Data Science vs Big Data vs Data Analytics: The Differences You Should Know

The data around us keeps growing exponentially as it continues to prove to be a vital part of our daily lives.

According to a 2021 estimate published on Statista’s website, the global data creation is projected to reach 180 zettabytes in the next five years!

This is why we have put together a well-curated article that will provide you with an in-depth understanding of data science, big data, and data analytics, and the role they play in framing the way we consume data today.

What is data science?

Data science is a field of computing that includes everything connected with the cleansing, preparing and aligning of data that can be further leveraged for extracting information and insights. 

What is big data?

Big data consists of significant volumes of data, both structured and unstructured, that can be used to scan through all data generated by a business on a regular basis.

It has developed into a buzzword in the modern financial market because of its ability to help leaders make better strategic business moves and decisions.

What is data analytics?

The science pertaining to the process of deriving conclusions based on analysing raw data is referred to as data analytics.

What are the applications of data science?

Data science is a perfect amalgamation of various algorithms, tools, and machine learning principles that work together to discover hidden patterns by putting together raw data.

The top four applications of data science are listed below which allows professionals to arrive at the solution of a given data problem.

1. Internet search

Search engines such as Google, Yahoo, and others make the best use of data science to deliver a swift result for your search query by using complex algorithms.

2. Targeted advertising

The entire digital marketing spectrum relies on data science algorithms to display advertisements in the same place at the same time, on websites and banners that the public can see on digital billboards at airports for example.

3. Recommender systems

Data science can help provide recommendations on services, websites and products based on a user’s previous search results, making it easier for companies to promote their suggestions and products in accordance with the relevance and interest of information to improve user experience.

4. Gaming

Data science allows a digital system to analyse the previous moves made by a user to accordingly shape its structure, leading the gaming experience to achieve a higher level by implementing data science technology.

What are the applications of big data?

Adopting big data projects can enhance an organisation’s customer experience, as well as achieve related goals, such as better targeted marketing and cost reduction.

Following are some of the top big data applications across industries.

1. Banking and securities

Big data protocols can help trading organisations monitor financial market activities to prevent illegal transactions, using natural language processors and network analysis.

2. Education

The application of big data in education oversees certain crucial processes including student logins into the system, behavioural classification, student aspirations, subject matter, and student demographics.

3. Manufacturing and natural resources

This specific industry utilises big data to support decision-making related to integrating and ingesting large amounts of data such as graphical, text, temporal and geospatial.

4. Transportation

The impact of big data in the transportation industry includes its applications by private organisations, government, and individuals to predict traffic conditions, optimize revenue management, and integrate strategy travel arrangements.

What are the applications of data analytics?

Data analytics can be implemented to collect, maintain and analyse data to evaluate and improve the growth of business organisations.

Here we have listed a few applications of data analytics in different fields.

1. Logistics and delivery

Popular international shipment companies like FedEx, DHL and more utilise data analytics to manage delivery timelines, as well as the best shipping routes.

2. Manufacturing

To ultimately increase the profitability of a company, the manufacturing industry implements data analytics tools to conduct regression analysis and predict the budgeting outcome.

3. Security

Security analytics is a modern way of delivering information proactively and safely, by identifying dangers that can affect the main framework.

What are the skills required to become a data scientist?

Besides possessing vast knowledge in diverse fields such as machine learning, database, and programming, data scientist jobs require you to have an understanding of statistics and profitability and the capability to use data visualisation tools.

What are the skills required to become a big data professional?

The big data industry calls for graduates who are proficient in big data Hadoop and Apache spark, additionally having a good grasp over SQL and NoSQL, as well as awareness of the statistical and quantitative analysis approach.

What are the skills required to become a data analyst?

Data analytics, being an essential part of every business process, requires individuals who possess effective presentation and data visualisation skills as well as showcasing the skill to create reports and dashboards using Microsoft Excel.

The Higher Diploma in Data Science and Analytics offered by the London School of Business and Finance Singapore (LSBF SG) has an in-depth technical curriculum on data science, big data, and data analytics that can help you acquire the industry-specific expertise to kick-start your career in this thriving field.

Start your application today and obtain a personalised road map to becoming a successful data analysis and management expert in no time!