Data Science vs Data Analytics vs Big Data

• Emergence of Data Science, Data Analytics, and Big Data
• Exploring Data Science?
• Tools used by Data Scientist
• Understanding Data Analytics?
• Understanding Big Data?
• Characteristics of Big Data
• Various Tools of Big Data
• How are these technologies impacting the economy?
• Skill Sets Required for Data Science, Data Analytics, and Big Data Profiles
• FAQs

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

Emergence of Data Science, Data Analytics, and Big Data

Now with the advent of the digital economy, the Big Data landscape has widened up to new avenues. Most of the time, however, people tend to use the terms, Data Science, Data Analytics, and Big Data 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 their skills. Therefore, it is of utmost importance for us to know the differences between them. Hence, in this blog, we would be discussing all Data Science vs Data Analytics vs Big Data.

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Exploring Data Science?

Data Science deals with the slicing and dicing of 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 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 the Data Science 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. 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 learn 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.

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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

“As per the trends, the number of data scientists will grow by 36% from 2021 to 2031. The field of data science will provide about 1100 millions of jobs by 2026” 

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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Understanding 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

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Data Analytics has shown tremendous growth across the globe. It has become a major part of many 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.

Understanding 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 from 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. 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.

Characteristics of Big Data

There are certain characteristics of Big Data that define its structure and importance. 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 is 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 gets 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 to 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.

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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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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 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.

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.

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

There are different skill sets required to become Data Scientists, Data Analysts, and Big Data Professionals. Though some skills are common in all 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 skillset:

1. Good grasp of 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

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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 Data Analysts in 2021. 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 how to create dashboards and reports

In this blog on Data Science vs Data Analytics vs Big Data, we understood the differences between 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.

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

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 of 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 of 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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Frequently Asked Questions (FAQs)