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. 

Also Read : how to become a data scientist

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.

Also Read : data science and big data analytics pdf

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

Skills required to become a data scientist 

As mentioned above, freshers or students willing to start a career as a data scientist can either take up online data science courses that guarantee certification and placement or work with companies as a trainee where they are exposed to the nuances of the profession.  The five most important areas where you must focus on to develop your skills as a data scientist are – 

1. Machine learning
2. Hadoop
3. Python
4. SQL
5. R

Besides the technical skills, the other skills that are equally important for data science professionals include

1. Communication skills
2. Interpersonal skills
3. Demonstrated proficiency and expertise in using MS Office suite
4. Analytical and problem-solving skills
5. People skills

Skills required to become a big data specialist

Some of the common skills to become a big data specialist are required in areas such as 

1. Hadoop
2. NoSQL
3. Machine learning and data mining
4. Visualization of data
5. Analytics delivery model
6. Programming languages
7. Statistics
8. Big data developer

Other key skills required include 

1. Business intelligence
2. Knowledge and understanding of data quality
3. Ability to create accurate reports based on data trends and patterns for stakeholders
4. Optimize the performance of the business based on key metrics 
5. Work with different teams to achieve business goals

Skills required to become a data analytics professional

Data analytics professionals must possess both technical and leadership skills.  Technical skills are required in the following areas 

• SQL
• R
• Python

Other key skills include but not limited to 

1. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data
2. Leadership skills
3. Communication skills, both written and verbal 
4. Problem-solving skills
5. Ability to make correct business decisions

Tools used in big data

Some of the commonly used tools in the field of big data include 

1. Apache Hadoop
2. Apache Spark
3. R
4. Cassandra
5. Knime
6. Datawrapper
7. MongoDB
8. Lumify
9. HPCC
10. Apache SAMOA

Tools used in data science

Data science tools are categorised on the basis of requirements, for example, data warehousing tools, data analysis tools, and data programming tools.  Commonly used tools in data science include 

1. SAS
2. BigML
3. MATLAB
4. Apache Spark
5. Excel
6. Tableau
7. Jupyter
8. NLTK
9. TensorFlow
10. Weka

Tools used in data analytics

Common tools used in data analytics are 

1. Xplenty
2. Skytree
3. IDEA®
4. Microsoft HDInsight
5. Spark
6. Telnd
7. Spice Machine
8. Plotly
9. Lumify
10. Elasticsearch

Data scientists’ salary 

According to Glassdoor, on average a data scientist can earn $113,436. This salary can vary depending on your experience, location, type of industry, job title, and company size. A fresher or an entry-level data scientist can expect to earn $96,000; a mid-level data scientist can earn $128,700; and an experienced data scientist can earn $165,000. 

Big data specialists’ salary

The salary of a big data specialist depends on skill sets, region, and experience. Depending on the role, on average a big data specialist can earn between $73,000 and $139,000 [Indeed.com]. big data vs data science salary: Salary of both professionals vary depending on the positions they hold and also the type of companies they work with. It’s difficult to say if one is better than the other because both are unique domains. However, data professionals can switch over to any closely related career options in big data during their tenure of employment. 

Data analytics professionals’ salary

A data analyst can earn on average $113,000 starting anywhere between $50,000 and $95,000 [According to University of Wisconsin]. This salary trend varies from industry-to-industry. A senior business data analyst can expect to earn on average $85,000 and an entry-level business data analyst can earn around $55,000.  Data science vs. data analytics salary: The salary of both data science and data analytics professionals is almost the same, with small variation in the entry-level trends. However, both professions require a different set of skillsets and experience for higher-earning opportunities. 

Impact of big data vs. data science vs.data analytics!

As mentioned earlier, big data, data science, and data analytics play crucial roles across scores of sectors, improving hundreds and thousands of lives on a large scale, every day. These three domains improve business growth, regardless of the size. They reveal potential customers and give birth to new channels to ramp up revenues for businesses.

Big data and data science have created a significant impact on the global economy, boosting job growth and innovative products and services.  Official reports indicate that companies that adopt big data analytics can increase their productivity by 6%, thereby creating a positive impact on the GDP.  Some of the key players in big data that have been creating an impact in everyday lives include Amazon, Walmart, IBM, Google, Facebook, Microsoft, and Oracle.

Which is best for you?

Which is better, is it data science or big data? Is the knowledge of big data necessary for data science? Although there’s much hype regarding the career of big data, over years it has blended with data science vs. data analytics vs. machine learning vs. artificial intelligence. This  means one domain is incomplete without the other.

However, the field of big data is vast and includes professionals to know about different concepts such as data mining and predictive analytics. To find which career is the best for you, you need to understand your interests and goals. Since all the three domains are interrelated, there are a wealth of opportunities, and it isn’t difficult to decide which profile suits you the best.  If you have professional knowledge about data science, you can take one step ahead and get into big data. And if you are a fresher with knowledge of machine learning and data science, you can start out with exploring data analytics. 

Comparison of big data vs data science Vs data analytics job responsibilities

One of the globally leading organizations,Amazon recruits a large number of data scientists/analysts and big data engineers throughout the year, all over the world. Some of the job responsibilities that Amazon expects its data science and big data experts to handle are –

1. Analyze data using predictive workforce models to measure performance and trends of the business.
2. Perform statistical analyses in the form of graphs, charts, and tables.
3. Be involved in collection of new data and audit of the existing data in the database management system.
4. Enagage with stakeholders and manage all written findings.

Crampete’s data science course

Check out the data science course on Crampete’s website. This course has been designed for both students and professionals, with and without prior coding experience. It covers the major topics that one needs to know including python, inferential statistics, exploratory data analysis, supervised machine learning, and regression and anova.

Crampete’s data science course promises one-to-one guidance and provides you with a lot of videos, assignments, project-based learning, and a certificate of completion.  This course has benefited professionals from several different organizations, such as TATA, Ford, HP, NTT, and Hinduja. 

Other related areas

With the discovery of IoTs, big data and data science have both laid a strong foundation; therefore the future of data science and big data looks promising. However, there are other equally demanding areas, which are closely related to both, such as machine learning, database management, artificial intelligence, database administration, and information engineering. 

Conclusion:

Hope this comprehensive guide on big data Vs data analytics Vs data science helps you understand and choose a career that is suited to your interest and skills. All the three domains are equally challenging and have inspired both men and women to explore different territories.