Learn how to distinguish between areas of data science. This video covers data science, artificial intelligence, and deep learning.
- [Instructor] Now that you know what data science really is and where a data scientist fits within the spectrum of other data professionals, let's talk about the four different flavors of data science. Those are data analysis, data science, artificial intelligence, and deep learning, and I really wanted to start off this course just by defining each of these four terms and giving you some perspective on what you're about to learn and where it fits in within these four, quote-unquote, flavors. Why we need to add clarity here is really just so that we can get you oriented so you understand where you're headed, where you're at now, and where you've been in the past. This will facilitate you to bloom and thrive where you're at. You probably have lots of great analytical competencies that qualify you, and perhaps even qualify as data science competencies. You may not even actually know this at this time, so you're going to get a bunch more skills by taking this course, but I want you really to understand that you're probably further ahead than you actually know you are. First, let's talk about data analysis. Data analysis is a process for making sense of data. Data analysis could be something like data cleaning, reformatting, recombining data, things like that. It's carried out with the express intention of discovering trends, patterns, and findings in data that describe real life phenomenon. You've probably done extensive data analysis in applications like Excel, or if you've ever worked with GIS. There are many, many different career paths that rely on a person's ability to analyze data, and I don't want you to discredit the experience you already have in data analysis. So, that's one part of data science. The next is proper data science. Proper data science, as we discussed in the last section, is the systematic study of the structure and behavior of data in order to quantifiably understand past and current occurrences, as well as to predict the future behavior of that data. Moving into artificial intelligence, when you hear the term artificial intelligence, that's referring to a machine or application with the capacity to autonomously execute upon predictions it makes from data. There are two main elements within artificial intelligence. Those are prediction and execution. Prediction is where you do the predictive modeling within data science, and then the execution is the autonomous response from the engineered systems. Lastly, there's deep learning, and I'm sure you've heard tons about deep learning. I wanted to touch upon that so you understand where that fits in with respect to what you already know and what you're about to learn. Deep learning is a set of predictive methodologies that borrows structures from the neural network structures of the brain. Deep learning is a class of methods that's particularly effective for making predictions from big data. It's really a subfield within data science. Another thing to point out about deep learning is that it can be used as a decision model within applications to produce deep learning AI applications. Now that you understand about the four flavors of data science, let's talk about why you'd use Python for working with data. We're going to talk about that in the next section, so I'll see you over there.
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly