Data Science vs Data Analytics vs Big Data

In this digital era, the amount of unstructured data is increasing day by day. With this humongous data, diverse avenues have opened up in the Big Data landscape. This includes Data Science and Data Analytics. Although people tend to use..Read More

  • Updated on 06th Feb, 17
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What is Data?

Data is the collection of facts and bits of information. In the real world, the data is either structured or unstructured. In this blog on “Data Science vs Data Analytics vs Big Data”, let us first understand the types of data.

Structured data is the data that has an order and a well-defined structure. As the structured data is consistent and well-defined, it is an easy task to store and access it. Also, searching for data is easy as we can use indexes to store structured data. 

Another type is the unstructured data. It is an inconsistent type as it doesn’t have any structure, format, or sequence. The unstructured data is error-prone when we perform indexing on it. Hence, it is a difficult task to understand and operate on unstructured data. Interestingly, in the real world, more than the structured data, what we have always is the inconsistent unstructured data. It can be in the form of audio, video, text, or any other format. 

Check out this video from Intellipaat to make a clearer comparison among Big Data, Data Science, and Data Analytics:

Why is data important?

Look at the statistics below to see what happens in the daily data life:

Average daily  –

  • People across the world:
    • Send more than 300 billion emails and 500 million tweets
    • Send over 65 billion messages via WhatsApp
    • Perform 5.6 billion searches on Google
  • Facebook creates nearly 4 petabytes of data
  • By the year 2025, there will be 463 exabytes of data worldwide!

Data is one of the biggest assets any company has in the present time. This, in fact, was long predicted by Forbes when it stated: ‘The total data market is expected to nearly double in size. It will grow from US$69.6 billion in revenue in 2015 to US$132.3 billion in 2020.’ By these statistics, we can infer how important data is and the need to utilize it for businesses.

Now, let’s understand the necessity of data with a real-life use case of bank payments.

Use Case of Bank Payments

Suppose, some customers make payment to their respective merchants (such as Paytm, Amazon, Flipkart, etc.). The customers use the Citi bank debit card for the transactions. Now, the merchants collect the data related to transactions. This may include the mode of payment, data of the payment receivers, the time of the transaction, and the amount. The merchants analyze the data and build specific data products on top of these parameters. These data products exclude the confidential details of the customers. They consist of the following details of the transactions:

  1. Mode of payment
  2. Frequency
  3. Amount
  4. Bank name
  5. Merchant to whom the customer has paid (e.g., Flipkart, Amazon, Zomato, Swiggy, etc.)
  6. The number of customers making transactions per day

These are the basic parameters for building data products. There can be more parameters based on the type of industry. After this, the merchants sell the data products to the banks.

The banks utilize the data to target customers by providing them with exciting offers. Due to this, the customers start making transactions through those banks that provide the greatest offer. These customer payments increase the revenue base of the banks. This is how data helps in increasing revenue generation for the banks, as well as for the merchants.

By this use case, I hope you understand the importance of data in real life.

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Emergence of Big Data, Data Science, and Data Analytics

Now with the advent of the digital economy, Big Data landscape has widened up to new avenues. Most of the time, however, people tend to use the terms, Big Data, Data Science, and Data Analytics, interchangeably in spite of the huge differences existing among these concepts.

Thus, aspirants often mistakenly opt for a different job role that does not match with their skills. Therefore, it is of utmost importance for us to know the differences among them. Hence, in this blog, we would be discussing all about Data Science vs Data Analytics vs Big Data.

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What is Big Data?

Big Data, Data Science, and Data Analytics are not just some technical jargon but are significant concepts contributing to the field of technology. While these terms are interlinked, there are fundamental differences among them. In this section of the ‘Data Science vs Data Analytics vs Big Data’ blog, we will learn about Big Data.

According to Forbes, today, there are millions of developers (more than 25% of developers globally) who are working on projects of Big Data and Advanced Analytics.

Big Data refers to huge volumes of data. It deals with large and complex sets of data that a traditional data processing system cannot handle. Big Data consists of tools and techniques that extract data, store it systematically, and extract useful information out of the data. Here are various types of data that Big Data deals with:

  1. Structured Data: This type of data contains organized data. It has a fixed schema. Thus, it is easy to understand and analyze structured data.
  2. Semi-structured Data: The data in the form of various file formats like XML, JSON and CSV is categorized as semi-structured data. It is partially organized data, which makes it difficult to understand.
  3. Unstructured Data: This type of data does not have a well-defined structure or a schema. The real-world data is always unstructured and hence challenging to understand. This data is generated through various digital channels including mobile phones, the Internet, social media, and e-commerce websites.

Further in this blog, we will look at the characteristics of Big Data.

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Characteristics of Big Data

There are certain characteristics of Big Data that define the structure and importance of it. The six characteristics of Big Data are described below:

  1. Volume: The amount of data generated per day from multiple sources is very high. Previously, it was a redundant task to store this big data. But, with the help of Big Data Hadoop, we can efficiently store these huge volumes of data.
  2. Variety: There are a variety of data collected from different sources. It can be an audio file, video, images, documents, or unstructured text. The tools in Big Data help in processing this variety of structured and unstructured data.
  3. Velocity: In this digital era, the number of Internet users is increasing rapidly day by day. Due to this, the speed of data generation get enhanced. The term Velocity refers to how fast this data generation and its processing are happening. It is used to understand the trends in the data and meet the demands of the market.
  4. Veracity: It relates to the quality of the data collected. Organizations need to take care of the quality of data while collecting it so that the data is relevant for them.
  5. Value: Big Data focuses on collecting data that creates some business value for the organizations. This helps them compete in the market and increase their profits.
  6. Variability: There is always a change in trends in the market. Variability refers to how often this change happens. Big Data helps in managing these drifts of data that benefit organizations to come up with the latest products.

Various Tools of Big Data

There are various tools for processing Big Data such as Hadoop, Cassandra, Apache Spark, RapidMiner, etc. Big Data has proven to be of great use since its inception. This is due to the reason that companies started realizing its importance for various business purposes. Now that the companies have started deciphering this data, they have witnessed exponential growth over the years.

Moving ahead with this Data Science vs Data Analytics vs Big Data blog, we will look into Data Analytics.

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What is Data Analytics?

Data Analytics seeks to provide operational insights into complex business situations. The prime concern of a Data Analyst is looking into the historical data from a modern perspective and then, finding new and challenging business scenarios. After that, he/she applies methodologies to find better solutions. Not only this, but a Data Analyst also predicts the upcoming opportunities that the company can exploit.

The responsibilities of a Data Analyst and a Data Scientist are similar to each other. However, they differ in the implementation part. The below diagram shows the difference between the responsibilities of a Data Analyst and a Data Scientist.

Data Analysts collect data for their organizations from multiple sources. They perform exploratory data analysis to visualize the data. Then, they filter and clean the data by checking the reports generated with the help of the Data Analytics tools. After that, the data is analyzed with the help of a data visualization tool. Also, they build effective strategies to optimize the statistical analysis of the data. This helps organizations note down the growth or the market trend.

Some of the tools used for Data Analytics are:

  1. R programming
  2. Python
  3. Tableau Public
  4. SAS
  5. RapidMiner
  6. KNIME
  7. QlikView
  8. Splunk

Data Analytics has shown tremendous growth across the globe. It has become a major part of many of the organizations. Soon, the Data Analytics market revenue is expected to grow by 50 percent. Besides, there will be a plethora of job opportunities for Data Analytics professionals.

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What is Data Science?

Data Science deals with the slicing and dicing of the big chunks of data. It uses techniques to obtain insightful patterns and trends from the data. Data Scientists are responsible for uncovering the facts hidden in the complex web of the unstructured data. This helps in making important business decisions in accordance with market trends. Data Science also involves the creation of Machine Learning models on top of the visualized data. To understand Data Science thoroughly, let’s look at its life cycle:

Understanding the Life Cycle of Data Science

  1. Understanding business requirements: Data Scientists perform a structural analysis of the business model. Then, they understand the market trends and customer needs. This helps to gather business requirements.
  2. Collecting data: The collection of valuable data is a necessary step in Data Science. The data is collected from multiple sources.
  3. Data understanding: The next step after data collection is understanding the data. For this, Data Scientists use data visualization tools and techniques.
  4. Data preparation: Since organizations need to create an effective strategy and model on the basis of data, Data Scientists prepare data accordingly. Suppose, if the need is for building a recommendation system on fashion trends, then Data Scientists have to prepare the data relevant to the trending fashion.
  5. Model creation: Data Science widely uses Machine Learning for building systems and models on top of the dataset prepared. Data Scientists use Machine Learning algorithms and techniques to build models. The organizations use these models to fulfill their business requirements.
  6. Model evaluation: Building a model is not enough. They have to assess the accuracy of the model. So, they use different data to train and evaluate the built model.
  7. Deployment of the model: After checking the performance of the model, it is deployed for implementation.
  8. Iteration of the process: The systems built with the help of Machine Learning learns from their experience. For this, Data Scientists expose them to a variety of real-time datasets. And the iteration of the learning process makes the models more accurate.

Tools used by Data Scientist

Tools used by Data Scientists for implementing the above steps are:

  1. Statistics and probability
  2. R and Python programming
  3. Tableau and Power BI for data visualization
  4. Machine Learning algorithms

“IBM predicts that the annual demand for data science work will reach nearly 700,000 with demand growth of 28% in 2020.” 

Data Scientists perform the aforementioned jobs by developing heuristic algorithms and models that can be used in the future for significant purposes. This amalgamation of technology and concepts makes Data Science a potential field for lucrative career opportunities. I hope by this explanation you are clear with the concepts of Data Science vs Data Analytics.

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Watch this Video on Difference between Data Analyst and Data Scientist

How are these technologies impacting the economy?

Data is the baseline for almost all activities performed today, be it in the field of education, research, healthcare, technology, or retail. Also, nowadays, the orientation of businesses has changed from being product-focused to data-focused. Even a small piece of information has become valuable for companies. The visualization and analysis of information help in acquiring business insights. This necessity gave rise to the need for experts who can bring out meaningful insights from data.

Big Data Engineers, Data Scientists, and Data Analysts are kinds of specialists who deal with data. These roles vary according to the process flow from the raw data to a finished data product.

 Impact on Various Sectors
Big Data Data ScienceData Analytics
  • Retail
  • Banking and investment
  • Fraud detection and analyzing
  • Customer-centric applications
  • Operational analysis
  • Web development
  • Digital advertisements
  • E-commerce
  • Internet search
  • Finance
  • Telecom
  • Utilities
  • Travelling and transportation
  • Financial analysis
  • Retail
  • Research
  • Energy management
  • Healthcare
 Skills Required
  • Analytical skills
  • Mathematics and statistics
  • Java
  • Hadoop
  • SAS
  • R/Python programming
  • Hadoop
  • SQL database
  • Analytical skills
  • Statistics
  • Mathematics
  • Visionary thinking
  • Programming
  • Communication
  • Artificial Intelligence
  • Data wrangling skills

It is evident from this table how these areas impact our economy. Actually, technologies are helping diverse sectors in a great way, allowing them to put each and every piece of insight into use. While Big Data is helping retail, banking, and other industries by providing some of the important technologies such as fraud-detection systems, operational analysis systems, etc., Data Analytics allows the industries of healthcare, banking, travel and transport, energy management, etc. to come up with new advancements using the historical trends. On the other hand, Data Science is letting companies get into Web development, digital advertisements, e-commerce, etc. and dive deep into the granular information for different purposes.

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Skill Sets Required for Big Data, Data Science, and Data Analytics Profiles

There are different skill sets required to become Data Scientists, Data Analysts, and Big Data Professionals. Though some skills are common in all the three profiles, the level of proficiency varies as per the job roles. Therefore, you should clearly know what you want to become and what skills you need to have for that. In this section of the ‘Data Science vs Big Data vs Data Analyst’ blog, we will look into the skill set needed for each one of them.

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Skills for Becoming a Data Scientist

Data Science is a broad field of study. It requires knowledge of various fields such as programming, database, and Machine Learning. According to Forbes, ‘Data Scientist jobs are among the best jobs in the IT industry.’ The average salary of a Data Scientist is US$120,000.

To become a Data Scientist, you must acquire the below skill set:

  1. Good grasp over Python and R programming language
  2. Knowledge of mathematics especially statistics and probability
  3. Awareness of SQL database queries
  4. Knowledge of data mining
  5. Knowledge of how to work on data visualization tools

If you acquire these skills, then you can easily start your professional career as a Data Scientist.

Further, we will see the skills required to become a Big Data expert.

Become a Big Data Architect

Skills for Becoming a Big Data Professional

Big Data is another widely used technology in the industry. According to LinkedIn, the average salary provided to a Big Data professional in the United States is US$115,689. In India, this salary figure is around ₹725,000 for a fresher.

Here are the skills that you must possess to get into the field of Big Data with a decent pay scale:

  1. Proficient in Big Data Hadoop
  2. Good grasp over Apache Spark
  3. Knowledge of NoSQL databases such as MongoDB and Couchbase
  4. Awareness of the quantitative and statistical analysis approach
  5. Excellent understanding of SQL
  6. Good command in programming languages such as Python, C, C++, Java, and Scala

Now, what are the skills required to become a Data Analytics professional?

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Skills for Becoming a Data Analytics Professional

Nowadays, Data Analytics has become an essential part of business processes. Organizations hire Data Analysts to perform essential analytics on data. According to McKinsey, there are more than 10,000 job openings for a Data Analyst in 2020. Also, the average salary of a Data Analyst is around US$105,253 in the USA. Below are the skills you should have if you aspire to become a Data Analyst:

  1. Programming experience in Python and R
  2. Knowledge of statistics and probability
  3. Data visualization and presentation skills
  4. Analytical skills
  5. Fair knowledge of Microsoft Excel
  6. Understanding of how to create dashboards and reports

In this blog on Data Science vs Data Analytics vs Big Data, we understood the differences among Data Science, Data Analytics, and Big Data. Also, we saw various skills required to become a Data Analyst, a Data Scientist, and a Big Data professional.

Stay tuned with us to know more!

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