Big data vs data science Vs data analytics!  Which is better for you?

Big data vs. data science vs. data analytics? Which is best for you? A complete guide for freshers, students, and working professionals willing to make a career in the world of data We are living in the age of digital revolution. Due to the increasing number of businesses relying on data, data collection and analysis have become more important than ever. Also, data mining – a process used to gather and measure data – is the fuel for most of the business operations.

But what’s the prominence  of data? You ask.  According to a report from Forbes, 2.5 quintillion bytes of data is created each day, and over the last few years, the usage of data has increased by over 90%. Data helps in ways more than one, for example, it helps in understanding customer behaviour  and making informed business decisions.

Data can solve problems related to the performance of a business. It helps prevent businesses from wasting their money and resources on goals that are un achievable and focus on the ones that are achievable. Therefore, data ensures better and larger return on investments,

hence more and more companies believe on the authencity of data to successfully run their business.  A career in data is a much sought-after one for people across different age groups – freshers, skilled professionals, or students. Whether it is data science or data analytics or big data, there is something for everyone who wants to be a data expert.

As the average world data keeps doubling every year at an increasing speed, the hunt for skilled data science professionals is on the rise. However, students or freshers from different disciplines with little or no knowledge in machine learning or artificial intelligence are also attracted to the fields of data science and data analytics. This is largely due to the widespread job opportunities and attractive salary packages.

In this article, we talk about the differences and comparisons between big data Vs data science Vs data analytics,  in detail, and also provide complete information on the salary trends, current opportunities, and skills required to be a successful data professional. This will aslo clear very common doubts like is big data necessary for data science? Or Choosing between big data vs data science career.

This is the ultimate guide that you need to choose a perfect career for yourself!  But, before we talk further about big data Vs data science Vs data analytics , let’s first understand the following terms:

What is data?

Simply put, data is defined as the information in the form of numbers, words, and images. Any information that can be recorded and processed is data. Based on the type of information available around us, data can be classified into personal data, transactional data, website data, sensor data, and behavioural data.

All these types of data require a lot of time and effort to be analyzed. A key parameter to analyze any form of data is to start with the data source. Depending on the source, three common forms of data structures can be identified – structured, unstructured, and semi-structured data.

What is structured data?

As the name implies, data that is available in a structured format and can easily fit into pre-defined models is called structured data (defined as structured data). This kind of data is machinereadable, and therefore requires very less human intervention.

Because structured data depends on pre-existing models, it can be easily stored in discrete fields and accessed without much difficulty. This attribute makes structured data highly powerful. An example of structured data is SQL database.

What is unstructured data?

Unstructured data is often text heavy, such as numbers, dates, etc. Because this type of data is often difficult to interpret using machines, human intervention is the key to read the data. This kind of data doesn’t exist in a pre-defined model and doesn’t follow a pre-existing format.

Big data is classified under the category of unstructured data. And in most industries, traditional tools do not support analyses of unstructured data; therefore, modern and versatile tools are required to assess these types of data.  Common examples of unstructured data include videos, images, and No-SQL database.

What is semi-structured data?

Semi-structured data doesn’t conform to any database or formats but has a few properties of structured data that make it easier to analyze.  XML and JSON are examples of semi-structured data.  [table id=26 /]

Data science vs. big data vs. data analytics – definition

In this section, we are going to define what’s data science, big data, and data analytics. This will help break down some of the key terms and give you a better understanding of each domain so that you can decide between big data vs data science which is better for your career

What is data science? 

Data science is the study of data, which involves both structured and unstructured data. This branch of science deals with methods to analyze, process, and store information in the form of data.  Check out Crampete’s data science Intro video Form [Follow us on Instagram] P.S: Check out our intro video to find difference between big data vs data science vs machine learning

What is big data?

All types of data gathered in large volumes are collectively called ‘big data’. There is no standard format or size in which data should be presented to be known as ‘big data’.  Complex data sets that cannot be read by using traditional software can be categorized as ‘big data’. Therefore, most of the unstructured forms of data are big data due to their complex nature (which is an example of big data without data science) .

What is data analytics?

Data analytics is just one niche area of data science. It is the process of analyzing raw data and applying its importance to a business model. Data analytics is used by professionals like business analysts and data analysts in organizations to understand customer behaviour. There are different metrics that help organizations understand the flow of information and improve the overall effectiveness of business.

Roles and responsibilities of a data science professional 

Commonly, someone who has the knowledge of artificial intelligence, machine learning, and data analysis can look forward to work as a data science professional. Moreover, graduates fresh out of college with computer science, B.Tech, or any other engineering degrees can also start as a beginner in companies that work with data.  P.S Check out this complete section to understand roles of responsibilities of big data developer vs data scientist The key roles of data science professionals are

1. Data analyst
2. Data scientist
3. Business analyst
4. Machine learning engineer
5. Big data engineer

The key responsibilities of every data science professional are as follows:

• Search for new data sources.
• Assess the accuracy of collected data.
• Create some customized statistical models and algorithms based on the end-user behavior.
• Use analytical tools to understand which data is important for the business.
• Develop new marketing and business initiatives based on the data, thereby improving customer experience and business growth.
• Act as the sole mediator between the company and stakeholders and identify any loopholes in the business.
• Work with two or more different teams to achieve business goals.

Role and responsibilities of a big data specialist

The roles and responsibilities of a big data specialist slightly overlap with those of a data science professional.  The key roles of big data specialists are –

1. Business analyst
2. Data analyst
3. Statistician
4. Big data engineer
5. Machine learning and deep learning expert
6. Data architect
7. Chief data officer/analyst
8. Data modeler
9. Data mining analyst
10. Data warehouse manager

The key responsibilities of big data specialists are

1. Work with stakeholders and data owners closely to understand and monitor data consumption and user behaviour.
2. Control and execute data management activities.
3. Create a data policy framework for every organization and ensure it complies with legal regulations.
4. Guide real-time data collection and analyze data by creating affecting tools and statistical models.
5. Use business intelligence tools to identify potential customers.
6. Explore new ways to use data to increase revenue growth.

Role and responsibilities of a data analytics expert

Since data analytics is a branch of data science, it is imperative that the roles will overlap depending on the industry you work with.  The key roles of a data analytics expert are

1. Data miner
2. Data analyst
3. Data scientist
4. Data architect
5. Platform architect
6. Analytics manager

The key responsibilities of a data analytics professional are

1. Obtain data from primary and secondary sources and create and maintain database.
2. Analyze and interpret data using several statistical models.
3. Data mining.
4. Data cleaning.
5. Create reports on data patterns and user behaviour based on relevant data.
6. Work with stakeholders and create documentation on various steps of data analysis.

Applications of data science

Data science is required across different industries such as healthcare, banking, education, and commerce. Some of the areas of application are –

1. Detection of risk in business
2. Healthcare
3. Targeted advertising
4. Internet behaviour and searches
5. Advanced Image and voice recognition
6. Gaming

Applications of big data

The discovery of Internet of Things [IoTs] has led to a massive increase in big data usage, and several companies across the world capitalize on big data.  Some of the key applications of big data are

1. Media and entertainment
2. Banking and securities
3. Healthcare
4. Education
5. Energy sectors
6. Retail and wholesale services
7. Government sectors
8. Insurance
9. Cyber security
10. Weather forecasting
11. Travel and tourism sectors
12. Scientific research

Applications of data analytics 

Data analytics can be applied to the following fields

1. Security
2. Transportation
3. Healthcare
4. Travel
5. Education
6. Digital search and advertisement
7. Government sectors