Fun fact — according to a 2011 report, in 2020 the world will generate 50 times the amount of data than in 2011, So with such drastic increase in the data inflow, new tools emerged, to make proper use of the raw data and use it fruitfully.
Data Science covers all such tools, techniques and technologies which help us handle data and use it for our good.
It’s an interdisciplinary blend of Data Inference, Algorithm Development, and Technology in order to solve analytically complex problems.
The 3 components involved in data science are organising, packaging and delivering data (the OPD of data).
- Organising Data — involves the physical storage and format of data and incorporated best practices in data management.
- Packaging Data — involves logically manipulating and joining the underlying raw data into a new representation and package.
- Delivering Data — involves ensuring that the message, the data has is being accessed by those who need to hear it.
Data Science is such a vast and subjective topic of discussion, it is not practically possible to encapsulate it in one single blog. It isn’t an independent field in itself, it is a combination of various fields including Computer Science, Mathematics and Statistics, and Business Strategy.
To gain a better understanding of how everything fits in, take a look at this venn diagram.
Some of the important tools required in Data Science.
Big Data —
Data being collected and our ability to make use of it.
Using big data, retailers can predict what products will sell, telecom companies can predict if and when a customer might switch carriers, and car insurance companies understand how well their customers actually drive, enable us to find new cures and better understand and predict the spread of diseases among other uses.
Machine Learning —
According to Tom Mitchell, machine learning isconcerned with the question of how to construct computer programs that automatically improve with experience.
Machine learning is interdisciplinary in nature, and employs techniques from the fields of computer science, statistics, and artificial intelligence, among others.
The main artifacts of machine learning research are algorithms which facilitate this automatic improvement from experience, algorithms which can be applied in a variety of diverse fields.
Data Mining —
Fayyad, Piatetsky-Shapiro & Smyth define data mining asthe application of specific algorithms for extracting patterns from data.
In data mining, the emphasis is on the application of algorithms, as opposed to on the algorithms themselves.
We can define the relationship between machine learning and data mining as follows: data mining is a process, during which machine learning algorithms are utilized as tools to extract potentially-valuable patterns held within data-sets.
Deep Learning —
Deep learning is a relatively new term, although it has existed prior to the dramatic rise in attention lately.
Deep learning is the process of applying deep neural network technologies — that is, neural network architectures with multiple hidden layers — to solve problems.
It’s like data mining, which employs deep neural network architectures, which are particular types of machine learning algorithms.
Artificial Intelligence —
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
Research associated with artificial intelligence is highly technical and specialized.
The core problems of artificial intelligence include programming computers for certain traits such as knowledge, reasoning, problem solving,perception, learning, planning etc.
Core parts of AI research - machine learning, knowledge engineering, robotics among others.
Where does Data Science fit along with all these concepts?
So taking all the concepts, tools and working into account, we can conclude that Data Science, is the what future holds for us. It’s going to change the world, and in a big big way.
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