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To begin with let us again understand what constitutes Data Sciences
In concise, Data Sciences is a combination of both technical expertise and nontechnical expertise, along with Contextual expertise, which is knowing what to implement and how to implement.
That being said there are many different roles for different specialist to play in this field. In fact, this is one area where all have got a role to play to achieve the final objectives.
The different Roles available in Data Sciences
Engineers: The people who administer databases, the things that happen in the back end.
The Big Data Analyst: The people who specialize in Math and Computer sciences, more so in developing the Machine learning Algorithms. The Big Data Analysts are also the people who develop Data products, giving the user the information as to which car to buy, which phone to purchase, which route to use, which hotel to stay, etc.
The Researchers: The people who handle domain specific research. For example you are interested in using Protein powder to increase your muscle mass, you may have specific concerns.
Though you want to use Protein powder, you are looking for a product which is Naturally derived. Products which would least impact the kidneys. So, the final solution that you are looking at should aim at addressing these concerns.
Analysts: This comes at the bottom of the table. These are the people who would work with structured data and help in the day to day functioning of a Business.
To effectively function in this field, it isn’t necessary to have an in depth knowledge of Data Sciences, just the application of structured data is sufficient.
Business people: These are people who speak the language of the Client’s requirements. In other words, they should be able to spell out the requirements of the business in crystal clarity so as to enable the specialists to produce the product that addresses the requirements.
These people should be Data literate, be able to understand the solution given and more so be aware of its working principle.
Entrepreneurs: These are the people who often individually run businesses and are all the time seeking solutions to increase their business productivity.
Minimum skill sets required to complete a Data Sciences project
Any Data Sciences project cannot be completed individually. Hence we need a working team which would have the following expertise.
- Coding-Machine learning/Languages
- Graphics-interpretation and presentation
- Math and Statistics intelligence
- Business Skills/ Business specific domain knowledge
- Project management
Introduction to Big Data
Let us talk about Big Data. We all know, the driving force for Data Sciences is Data, but it is imperative to understand that Big Data is totally a different field altogether.
When Big Data and Data Sciences intersect, we have what is called as Big Data Sciences.
Now let us learn what characterizes Data as Big Data?
Any Data group can be classified to be Big Data, if it has the following attributes.
- Volume: Sheer volume of data. For example you ought to recognize all of the people passing through a certain airport terminal.
- Velocity: The frequency at which the Data is flowing. Airport immigration check is the best example. The place is always flooded with People.
- Variety: the data is classified based on different types. Again, the above example is appropriate. People of different facial topography, color, race, composure, all abound.
- Veracity: the incoming data ought be genuine.
Big Data and Statistics
Like in the case of Data analytics, here too we extract information by using Statistical tools and producing Statistical measures that would enable us to visualize the insights that the Data provides.
Big Data and Machine learning
we all know that the birth of Machine learning was largely due to the availability of cheap computational powers, and the field is comparatively new.
In other words, if there weren’t any Machine Learning, then these Big Data ought to be only analyzed by Statistical tools to say the least.
The usual Machine learning and Data: The data is fed into the Algorithm, the pattern is obtained and then the pattern is used on any new data.
The Statistical Machine learning: This branch comes into play when dealing with Big Data. This field of Machine learning uses Statistics and computers sciences concepts and tools to draw insights from Big Data.
The following are the list of Skills required to becoming a Machine learning Expert. We shall discuss each Branch in detail.