Data science is the study of data. Like biological sciences is a study of biology, physical sciences, it’s the study of physical reactions. Data is real, data has real properties, and we need to study them if we’re going to work on them. Data Science involves data and some signs.
It is a process, not an event. It is the process of using data to understand too many different things, to understand the world. Let Suppose when you have a model or proposed explanation of a problem, and you try to validate that proposed explanation or model with your data.
It is the skill of unfolding the insights and trends that are hiding (or abstract) behind data. It’s when you translate data into a story. So use storytelling to generate insight. And with these insights, you can make strategic choices for a company or an institution.
We can also define data science as a field which is about processes and systems to extract data of various forms and from various resources whether the data is unstructured or structured.
The definition and the name came up in the 1980s and 1990s when some professors, IT Professionals, scientist were looking into the statistics curriculum, and they thought it would be better to call it data science and then later on data analytics derived.
But the biggest question and confusion in the world is what is Data Science?
I’d see data science as ones and from one to many attempts to work with data, to find answers to questions that they are exploring. On summarizing all, we can say that it’s much more about data than about science . If you have proper or improper data, and you have curiosity for working with data, and you’re manipulating it according to your needs, you’re exploring it according to your needs, the very exercise of going through analyzing data, trying to get some answers or fulfill the society need from your explored, manipulated and exercised Data – it is Data Science.
Data Science is relevant today because we have millions of data available on single data or for single data. We didn’t use to worry about the lack of data. Now we have tons of data. In the past, we didn’t have defined algorithms, now we have algorithms. In the past, the software was not affordable by everyone because it was too expensive, so only industries with big-bucks can use it but now it is open source and freely available. In the past, we didn’t even think about storing a large amount of data, because the storage facilities are also very costly and now it is available for a fraction of a cost, we can have gazillions of data sets for a very low cost. Also, the internet connectivity was not common and too costly. So, the tools to work with data, the variability of data, the ability to store, analyze data and last and most important Connectivity, it’s all cheap, it’s all available, it’s all ubiquitous, it’s here. There’s never been a better time to be a data scientist than now.
Who is Data Scientist ?
Is he/she someone struggling with data all day and night or experimenting in his/her laboratory with complex mathematics? After all, ‘Who is a Data Scientist’?
There are many definitions available in the market on Data Scientists. In simple words, a Data Scientist is one who knows and practices the art of Data Science. The super popular term of ‘Data Scientist’ was coined by DJ Patil and Jeff Hammerbacher. Data Scientists are those scientists who crack complex data problems with their strong expertise with certain scientific disciplines. They work with many elements related to mathematics, statistics, probability, Quantitative and Qualitative forecasting, computer science, etc. (though they may not be an expert in all these fields).
We can say that Data Scientists are Business Analysts and Data Analysts, with a difference!
Though the initial training or basic requirements are similar for all these disciplines,
Data Scientists require:
Just like an agricultural scientist wants to know the percentage increase in the yield of wheat this year as compared to last year’s (also the reasons associated with it) or if a financial company wants to classify its customers based on their creditworthiness (before granting loans) or whether a retail organization wants to reward extra points to its loyal customers, all need data scientists to process large volume of both structured and unstructured data in order to make crucial business decisions.
In today’s dynamic and vast world, the main challenge that today’s Data Scientists face is to find solutions to the existing business problems and above it, to identify the problems that are most relevant and crucial to the organization and its success.
Why Data Scientists are called ‘Data Scientists’?
The term “Data Scientist” has been in existence after considering the fact that a Data Scientist collects a huge amount of information from the scientific fields and applications whether the information is statistical, mathematical or computer science. They make use of the latest technologies and tools in finding the solutions and reaching the conclusions that are important for an organization’s growth and development. Data Scientists present the data in a much more useful form as compared to the raw data available to them from structured as well as unstructured forms.
Just like any other scientific pieces of training, data scientists always need to ask and find answers of What, How, Who and Why that data available to them. They are required to make a clearly defined plan and work towards achieving the results within a limited time, effort and money.
- Classifying data using Support Vector Machines(SVMs) in Python
- Data Preprocessing for Machine learning in Python
- Analysis of test data using K-Means Clustering in Python
- ML | Introduction to Data in Machine Learning
- ML | Understanding Data Processing
- Data Cleansing | Introduction
- Basic Concept of Classification (Data Mining)
- Processing of Raw Data to Tidy Data in R
- Classifying data using Support Vector Machines(SVMs) in R
- Multidimensional data analysis in Python
- Redundancy and Correlation in Data Mining
- Box plot and Histogram exploration on Iris data
- Exploring Data Distribution | Set 1
- Exploring Data Distribution | Set 2
- Exploring Categorical Data
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