Big data is now in the mainstream in the technology world, and through actionable insights, data science and data analytics enable businesses to glean. Big data, which is all about creating and handling large datasets, needs an understanding of the technology itself and competency with the tools related to it for parsing data. Interestingly, to better comprehend Big data, data science and data analytics have become an integral part of it. Thus it often creates confusion regarding the difference between data science vs. data analytics vs. big data. Though somehow they are interconnected, they provide different results and approaches. Let’s find the differences between these three essential technologies.

Difference between data science, big data and data analytics

To understand the difference between the three technologies, let’s see how they are defined and what skills are necessary for mastering them and their applications.

What is Big Data

Big Data refers to a massive volume of heterogeneous data that cannot be processed effectively with traditional applications. Here the data could be structured, semi-structured, or unstructured. It is mainly raw data and often impossible to store in single computer memory. Big data provides better insights on the day to day data after processing and lead to better decision making and strategic business moves.

The definition of Big Data, given by Gartner, is, "Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation."

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A Big Data professional needs below skills:

An inclination towards data: Big data means you need to handle large and varieties of datasets. So, a big data professional must be comfortable enough to play with data.

Analytical skills: When the purpose is to make sense of the data's piles, the analytical ability is an obvious necessity. Unless you have the proper analytical ability, you can't determine which is relevant data for business.

Innovation: Big data also need innovative skills, like creating new methods to gather, interpret, and analyze a data strategy.

Mathematical skills: When working with big data, you must have mathematical and statistical knowledge to interpret the results.

Knowledge of Computer science: Big data also need the right amount of programming skills, particularly Java's specialized knowledge. Besides, knowledge of algorithms is also necessary to process data into insights.

Business skills: Not only programming skills but also understanding of the underlying business process is also needed to grasp business growth.

Applications of Big Data:

Big data is used in every sector. Some of the examples are as below -

Big data solve financial services where massive amounts of multi-structured data live in multiple disparate systems. In this sector, big data is used in several ways, like:

- Customer analytics

- Fraud analytics

- Compliance analytics

- Operational analytics

Big Data in telecommunications: Telecommunication service providers have top priorities like gaining new subscribers, retaining customers, and expanding into current subscribers. A massive amount of customer-generated and machine-generated data needs to be analyzed every day, which is a real challenge in this segment.

Big Data for Retail: Competitive analysis is the Brick and Mortar for an e-commerce platform. To stay in the game understanding the customer is the only way to serve them better. For this, understanding and analyzing all the disparate data sources are necessary. This data may include the data companies to deal with every day. For example, weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data.

What is data science

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. Data science is used to tackle big data, including data cleansing, data preparation, and analysis. In this process, data is sourced from multiple channels, and after applying different machine learning algorithms, predictive analytics, and sometimes sentiment analytics extracts information from the data sets. Thus we can say, data science is the incorporation of multiple disciplines, or it is multi-disciplinary, which include-

- data analytics

- software engineering

- data engineering

- machine learning

- predictive analytics

- data analytics

Sentiment analytics

Source: Quora

Knowledge wise, as defined by Hugh Conway in 2010 through a Venn diagram, data science is a combination of mathematics, statistics, subject expertise, and hacking skill.

Venn Diagram
Source: Drew Conway

What Skills are needed in data science?

A data scientist must be proficient enough in –analytics, programming, and domain knowledge. Furthermore, the following skills will help one carve out a niche as a data scientist:

- Strong programming knowledge of Python, SAS, R, Scala

- Practical experience in SQL database coding

- Familiarity with unstructured data and at the same time, the ability to work with it irrespective of the sources of data.

- Concepts of multiple analytical functions

- Knowledge of machine learning

Application of Data science

Search Engines

Data science applies search engine algorithms to deliver accurate results for queries. In Data Science, these algorithms are used to process a significant amount of queries and convert them into useful patterns. Based on the user’s requirements, it enables providing accurate results according to the user's needs.

Delivery Logistics

Data science improves the delivery experience of e-commerce retailers with accurate tracking capability on their logistics side when there is a massive demand for online shopping.

Fraud and Risk

Fraud analysis is an essential criterion in the era of the online transaction. Whether it is retail or finance, data science helps companies to find patterns of fraud detection. Thus it provides broader security checks and improves customer profiling.

Tasks a data scientist needs to perform

- Data transformations, as well as data cleansing. A Data Scientist is also required to pre-process the data.

- Utilizing machine learning for forecasting and classification of patterns.

- Performing the optimization of predicting models and tuning them appropriately.

- Analyzing the requirements of the company and formulating questions for solving them further.

- Performing interactive visualizations for communication results with the team.

What Is Data Analytics?

A data analyst performs the following tasks –

- Fundamental descriptive statistical analysis

- data visualization

- Communicate data points for conclusions.

- Performing analysis and interpretation of data using statistical techniques.

- Extracting data and storing it in databases.

- Performing data cleaning and data filtration.

- Using exploratory data analysis for visual communication of data.

- Working with the teams to analyze business requirements.

Applications of Data Analytics

Management of energy

Data analytics has a sharp footprint in the energy sector. Data analytics helps to focus, monitor, and control network devices for energy optimization, building automation, smart-grid energy, or energy distribution. Besides, it helps with service outage management.

Healthcare

Data analytics is playing a significant role in hospital management. While nowadays, hospitals face massive pressure for treatments and patients, data analytics helps hospitals improve the quality of care. Utilizing machine and instrument data, data analytics optimizes and tracks treatment, patient flow, and equipment use. 

Gaming

The gaming industry has flourished, notably. So, for manufacturers, it has become essential to analyze users' likes and dislikes. Using data analytics, manufacturing companies get a good insight into the same.

Travels

Data analytics in Travel helps to optimize the buying experience via website blogs, data analysis, and social media. Customer's desires and preferences can correlate with the existing sales, followed by browsing can enhance conversions.

What skills a data analyst requires?

So, a data analyst must have

- Basic understanding of statistics

- A perfect sense of databases

- The ability to create new views

- The perception to visualize the data.

Conclusion

In the current scenario, when data has become the dominant backbone of almost all business activities, knowledge of any of these three technologies has become essential. Today business focuses more on data than a product. Etc.  Thus professionals in each of these fields are high in demand. Additionally, the IT industry is experiencing a revolutionized phase regarding data due to the slash in hardware prices with cloud adoption throughout the world. So, the demand for professionals in the field with appropriate techniques will increase every single day.