As the planet registered the era of full data, the need for its storage also grew. It was the main battle and concern for the enterprise industries until 2010. The main care was on building a framework and solutions to store data. Now when Hadoop and other support have successfully resolved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the suggestion which you imagine in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the inevitable of fake Intelligence. Therefore, it is very important to understand What is Data Science and how can it add value to your business. Edureka two thousand and nineteen Tech Career Guide is out! fiery business roles, formal learning paths, industry outlook & more in the guide. Download now. In this blog, I will be covering the following topics. By the result of this blog, you fing be capable to understand What is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us. To get in-depth knowledge on Data Science, you can enroll for live Data Science Certification Training by Edureka with 24/7 support and lifetime access. What is Data Science in simple words? Data Science is a blend of many tools, algorithms, and machine determining principles with the goal to discover hidden patterns from the rough data. But how is this different from What statisticians have been doing for years? The answer lies in the difference between explaining and predicting. As you check see from the above image, a Data Analyst usually explains What is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, 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 the data from many angles, sometimes angles not known earlier. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning. Predictive causal analytics – If you wish a paradigm that can predict the possibilities of a different event in the future, you need to apply predictive causal analytics. Say, if you are serving profit on credit, then the probability of customers making impending credit payments on time is a matter of concern for you. Here, you can build a model that can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not. Prescriptive analytics: If you fing a example that has the Intelligence of taking its personal decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively current land is all about serving advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes. The best type for this is Google’s self-driving car which I had discussed earlier too. The data congregated by vehicles can be used to train self-driving cars. You check run algorithms on this data to bring Intelligence to it. This will enable your car to take decisions like when to turn, which path to take , when to slow down or speed up. mechanism establishing for making predictions — If you have transactional data of a finance company and need to build a model to determine the inevitable trend, then mechanism determining algorithms are the best bet. This falls under the example of inspected learning. It is asked supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases. robot determining for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be capable to make meaningful predictions. This is nothing but the unsupervised example as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering. Let’s speak you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength. Let’s imagine how the correlation of above-described approaches differ for Data Analysis as well as Data Science. As you keep see in the icon below, Data Analysis includes circumstantial analytics and prediction to a certain extent. On the other hand, Data Science is more about Predictive Causal Analytics and Machine Learning. Now that you know What exactly is Data Science, let now find out the reason why it was needed in the first place. Why Data Science? Traditionally, the data that we had was mostly structured and little in size, which could be analyzed by using simplistic BI tools. Unlike data in the conventional systems which was mostly structured , today most of the data is unstructured or semi-structured. Let’s have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured. This data is created from several sources like pecuniary logs, text files, multimedia forms, sensors, and instruments. simplistic BI knife are not skilled of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it. This is not the only excuse why Data Science has conform so popular. Let’s dig deeper and see how Data Science is being used in various domains. How about if you could understand the punctual prerequisite of your customers from the existing data like the customer’s former browsing history, purchase history, age and income. No question you had all this data earlier too, but now with the countless amount and variety of data, you check train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization? Let’s accept a various thread to understand the role of Data Science in decision making . How about if your truck had the Intelligence to drive you home? The self-driving truck collect live data from sensors, including radars, cameras, and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms. Let’s imagine how Data Science can be used in predictive analytics. Let’s happen climate forecasting as an example. Data from ships, aircraft, radars, satellites check be collected and analyzed to build models.
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