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.

For a complete beginner, what is data science? 

Data science is an exciting field,there is something for everyone. There is a little bit of math, a bit of coding, visualization, and communication. There are a lot of ways to be a Data Scientist. If you are feeling overwhelmed at all, start with one thing. Pick one type of math or one Python concept and then keep moving up. Little steps every day. 

The data portion is developing algorithms that will help us learn about our world in a more structured way. We're essentially looking for patterns. “Data" doesn’t just mean numbers. Data   could be a lot of unstructured things like text, images, or audio files. 

The science part is how we make sense of data. We found out pretty quickly that you can't put data into black-box algorithms. We need to take a scientific and rigorous approach to design the setup, ask the right questions, and challenge our hypotheses. All of that falls under the scientific approach. 

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.

For data science, the components are math concepts. We are not playing around with data only for the fun of it.We are solving real problems that a user or customer is facing. We often have to be creative with how we solve those problems to meet an overarching goal. That creativity can be in data sourcing, feature engineering, or how we're going to combine those results. Any of those things require a high level of creativity.  

If you're a strong programmer, focus on math first. If you're strong in math, focus on coding

To become a data scientist, you must have a strong understanding of mathematics, statistical reasoning & at least one programming languages especially python . 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.  

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.