What is Data Science?

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At the present time, the world is entering in the era of big data and the need of having storage for it is also growing in data science. It has become the major challenge and concern for the big organizations and enterprises to handle the big data until the year 2010. Now various frameworks have become arrived in the market which are handling and solving the problems of big data successfully and now the major focus of the world has been shifted towards the processing of data. And amazingly here comes the data science in the action and it is a secret sauce which can spice up the data world amazingly.

As a tech person or a data science consultant, it can become really hard for someone to explain what data science is actually. Well, in this article you will be able to understand what data science is and what it can do in the digital world and how it can be helpful for the companies to handle big data and process that big data efficiently. So, let’s start from the beginning by explaining what is data science?

What is Data Science?

Data Science is a multidisciplinary mixture of algorithm development, technology and data interface which can be used to solve complex problems analytically. The use of this term has been much common at the present time but a lot of people are unaware of the fact that what does data science actually mean. Well, Data science is a blend of different algorithms, machine learning, tools, and technology with a common goal to discover all of the hidden patterns of the data from the raw information which we have collected.

The core of data science is obviously data. Troves of underdone information, streaming and storing this in the data warehouses of enterprises. Many of the capabilities to learn to mine this data and then building the advanced competencies can be built by this. And if we talk about Data Science for the enterprises then it is an ultimate way to use the data of organizations in different creative ways to generate the business value in an effective way. 

Well, within an organization a data analyst commonly explains the fact of what is being done by processing the history of the present data. While on the other hand, Data scientist is responsible of explanatory analysis of this data to discover useful insights from it and will utilize some advanced machine learning techniques and algorithms to identify that which particular event could occur in the future. Data Scientist will responsible to understand the data from various perspectives and in many of the cases these angles or perspectives are not defined earlier. Therefore, data science is basically using machine learning, prescriptive analytics, and predictive causal analytics to make better predictions and decisions about the future of any organizations.

Why Data Science is needed?

According to the recent statistics 2.5 quintillions (1018) bytes of the data is being generated on a daily basis. At the present time we are living in a digital world where everything around us collecting or generating a huge amount of data on a regular basis including Social media sites, Business transactions, Location-based data, Sensors, Digital photos, videos and Consumer behavior (while online or in-store transactions), etc. And due to this huge amount of data more and more databases are being created and cloud-based storage systems are being widespread.

In the beginning, the data was of enough small size which could be handled with the simplest Business Intelligence tools with ease and most importantly, data in that time was highly structured but as the data is increasing in its size, it is becoming unstructured or semi-structured and it may be because this data is being gathered from various sources into different forms. And with this hugely increased amount of data, everyone is looking for better predictability, customer satisfaction, great user experience, Data prevention, and Data forecasting, etc. And simple business tools are not capable anymore to handle this data to make better decisions anymore. And to manage this complex and big data we need more advanced and complex analytical tools along with more powerful algorithms to design, process and analyze this data to draw meaningful insights in the best possible way. And this is why data science is becoming much popular with every passing day.

What is the Life Cycle of Data Science?

Do you want to know about the major phases of the life cycle of data science? Then having a look at the following various phases of the data science is surely going to be beneficial for you to know:

Before going deep into the project, it is highly important to understand the requirements, priorities, required budget and then frames the business problem to formulate the initial hypothesis. Then it is time to start the process as following.

1.    Obtain data from various sources: Obtain the data which you need according to the nature of your project from all of the available resources and in this regard, you can process your queries into various databases and get the data in file formats for further processing.

2.    Scrub Data to filter: after you have gotten required data, now you have to apply various filters to scrub the data to make it clean and this is because if you are not going to filter data then the results of your data analysis can alter too.

3.    Explore data to find results: Once the data you have is filtered and ready to use than before jumping into the AI processes, you have to examine the data by exploring it. So that you can help the business to figure out the questions.

4.    Build useful data models: This is the phase where actual magic will happen and to reach this magical step you have to keep in mind that scrubbing and exploring data is equally useful to build useful data models which can be predictable and must have the ability to generalize the unseen future. 

5.    Interpret data to see the unseen future: this is the most important step of your entire project. The model line which you have developed here will be interpreted to present it perfectly even to a layman effectively. The results of interpretation would be delivered in the forms of answers to the question of the organization. 

Data science is becoming important in our increasingly digital world. And this is being used in every angle of our society to make better decisions and predictions on the basis of raw data which organizations have.

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January 9, 2020

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