Data Science vs. Big Data vs. Data Analytics

Introduction To Data Science vs. Big Data vs. Data Analytics

What is big data? What exactly constitutes the fields of data science and data analytics? These are trending topics and are no longer restricted in the technology domain. All these terms are becoming more and more relevant in modern times. Humans are creating and generating much more data than the previous century can even fathom – the rate is exponential! Need context? In the past two years alone, we have generated 90% of the total data in the world. As the internet continues to grow and touch every corner of the planet, this statistic is only bound to rise even more.

But why do we even need to discuss about data? What difference does it make to businesses and users? Enterprises have now discovered that data is the biggest asset of this generation. And only data scientists and data analysts can excavate from this gold mine.

Daniel Kays Moran clearly elucidates the power data holds –

You can have data without information, but you cannot have information without data.

Information here refers to customer data insights, profit loss features, marketing impact, sales information – anything that can be determined by cold, hard data. In this article, we will now briefly explain how information is handled in the context of big data, data science, and data analytics.

Big Data

Before learning about big data, let us discover about the various forms data appears in:

1. Structured Data: This type of data comes in a completely organized format. It has a fixed schema. This makes it easier to store and analyze. It can be easily queried upon since the structure of data (rows and columns) is determined.
2. Semi-structured Data: Semi-structured data is only partially organized. The data is in the form of file formats like XML, JSON, and CSV.
3. Unstructured Data: This type of data is not well defined, thus having no particular schema. Real-world data is mostly unstructured making it cumbersome to analyze. Streaming data from digital channels including mobile phones, the Internet, social media, and e-commerce websites constitutes unstructured data.

Now imagine all the aforementioned types of data coupled with huge volume and velocity. That’s what big data is in the most basic sense of the term.

Big Data generally refers to huge volumes of data. It deals with large and complex sets of data that a traditional data processing system (Excel, SQL applications) cannot handle. It can be understood as data that contains greater variety, arriving in increasing volumes and with more velocity. These are popularly called the Three V’s of big data.

Variety	Data doesn’t necessarily come from one source. Big data needs to have the ability to handle data from disparate data sources of structured as well as unstructured data. Frameworks are in place to perform preprocessing on unstructured data like audios, videos as well as derive meaning from metadata.
Volume	Volume refers to humongous quantities of low-density and unstructured data. This includes data from social media platforms, IoT devices, and streaming applications on websites or mobile applications.
Velocity	The amount of data is increasing, but so is the rate at which it is being generated. Big data also has the added capability to store real-time data at the speed at which it is produced.

Technologies used in Big Data

Companies have begun to realize the potential of storing and analyzing the data they generate. Following are some of the commonly used technologies for this purpose –

1. Data warehouses, data lakes, cloud architecture to handle the large volume of data.
2. Data pipelines, ETL tools to deal with the high velocity of big data
3. Big data architectures like Hadoop, Cassandra, Apache Spark, etc. have developed for data mining and analysis purposes. These frameworks are popular for their sturdiness and robust nature of handling big data.

Skills required to become a Big Data professional

1. Proficiency in technologies like Hadoop, Apache Spark, etc.
2. Knowledge to handle unstructured data with NoSQL databases like MongoDB
3. Good command in programming languages like Python, R, Scala, etc.

Data Science

Data science encompasses all the ways in which information and knowledge can be extracted from big data. It is the combination of statistics, mathematics, programming, and problem-solving. Data Scientists are responsible for uncovering facts and patterns hidden in unstructured data. They are also known for developing heuristic algorithms and models that can be used in the future for significant purposes.

We can better understand the role of data science in the industry by studying its life cycle –

1. Understanding business requirements – In this stage, the stakeholders decide the business requirements. The domain experts, business analysts, data engineers, data scientists, and BI experts all contribute to determining the scope of the project.
   
2. Data Acquisition – We know that an enterprise’s data is collected from disparate data sources. Data scientists have to ensure that they only use the data pertaining to their project. Data pipelines are used for this process.
   
3. Data Understanding and Preparation – Exploratory Data Analysis (EDA) is done in this step. Data Scientists use data visualization tools and techniques. In order to optimize results, data also needs to be pre-processed.
   
4. Model creation and Evaluation – Machine learning techniques and models are built to predict outcomes, classify data and identify patterns and relationships. Model creation is not enough. Training and performance evaluation also needs to be done to ensure efficiency.
   
5. Deployment of the model – The model developed is then deployed on production data. It can be reused over time to fulfill different business requirements.

Tools used in Data Science

1. Statistical models and probability metrics
2. Machine learning models developed using Python and R
3. Data visualizations through Tableau and Power BI

Skills required to become a Data Scientist

1. Inquisitive and creative thinking
2. Strong mathematical and statistical knowledge to develop ML algorithms
3. Programming in Python/R
4. Analytical skills for EDA
5. Machine learning prowess

Data Analytics

Data analytics is the process of examining data in order to find patterns and draw conclusions that help businesses. A large number of software and technologies have been developed to aid this process. It is a subset of the data science domain. The prime concern Data Analytics aims to solve is looking into the historical data from a modern perspective. Using this data, provide operational insights for complex business scenarios.

Data Analytics can broadly be divided into four types –

• Descriptive Analytics
  
  It helps answer questions about what has already occurred. It involves summarizing large datasets to describe outcomes to stakeholders. This process provides essential insight into past performances.

• Diagnostic Analytics
  
  This classification of analytics digs around to find the reasons behind a particular outcome. It supplements descriptive analytics and is often clubbed together. They take the findings from descriptive analytics and dig deeper to find the cause. It generally involves three steps:
  
  • Identify anomalies in the data. These may be unexpected changes or patterns.
  • Find the attributes that contribute to the anomaly.
  • Statistical techniques are used on these attributes to determine the relationship.

• Predictive Analytics
  
  It helps determine and find out about what will happen in the future. Predictive analytical tools provide valuable insight into what may happen in the future. Techniques used here are decision trees, regression models, and other unsupervised learning practices. It is one of the most widely sought-after forms of machine learning along with descriptive analytics.

• Prescriptive Analytics
  
  It determines the formulas and techniques that need to be employed to solve a business scenario. By using results gained from predictive analytics, data-driven decisions can be made.

Tools used in Data Analytics

1. Python
2. R Programming
3. Data manipulation tools like MS Excel
4. Readily available visualization tools like Tableau, Power BI, QlikView, RapidMiner, etc.

Skills required in Data Analytics

1. An understanding of programming with R and Python
2. Analytical skills
3. Data visualization and storytelling skills
4. Making creative and insightful charts and dashboards
5. A good grip on statistics

We hope that through this blog post, we were able to explain clearly these key concepts in the technological world. Even people with no technical and programming skills must at least be aware of these terms. Data has become the new currency. And just like it is essential to know about financial concepts like stocks and market. It is equally important to be in sync with data – something that holds tremendous potential in the years to come.