Data science has been a buzzword in the market for quite some time, it has even been labelled the "Best Career of the 21st Century”. Data Science is a multi-disciplinary study, and heavily utilizes scientific methodologies.
Maths and statistics form the basis of most data science techniques. As a data scientist you will need a good foundational knowledge of these core concepts in order to understand how to perform exploratory data analysis, forecast future events, or apply machine learning and deep learning well. When trying to make sense of data, statistics is an invaluable tool as it wrangles the data in an approachable manner.
To become a data scientist, you must have a strong understanding of mathematics, statistical reasoning, computer science and information science. You must understand statistical concepts, how to use key statistical formulas, and how to interpret and communicate statistical results.It include the key concepts of probability distribution, statistical significance, hypothesis testing and regression. Bayesian thinking is also important for machine learning; its key concepts include conditional probability, priors and posteriors, and maximum likelihood.
Data Science exists at the junction of statistics, business knowledge & technical skills. It is a way to extract important information from structured & unstructured data. Data Science also focuses heavily on being able to derive informed decisions and strategic moves from data often termed as insights. Insights are one of the biggest products of practicing data science and offer numerous advantages.
It’s also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, we’re analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination—charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. This is especially important to note—the “audience” for our output is human.
As a Machine Learning engineer, on the other hand, our final “output” is working software (not the analyses or visualizations that you may have to create along the way), and our “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves.
Machine learning is the natural progression of artificial intelligence using extensive data. In machine learning, we develop computer programs which automatically learn from the data set available to them and don’t need to be explicitly programmed. An example of Machine learning (ML) would be the kind that Uber or Ola use on the ride sharing apps. The app automatically estimates the cost of our ride, the distance between our locations and also the surge pricing depending on various factors. All these are possible due to machine learning capability of the app with the availability of different data like users in the area, past price trends etc.
The world is going through what is popularly called ‘digital transformation’ – and this is revolutionizing the way we live- the way we communicate, consume, use time, and work. A lot of what we know today as work is being taken over by progressively intelligent machines. A career in data science and machine learning makes sure you are also part of the revolution.
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Over the years, data science has become an integral part of many industry like agriculture, marketing optimization, fraud detection and public policy among others.
By using data preparation, statistics, predictive modeling and, data science tries to resolve many issues within individual sectors and the economy at large.
Data science emphasizes the use of general methods without changing its application, irrespective of the domain. This approach is different from traditional statistics tend to focus on providing solutions that are specific to particular sectors or domains.
The traditional methods depend on providing sectors with solutions that tailored to each problem rather than applying the standard solution.
Today, data science has far reaching implications in many fields, both academic and applied research domains like machine translation, speech recognition, digital economy on one hand and fields like healthcare, social science, medical informatics, on the other hand.