02. Intro to Data Science and Technology – Content [228]

This lesson gives an insight into Data Science and Artificial Intelligence (AI) giving the definition, showing some examples of how it can be used, and finally explaining the progression of data analysis usage.

Data science is an interdisciplinary field that combines scientific methods, processes, business understanding, and systems to gain knowledge or insights from data. A data scientist analyzes data using multiple methodologies to help companies to improve business through insights, predictions, and automation. These data-driven processes, of course, start with data and apply various knowledge and techniques to bring about insights.

AI is the field of developing systems that can perform tasks that require human-type intelligence and this is what Data Scientists strive to accomplish within the new technology.

Below are some real-world examples of what Data Science and AI can do.

REAL WORLD EXAMPLES OF AI

Deep learning can be used for a range of applications from teaching a system to playing a video game to driving a car. It has very powerful potential.

SYSTEM LEARNING TO PLAY MARIO BROTHERS VIDEO GAME

This video is important because it shows a breakthrough in AI where deep learning is used to learn how to play and win at a video game without being preprogrammed with the rules of the game. This is a step in the direction of AI being able to learn, grow and discover paths to a solution that even the developer of the game may not know exists. The important part of the video is the first 37 where you see the solution that a computer program developed over the course of a few days being developed by a system programmed by a single person using free software and an affordable computer that solve a complex problem without knowing anything about the environment. To understand more detail about how this works then feel free to watch the video all the way to the end.

AI USED IN A SELF-DRIVING CAR

Here is another example of how AI along with other advances in technology is being used to solve real-world complex problems. There are many components that make a self-driving car work. One of the core technologies is AI which takes a lot of different data (sensor data, vision data, driving rules, etc.) and makes decisions on what to do. The AI part is exposed to many human examples of driving and the more exposure the better the system gets. The total understanding of how a self-driving car is beyond the scope of this course but the video below gives you a flavor of how it works. Look at the video as an example of how the change in technology is creating new opportunities in the world we live in.

PROGRESSIVE DATA USAGE

Data can be used on many levels which progresses in the power of the data along with the sophistication of the usage. Below is a chart that captures the different uses of data and how you can go from getting access (excel spreadsheet) to data to actually having data drive decisions automatically (self-driving cars)

Data usage spans the spectrum from gaining visibility into the data to using the data to automate the process. Companies, individual departments, and even individual people will use data in a spectrum of ways depending on the project, need and capabilities as defined below.

Data Access

  • Spreadsheets help process data, but they are limited in that it is still up to the user to interpret the data.
  • Spreadsheets are a good tool when looking for answers to already formulated questions

Data Insights

  • By progressing into dashboards, data became easier to manipulate. Dashboards provide visual representations of data, which helps the user more easily interpret data.
  • Dashboards allow for a more interactive way to analyze data.
  • Using spreadsheets and dashboards as a means to analyze data is user-driven.

Predictive

  • Machine Learning allows a system to identify trends and insights from processed data without significant influence from the user.
  • With a goal in mind, the user can receive unsolicited insights from the system.
  • Contrasts with spreadsheets and dashboards which require the user to interpret the data and develop insights without help from the system.

Prescriptive

  • Deep learning allows the user to provide high-level goals. In addition to helping the user develop insights on the data, the system is now able to make decisions based on the data input in order to achieve the high-level goal.
  • For example, a good application of deep learning would be a self-driving car. The car is given a destination (the high-level goal) and is able to follow the path from start to finish based on the data it receives from its sensors.
  • Deep learning develops insights and provides unexpected solutions to high-level goals.

Automation

  • Finally, the technology and data are so rich that the system can make decisions better and quicker than humans, therefore we automate the entire process.
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