What is Data Science?

As big data came into the picture, storage has become a major concern in the IT world.  This storage has taken as the primary concern since 2010. It is taken as the primary consideration due to increase in rapid exponential amount of data. And we cannot clone this data whenever its utilization was finished. Because, there are many chances for the re utilization of its data. So we need to store this data for future utilization.  An analyst usually filters this data and utilizes this as per the requirement. Do you know "how do analysts filter this data"? Also, Are you aware of "Which algorithm is used to analyze this huge amount of data"? If no read this complete article on Data science and get answers to all these questions.

Let us start knowing about data science through data science definition

What is Data Science and analytics?

Data Science is the blend of various tools, algorithms, and machine learning principles. Its goal is to discover the hidden patterns of data. It is primarily used to make business decisions and predictions.

As mentioned you earlier, data gets generated from various sources. This includes financial logs, text files, multimedia forms, sensors as well as instruments. Simple BI tools were not capable of analyzing this huge volume as well as a variety of data. Hence there is a need for advanced complex and advanced analytical tools and algorithms for processing, analyzing, and drawing meaningful insights of it. So here data science came into the picture with various algorithms to process this huge amount of data. It makes use of predictive casual analytics, perspective analytics, and machine learning.

Get more information on Data Science by live experts at Data Science Online Training

let us have a quick look at those briefly.

Predictive casual analytics:

 If you want a model that can predict the possibilities of the particular model in the future, predictive casual analytics comes into the picture. For example, if you are providing the money on a credit basis,then the probability of making credit card payments on time comes into the picture. Here you can build a model that can perform predictive analytics based on the payment history of the customer to predict the future payments of the customer.

Perspective Analytics:

This analytics comes into the picture if you want a model that has the intelligence of taking its own decisions. In other words, it not only predicts but also suggest the range of prescribed actions and the associated outcomes. The best example of this analytics is self-driving cars. Here the data gets generated by vehicles to train the self- driving cars. You can algorithms on this data to bring intelligence to it. Using intelligence with the data, it can make better decisions in different situations like taking U-turn, car reversing, speed regulation, and so on.

Machine learning for making decisions:

If you have the transactional data of the finance company and need to build a model to determine the future trend then machine learning algorithms comes into the picture. This machine learning comes under supervised learning. It is so-called supervised machine learning because you have data where you can train your machines. For instance, a fraud detection model can be trained using the historical data of the fraudulent purchases.

Who is a Data Scientist?

Data scientists can be defined in multiple ways. One of them is as follows:

A Data Scientist is the one who practices and implements the Data Science art. Data Scientist roles combine computer science, statistics, and mathematics. They analyze the process as well as model the data and interpret the results to create actionable plans for companies and other organizations. Data Scientist were the analytical experts who utilize the skills in both technology and social science to find trends as well as manage the data.

What Does Data Scientist do?

A Data Scientist work typically involves making a sense of messy, unstructured data from various sources like smart devices, social media feeds, and emails that don’t fit into the databases.  A data scientist usually cracks complex problems with their strong enterprises in certain disciplines. A Data scientist usually works with several elements related to mathematics, statistics, computer science, and so on. Besides, these people use a lot of tools and technologies in finding solutions and reaching solutions that were crucial for organization growth and development. Data Scientist presents the data in much useful form when compared to the raw data available to them from both structured as well as unstructured form.

Life Cycle of data science:

The life cycle of data science involves various activities as follows:

a)Discovery:

Before beginning your project, it is important to understand various specifications, requirements, priorities, and required budget. Here you should assess yourself whether you have the required resources present in terms of people, technology, time, and data to support the project. Moreover, here you need to frame the business problem and formulate an initial hypothesis to test.

b)Data preparation:

In this phase, you require a sandbox, where you can perform the analytics for the entire project. Besides, you need to explore, pre process, and condition data before modeling, Besides, you will perform ETL(Extract, Transform, Load) to get data into the sandbox.

c)Model planning:

Here, in this phase, you will determine various methods and techniques to draw the relationships between the variables. These relationships will set the base for the algorithms which will be implemented in the next phase. Here you will apply exploratory data analysis using statistical formulas and visualization tools.

data science life cycle | Kits Online Trainings

d)Model Building:

In this phase, you will develop the data sets for training as well as testing purposes. Moreover, you will be checking whether your existing environment suits get for running the models. Besides, you will also analyze various learning techniques like classification, association, and clustering to build the model.

e)Operationalize:

In this phase, you will deliver final reports, code, and other technical documents. Besides, in some cases, a demo project is also implemented in a real-time project. So with this demo project, you will be getting an idea of the project outcome and also the probable loopholes of the project.

f)Results communication:

We can consider this phase also as an verification phase. Here in this phase, you will be evaluating your project success. i.e checking your goals whether they meet the project requirement (or), not? that was expected in the first phase. Besides, in this phase, you will be also thinking of various findings, communication to the stakeholders and determines the outcome of the project based on the criteria developed in the first phase.

Hence with this, the project of the life cycle of the data science goes on. You people can get the practical working of this data science cycle at the Data Science Online Course. With this, I hope you people have got enough ideas on data science overview, life cycle, and so on. In the upcoming articles, I will be sharing the details of applications of data science in various fields with practical use cases. Meanwhile, have a glance at our Data Science Interview questions and get placed in your dream company.