What Is Data Science? - A Beginner’s Guide To Data Science
The demand for storage increased as the world moved into the era of big data. It was the main problem and source of concern for the corporate industries until 2010. The development of a framework and data storage solutions were the major priorities. The attention has shifted to data processing now that Hadoop and other frameworks have successfully tackled the storage difficulty. Data science is the secret sauce here. All of the ideas you see in Hollywood sci-fi movies can become a reality because to data science. Data Science is the future of artificial intelligence. As a result, it's crucial to know what Data Science is and how it may help your business.
What is Data Science?
Data Science is a collection of tools, algorithms, and machine learning approaches used to uncover hidden patterns in enormous volumes of data. But how does this study vary from the work of statisticians for years?
The key is to understand the difference between explaining and predicting.
A Data Analyst usually explains what's going on by going over the data's history, as shown in the diagram above. A Data Scientist, on the other hand, not only does exploratory research to unearth insights, but also uses strong machine learning algorithms to forecast the occurrence of a future event. A Data Scientist will look at the data from a variety of angles, including ones that have never been considered before.
To create decisions and predictions, Data Science employs predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.
Predictive causal analytics - Predictive causal analytics is the way to go if you want a model that can predict the likelihood of a given event occurring in the future. The possibility of consumers making future credit payments on time is a problem if you're lending money on credit, for example. Based on the customer's payment history, you can construct a model that employs predictive analytics to predict whether future payments will be on time or not.
Prescriptive analytics: If you want a model with the intelligence to make its own judgments and the ability to change it with dynamic parameters, you'll need prescriptive analytics. In this still-developing industry, it's all about providing direction. To put it another way, it not only predicts, but also provides a set of predetermined actions and outcomes.
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Google's self-driving car, as I previously discussed, is the best illustration. Data collected by vehicles can be used to train self-driving automobiles. Algorithms can be used to add intelligence to this data. As a result, your car will be able to make decisions such as when to turn, which course to take, and whether to slow down or accelerate up.
Machine learning for forecasting - Machine learning algorithms are your best bet if you have financial transaction data and need to construct a model to estimate future trends. This is an example of supervised learning. It's called supervised learning since you already have the data to train your robots with. A database of fraudulent purchases, for example, can be used to train a fraud detection algorithm.
Machine learning for pattern discovery — If you don't have the parameters for making predictions, you'll have to look for hidden patterns in the data to make accurate forecasts. This is an unsupervised model because there are no defined labels for grouping. The most widely used pattern identification algorithm is clustering.
Assume you work for a telecommunications company that has to establish a network by erecting towers over a region. Then you may use the clustering technique to figure out which tower positions will give everyone the best signal strength.
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Why Data Science?
Previously, the data we had was mostly structured and small in size, and it could be analyzed using basic business intelligence tools. Today's data is generally unstructured or semi-structured, in contrast to data in previous systems, which was mostly structured. Look at the data patterns in the graph below, which shows that by 2020, more than 80% of data will be unstructured.
Financial records, text files, multimedia formats, sensors, and devices are only some of the sources of this data. Simple BI solutions are unable to handle such a big volume and variety of data. This is why we need more intricate and powerful analytical tools and algorithms to process, evaluate, and extract relevant insights from it.
Data Science's popularity isn't just due to this factor. Let's look at how Data Science is used in a variety of sectors.
What if you could deduce your clients' exact requirements based on current information such their past browsing history, purchasing history, age, and income? You presumably already had all of this data, but now that you have a wider and more diverse set of data, you can train models more effectively and provide your clients more specific product recommendations. Wouldn't it be wonderful if it meant more business for you?
Consider an alternative scenario to better understand the value of Data Science in decision-making. What if your car could drive you home on its own? Self-driving cars use live data from sensors like radars, cameras, and lasers to create a map of their surroundings. It makes decisions based on this data using complicated machine learning algorithms, such as whether to speed up, slow down, overtake, and where to turn.
Who is a Data Scientist?
There are several definitions available on Data Scientists. A Data Scientist is a person who specializes in data science in simple terms. After it was discovered that a Data Scientist collects a large amount of data from numerous scientific fields and applications, such as statistics and mathematics, the term "Data Scientist" was coined.
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What does a Data Scientist do?
Data scientists are experts in specialised scientific fields who apply their knowledge to complex data problems. They work on a wide range of subjects, including mathematics, statistics, and computer science (though they may not be an expert in all of these fields). Many companies are presently being digitised. Leading companies have already used this technique and are receiving enormous economic, financial, and customer satisfaction gains. Staying up to date on the latest news and information concerning digitalization is important for anybody who wants to establish a company or transform an existing one to a digital one.