In this section we will understand the Data Science and see its importance in today's high performance computing world.In this section we will understand the Data Science and see its importance in today's high performance computing world.
In this section we will introduce you with the Data Science and relate it with the today's requirement in business. We will also see how Data Science is important in exploring today's huge amount of Data which is usually stored in a Big Data environment. So, let's get started with the Machine Learning and Data Science.
Data is very import for any organization as it helps the business leaders to make decisions based by analyzing and predicting from the data. For predicting the future various statistical techniques are used to analyze the data and then find out the trends which might help business in growing profits. Due to huge increase in various types of data and availability of data storing/processing techniques data science came into picture. Data Science is a multidisciplinary field that employs the knowledge of various science steams to process data for business growth. Check Artificial Intelligence Disciplines and learn what all field of science are required for Data Science.
Data Science is combined use of various scientific approaches, procedure, algorithms, and framework in a coherent way to analyze vast amount of data to develop machine learning model for predicting when new data is given to the Machine Learning/Deep Learning model. Data Science is actually used to learn from vast collection of data and finally extract the knowledge which can be used for business for taking action. In Data Science first we have to collect the data and the data should be extracted from structured or unstructured formats. The extracted data is prepared and make it ready for model training. Finally model is trained, tested and deployed if the model is performing as per business requirement. The process of working machine learning model is complex and requires lot of technical skills. Data science is actually uses many techniques such as data mining, statistics, predictive analysis, Machine Learning, Deep Learning and many more to achieve the results.
Let's see what is Data Science? The term data science is probably the single most misunderstood term in the fields of data analysis, marketing and information technology. By definition, a data scientist uses machine learning to make data visualization (or graphics) more transparent and powerful. In a nutshell, a data scientist uses machine learning to make "advanced" predictions about data. These might be more sophisticated mathematical algorithms or human-designed algorithms.
Data Science is a rapidly evolving branch of computer science that deals with modeling, analysis, and classification. Its fundamental premise is that you can learn a new subject by first learning what it does well, and then applying it to a problem. In other words, if you have a set of data, you would like to model them using a machine learning approach that provides insights about the world around you. We have discussed Data Science in detail at: What is Data Science?
On a macro level, machine learning refers to the systematic analysis and learning of large amounts of data in order to better understand the complex nature of data collections. This data-gathering process is part of any business. For example, data analysts gather data from a database, and then use it in order to make business decisions, or analysts analyze their own data from their company smartphone app.
As a data scientist, you can build powerful data-driven business models that help business grow. There're data science tools that help you make good, efficient data-mining decisions, which let you better anticipate what data you're likely to get. But most of the time you will have to code your own Machine Learning model, train with the data, test the model and finally use the model for business use. The installation and use of model in production environment for prediction is called the deployment. You will also need an understanding of the techniques underlying this, the model deployment to successfully use your model for production. So, Data Science is really a big work and it needs lot of technological skills.
Data Science activities involves following 5 steps:
You can check more details on the various steps of Data Science at What is Data Science?
Above steps gives you the clear picture of use of Data Science and overview of the steps in Data Science. Data Scientists spend over 70% to 80% of total time in Data processing.
Data Scientists job is very important for the business and it add value to the business. Due to the nature of the job machine learning is the primary focus of most data science jobs. While data science is becoming increasingly important for the business around the world. Companies are pro actively started using machine learning in the enterprise to find new opportunities and run their business added with machine learning. So, large companies are taking help of experts to analyze large data sets and get business values out of their data.
The use of Data Science in business is not new, but it is now being used in much expanded way due to availability of large amount of data and the availability of huge processing power. Large amount of data can be stored in Big Data environment and then distributed processing engine can be used for processing of data. Model can be trained on the large cluster to learn from data. Business is using Big Data and Data Science to analyze their data, these businesses reported quantifiable benefits of using these technologies.
So, Data Science plays an important role in today's world and use of Data Science provides numerous benefits to the business.
The first step towards Data Science is to collect the data either from traditional sources or from Big Data. Traditional sources include relational databases or text, csv, excel files etc.. Big Data sources may include text, columnar data, videos, images, server logs and other data file formats. Data received from all these sources must be cleaned, formatted, validated and then formatted if some issue is found with the data. After data cleaning it is used for training the model with the less quantity of data and the model is evaluated. Once a model is found satisfactory it is trained with the large amount of data. After complete training model and validation it is moved on to the production for prediction. All the steps involved in Data Science are highly technical and require a lot of skills to develop a production ready model.
Now we will explore the use of Data Science in real-life. Data science is the knowledge (of various science field) of how to deal with data so that it can be effectively used in data analysis. Data Science is used by industry to get real insight of organizations data.
The Data Science field is growing fast as more and more organizations started using it for their day-to-day operations. High growth in Data Science became possible due to availability of cheap storage and fast computation hardware. Now data can be processed very fast over distributed clusters such as Hadoop, Spark or GPU clusters. All these innovations in store and computing technologies fueled the growth of Data Science.
As explained above Data Science uses the techniques of Machine Learning, Data Analytics and Deep Learning (Neural Networks) for analysis of different kinds of data to extract the information hidden inside the data.
Here are the top uses of Data Science in the following industries:
Apart from the above use cases almost all industries and research organizations are using Data Science.
Check top Data Science tutorials: