Why data science field is booming

Data is everywhere

The field of data science is growing because data is all around us. Data has been with us since the beginning of time, but it's become more and more important to record and track in recent years.

You may be thinking, "My life isn't data! I'm not a robot." But you are wrong. All the things you do in your day-to-day life generate data. What time did you wake up this morning? How long did it take you to get to work? These are all pieces of data that can be recorded by sensors that track your movements through GPS on your phone or car and can even be accessed by companies like Google. The way you move around a city gives insight into how developed infrastructure needs to be for that city, how many cars need to be on the roads at certain times, travel patterns etc.

Every piece of technology saves some kind of useful information or data about its use as well. This can range from when your computer was last updated and what programs you have installed on it, to how many times per second a video game character jumped over an obstacle.

The idea behind collecting all this information is simple: figure out what people want based on what they do, then give them what they want more often than not so they keep doing business with you (or simply using your product). Say Target sees through their own customer's buying habits that someone is pregnant before her family finds out; now Target knows exactly when to start sending coupons for baby items so they'll have already established brand loyalty when the baby arrives, making it likely the new parents will continue shopping at Target for their new family's needs as well.

The advent of big data has grown

The advent of big data has grown to a formidable force. It’s become a booming field that’s increasingly becoming valuable across industries and enterprises. Data science is equally now commonplace in the world we live in, but there are differences between the two. In this post, you will learn about both how they relate to each other, as well as how data science and big data are different.

What is Big Data?

Big data refers to large volumes of data (data sets) that cannot be managed by traditional tools or methods such as relational databases or Excel spreadsheets. It consists of three components: volume, variety, and velocity. Volume refers to the amount of data generated from different sources such as social media websites like Facebook and Twitter or search engines like Google and Bing. Variety is the sheer number of types of information that is generated through these sources — for example text files, images, audio clips and video clips — all made possible through technological advancements like cloud computing platforms and mobile devices. Finally, velocity refers to the speed at which this information is created in real-time; i.e., it cannot be stored for an extended period because it becomes outdated too quickly!

Data science is a field with lots of moving parts

Data science is an interdisciplinary field that combines math, statistics, and computer science. This means you’ll be working with a variety of data types, from numerical to textual, to images and audio.

The problem-solving process can be broken down into six stages: framing the problem, collecting data, preparing the data for analysis and exploration, exploring the data (performing basic statistical analyses), building models based on your findings (from the "exploring" stage), and finally communicating your findings.

Data science such a booming field

Data science is a challenging, fun and rewarding field. It’s also one of the fastest-growing and most in-demand fields at the moment.

Companies are seeking data scientists to help them make sense of their data, because they know that good analysis can provide insights that could transform the way businesses operate and change the way decisions are made

Data Science popular tools

It is an emerging field that is rapidly evolving. It deals with analyzing data, deriving new insights, and sharing them with stakeholders in an efficient way. To help you navigate this vast ocean of information, here is the list of some of the popular Data Science tools ,language and package available today:

• Python
• R
• SAS
• Apache Hadoop
• Tableau
• TensorFlow
• BigML
• Knime
• RapidMiner
• Excel
• Apache Flink
• PowerBI
• DataRobot
• Apache Spark
• Sap Hana
• MongoDB
• Trifacta
• Minitab
• Apache Kafka
• QlikView
• MicroStrategy
• Google Analytics
• Julia
• MATLAB