Quick Data Science...

The data science industry is booming, and it’s not difficult to understand why.

The growing volume of data in this world, along with powerful new discovery techniques, has created a vast number of opportunities for talented data scientists who can think beyond conventional approaches.

How to Make Money from Data Science

Data Science is everywhere these days. Businesses big and small are making use of this once-niche skill set, with the very best databases databases powers almost every organization. In this article, we'll learn how to make money from Data Science in order to help you decide if it's a good strategy for your company too.

What is Data Science?

It's really not a very difficult thing to grasp, and the following definition should help.

"Data Science is the application of science, engineering, and mathematics to the processing, storage, retrieval, management, and analysis of data. It is a multidisciplinary field that incorporates aspects of statistics, mathematics, computer science, and many other areas."

Data Science Is All About Data

The thing that most people get wrong when they think about data science is that data science is all about data. This is true, but it's not the whole story.

Data science is about understanding the relationship between data and reality. Data science is the combination of several disciplines and knowledge to turn data into insights. It's about creating an understanding of the data in order to make predictions, find insights, and come up with recommendations.

The Data Science Process

To put it simply, the data science process is all about turning data into insights. It involves three steps:

? Data Collection

? Data Preparation

? Data Analysis

These three steps are usually broken down into the following sub-steps:

? Data Collection

Data Collection

This is where the data comes from.

The first thing that happens is that data is collected from a variety of sources.

Data can come from any number of places, such as sensors, questionnaires, surveys, market research, social media, websites, databases, or even email.

The data can be structured or unstructured. Structured data can be found in databases, spreadsheets, or files. Unstructured data, on the other hand, is data that is not in a structured format. This includes text, audio, images, video, etc.

After the data is collected, it is cleaned and prepared.

Data preparation is the next step in the data science process. It's the process of cleaning the data so that it can be properly used for data analysis.

? Data Preparation

This is where data can be manipulated, or changed, so that it can be used.

Data preparation can include:

? Data Cleaning

? Data Munging

? Data Scrubbing

? Data Manipulation

Data cleaning is the process of cleaning up data so that it can be used.

Data munging is the process of changing data so that it can be used.

Data scrubbing is the process of taking out any unnecessary information from data so that it can be used.

Data manipulation is the process of changing data so that it can be used.

Data analysis is the final step in the data science process.

? Data Analysis

It's the process of taking the data and turning it into insights.

In this step, data is analyzed and patterns are discovered.

Data analysis can include:

? Data Exploration

? Data Mining

? Data Visualization

? Data Exploration

This is where data is explored.

Data is explored by discovering patterns and trends in the data.

Data is mined by finding insights that can be used to make predictions.

Data is visualized by making sense of the data and making it understandable.

Data Analysis is the final step in the data science process.