Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.

It is a multidisciplinary [1] field that uses tools and techniques to manipulate the data so that you can find something new and meaningful.

Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.

In short, we can say that data science is all about:

• Asking the correct questions and analyzing the raw data.
• Modeling the data using various complex and efficient algorithms.
• Visualizing the data to get a better perspective.
• Understanding the data to make better decisions and finding the final result.

Example:

Let suppose we want to travel from station A to station B by car. Now, we need to take some decisions such as which route will be the best route to reach faster at the location, in which route there will be no traffic jam, and which will be cost-effective. All these decision factors will act as input data, and we will get an appropriate answer from these decisions, so this analysis of data is called the data analysis, which is a part of data science.

Need for Data Science:

Some years ago, data was less and mostly available in a structured form, which could be easily stored in excel sheets, and processed using BI tools.

But in today's world, data is becoming so vast, i.e., approximately 2.5 quintals bytes of data is generating on every day, which led to data explosion. It is estimated as per researches, that by 2020, 1.7 MB of data will be [2] created at every single second, by a single person on earth. Every Company requires data to work, grow, and improve their businesses.

Now, handling of such huge amount of data is a challenging task for every organization. So to handle, process, and analysis of this, we required some complex, powerful, and efficient algorithms and technology, and that technology came into existence as data Science. Following are some main reasons for using data science technology:

• o With the help of data science technology, we can convert the massive amount of raw and unstructured data into meaningful insights.
• o Data science technology is opting by various companies, whether it is a big brand or a startup. Google, Amazon, Netflix, etc, which handle the huge amount of data, are using data science algorithms for better customer experience figure1 shown below.

Figure1: Data Science.

• Capture: This is the gathering of raw structured and unstructured data from all relevant sources via just about any method—from manual entry and web scraping to capturing data from systems and devices in real time.
• Prepare and maintain: This involves putting the raw data into a consistent format for analytics or machine learning or deep learning models. This can include everything from cleansing, deduplicating, and reformatting the data, to using ETL (extract, transform, load) or other data integration technologies to combine the data into a data warehouse, data lake [3], or other unified store for analysis.
• Preprocess or process: Here, data scientists examine biases, patterns, ranges, and distributions of values within the data to determine the data’s suitability for use with predictive analytics, machine learning, and/or deep learning algorithms (or other analytical methods).

References:

1. https://www.coursera.org/certificate/ibm-datascience
2. https://www.javatpoint.com/data-science
3. https://www.ibm.com/cloud/learn/data-science-introduction

Cite this article:

S Nandhinidwaraka (2021), A Deep Study of Data science, pp. 10