be proficient in SQL querying, Data Visualization tools like Hadoop, Tableau.

On the other hand, people who work on Data Science are generally termed as Data Scientists and the roles and responsibilities differ from Data Analysts. Their annual salary would be around 130,000$ per annum. They would be proficient in Python and R languages. Despite performing with the same operations they have huge overlap between the Data Scientist and Data Analytics, they perform the same operation but with the major concrete differences.

The increase in data creation has led to many industries taking advantage of the benefits of accessing massive tons of data that being produced today. Both make the lives pretty easier every day. Whatever the medium you choose Digital Nest offers various courses for the students, IT professionals, who wish to pursue the career in Big Data and Data Science.