What is data science?

Data Science is a very interesting field of examination. Simply speaking, Data Science is the process of dealing with an input that contains a huge amount of data and then classifying the data collected to further enhance productivity and decision-making in any industry. the key components of the data science process, including data collection, data cleaning, analysis, and visualization. We will explore these concepts as we drive further in this article.

Data Science helps businesses improve decision-making by using data science to monitor, manage, and gather performance measures. Using trend analysis can also help businesses make crucial decisions that will raise revenue, increase consumer involvement, and improve corporate performance.

This article will help you understand the basic concepts of Data Science, and understand how to use different techniques to analyze and visualize the data to make informed decisions using data.

Applications of data science:

As mentioned earlier, data science has a huge range of applications in various industries and businesses. We are going to show you a few examples just to make you familiar with data science in action.

Data Science Application in Marketing and Communications:

First, navigating the huge amount of data gathered by the various market research techniques, including but not limited to what are the users' favorite songs, football clubs, and any other relevant data. Then, using these data to deliver tailored Marketing and Communications messages to the target market, which in turn drives the growth of any organization.

Data Science Application in Social and Digital Media:

The enormous amount of data that were offered by the various social media platforms represented a revolution in data science. Any organization, thanks to social and digital media, can have a constant feed of data that is refreshed daily without any effort. Adding to that the advanced concept of RTD (Real-time) data that allows automatic suggestions and ads to appear to the end user based on his recent activity, for example, if someone posted on his social media that his camera is broken, anonymous data collection automatically shows him ads for new cameras. This service is offered by agencies known as social media intelligence or analytics agencies, also many companies now have an in-house team for Social Media Content Analysis.

Data Science Application in HealthTech:

Today we are witnessing some major changes in our health system that can be referred to as HealthTech. Now we can see many applications for applying data science in HealthTech, Just have a look at the World Health Organization at the time of the Covid-19 pandemic, it was a huge data center that processes data from all over the world and presented them visually so that the whole process of decision making is ensured to be data-driven. Adding to that the ability to predict future potential diseases and prevent them before they are active based on the analysis of the medical history of any patient.

Data Science vs Machine Learning:

Although the concept of Machine Learning has been widely used over the past years constantly, many young calibers just understand the definition and concepts without practical knowledge.

Machine Learning is the process, where you teach the machine how to think and provide logical answers. For example if you want a machine to understand what is the red color, you show it how the red color looks so that it can identify any image containing the red color. This is just a very basic example that can be extended to various applications as long as you fully understand the fundamentals.

The machine is represented as a computer ship mainly and can be attached to physical parts, like in the Automatic Brake System in modern cars, where the computer ship is taught to stop when the front sensors detect any moving or static object.

To sum up, in both data science and machine learning we extract patterns and insights from data to deliver better decisions.

The Data Science Process:

• Data collection techniques.

Data Collection Techniques in the Data Science Process:

If you reached here, I could tell that you understand the importance of data, but how can you collect valuable data as a promising data scientist? In the following section we will introduce to you various data collection methods:

• Web Scraping
  
  Web scraping is the process of extracting large amounts of data using automated tools from various websites. These data can then be used as a valuable asset in many analytical efforts.
  
  

• APIs (Application Programming Interfaces)
  
  You may have encountered permission requests from the social media platforms you use to communicate and exchange data and information with other third parties. These data are then used anonymously by online advertisers to show you ads that are relevant to your search history on social media platforms.
  
  

• Surveys and Questionnaires

Surveys and questionnaires have been always used in many industries for a long time, and yet they proved their effectiveness in collecting valuable data. You can distribute surveys and questionnaires either physically or online through social media and other digital channels.

• Sensors and IoT Devices
  
  Those advanced devices offer RTD (Real-Time-Data) from their surrounding environments, think of your smartwatch that keeps collecting data about your health and traffic sensors that catch any speeding car through motion detectors. 

These data collection methods represent a good example for you to grasp the basics of data collection in the process of Data science, there are other methods like public datasets, experiments and simulations, log files and user activity tracking, and Crowdsourcing. The methods differ but the concept is still the same.

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