What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
What is Data Science and Business Intelligence (BI)?
The Data Science Lifecycle with a Use Case
What is Data Science?
Data Science is the combination of various tools, algorithms, and machine learning principles with the goal of discovering hidden patterns in raw data. But how is this different from what statisticians have been doing for years?
What is data science is the difference french email address list explaining and predicting.
As you can see from the image above, a Data Analyst usually explains what is happening by processing historical data. On the other hand, a Data Scientist not only does exploratory analysis to uncover insights but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at data from many angles, sometimes from angles that were previously unknown.
So, Data Science is initially used to make decisions and predictions using predictive causal analysis, prescriptive analysis (predictive + decision science) and machine learning.
Predictive Causal Analysis – If you need a model that can predict the likelihood of a particular event in the future, you need to apply predictive causal analysis. Let’s say, if you are providing credit, then the likelihood of future customers making credit payments on time would be a matter of concern for you. Here, you can build a model that can perform predictive analysis based on the customer’s payment history to predict whether future payments will be on time or not.
Prescriptive Analytics: If you need a model that has the intelligence to make its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for that.
Machine Learning for Predictions – If you have transactional data from a financial company and need to build a model to determine future trends, then machine learning algorithms are the best bet for this. This falls under the supervised learning paradigm. It is called supervised because you already have the base data on which the machine learning can be trained. For example, a fraud detection model can be trained based on past history of fraudulent purchases.
Let’s see how the proportion of the approaches described above differ to understand what is Data Analytics and what is Data Science. As you can see in the images below, Data Analytics includes descriptive analysis and predictions to some extent. On the other hand, Data Science is more about predictive causal analysis and machine learning.
Now that you know exactly what Data Science is, let's now look at why it is necessary.
Traditionally, the data we had was mostly structured and small in size, which could be analyzed with simple BI tools. Unlike traditional system data which was mostly structured, nowadays most of the data is unstructured or semi-structured. Let's take a look at the data trends in the image below which shows us that by 2020 more than 80% of the data will be unstructured.
Data is generated from different sources, financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. That is why we need more complex and advanced analytics tools and algorithms for processing and analysis to gain meaningful insights.
This is not the only reason why Data Science has become so popular and more and more people want to know what is data science. Let’s dig deeper and see how Data Science is being used.
What if you could know your customers’ requirements precisely through their data, such as their search history, purchase history, age, and salary. You’ve no doubt had this type of data before, but now with a vast and varied amount of data, you can train models more efficiently and recommend products to your customers more accurately. Wouldn’t that be amazing, as it would bring in more revenue for your business?
Let’s imagine a different scenario to understand the role of Data Science in decision-making. What if your car had the intelligence to drive you home like Tesla ’s ? Self-driving cars collect real-time data through sensors, including radars, cameras, and lasers to create a map of their surroundings. Based on this data, it makes decisions such as when to accelerate, when to slow down, when to pass a car, and where to turn – using advanced machine learning algorithms.
Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircraft, radars, satellites can be collected and analyzed to build models. These models will not only predict the weather but will also help you predict the occurrence of any natural calamities. It will help you take appropriate measures in advance and save the lives of many people.
Let’s take a look at the infographic below to see the areas where Data Science is excelling.