Mari defines data science as turning massive digital data into business insights through four capabilities: describing current state, detecting anomalies, diagnosing causes, and predicting futures. The field exploded because organizations can now collect unprecedented volumes of data and link disparate sources like dealership records with social media to build rich customer profiles.
Chapters
Hi, I'm Mari and I lead the content team at datacamp. We are an international team of data scientists, analysts, statisticians, programmers, and much more. Data science is a dynamic field. The tools that we use and the capabilities of our teams are changing every day. In this course, I'll explain what data science is and how you can use it to strengthen your organization. If we google what is Data Science, we'll see a huge amount of confusing information. But data science is actually simple. It's a set of methodologies for taking in thousands of forms of data that are available to us today and using them to draw meaningful conclusions. Data is all around us every like click email, credit, credit card, swipe or tweet is a new piece of data that can be used to better describe the present or better predict the future. So what can data do for you and your team? Data can describe our current state. This can be accomplished with dashboards or alerts, simplifying time intensive reporting processes. With new data technology, it can help detect anomalous events. If we have data on what has happened previously, we can increase efficiency by automatically detecting a new event that is unexpected. Data can also diagnose the causes of observed events and behaviors. Rather than determining correlations between small numbers of events. Data science techniques help us understand complex systems with many possible causes. Finally, data can predict future events. We can use new techniques to take various causes into account and predict potential outcomes. Further, we can evaluate the probability of our prediction mathematically to clarify our level of uncertainty. So now we know what data science is. The next question is why is it so popular? The answer is pretty banal. We're collecting more data than ever before. Suppose that you visit a car dealership and fill out some information. All of that data is automatically entered into a computer and combined with the data from hundreds of dealerships into one big database. Once we have that data, it's easy to use the email address that you provided when you bought that car to tie your car purchase data with your data from social media or web browsing. Suddenly, we have a very complete picture about everyone who purchased a car in the last year, their ages, their likes and dislikes, their friends and family. This additional data can be used to predict what price you can pay for your car, what other purchases you're likely to make, or how best to sell you insurance for that new car. Data is everywhere and it is incredibly valuable for businesses. So how do we start to use our data? In data science, we generally have three steps to any project. First, we collect data from many sources such as customer surveys, Web traffic results, emails between sales representatives and potential clients, and financial transactions. Next, we explore and visualize our data. This could involve building dashboards to track how our data changes over time or perform comparisons between two sets of data. Finally, we make predictions with our data. For example, this could involve building a system that segments customers or classifies pictures of different types of cars. Now you know why data science is important to business and the first three steps in the data science workflow. Let's explore these themes in some practical.
