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[Natashia Bryant]

One common misconception among people about data science is that it is all about a single discipline. But the actual fact is that it is a blend of the various disciplines which are interconnected. Hence, it is interdisciplinary.

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It is the science of using various scientific techniques to extract useful data in various forms from a large pool of information. Speaking of forms of data, the data may be structured or unstructured. This process is called data mining.

Is Data Science Worth Extra resources The Hype?

Why not? According to Harvard Business Review of 2012, Data Science has been called the sexiest job anyone could get in the twenty first century. It has developed a lot in the recent years and there have been a significant increase in the number of jobs and vacancies in various local firms and multinational families due to the current increasing demand in data science in the information technology sector.

How is Data Science Related to Statistics?

It has not only brought a boon in the sector of information technology, but has also influenced the business sector to a large extent. There has been a noticeable rise in the job openings in the business sector as well. It is very closely linked to statistics. In fact, some data scientists have asserted that there is no difference between it and business statistics. According to them, they are the same. But, apart from that, there are some critics who have tried to belie the aforementioned assertion by stating that data science is just a redundant term that has arisen out of business analytics itself. But, the bottom line is that both data science and business analytics employ various scientific and non scientific techniques. Both of the things include using various scientific and non scientific methods to extract out and analyze data and use it in various contexts. Hence, it can be safely concluded that they are indeed very closely linked to one another.

Machine Learning

Machine learning is a very important aspect. Making a machine learn is something which comes from feeding the machine with data only. Hence, there are various aspects, but it is the data scientist who has to decide where his or her interest lies and where he or she should specialize. Machine learning is a very vast topic, yet it is just a fraction of data science. This can give one a clear idea as to how wide and vast the field of data science is. Also, machine learning is also divided into various subparts like artificial intelligence, also abbreviated as AI. It gives a computer an ability to communicate with the user and hence do the necessary task. One needs to be good at programming to be a good data scientist. For machine learning, programming in python is preferred mainly. But it is totally up to the user as to which programming language he or she wants to code in.

Why Should One Study Python For Data Science?

It is no doubt that python is one of the best suited programming languages when it comes to a data scientist. It has been spoken of time and again that Python is the most common programming languages in case of computing. But often times, the question of why one should study this language comes into view.

Here is why, you should learn Python, if you want to venture into the field of data science - Because Python is a flexible language, it is free and powerful along with being an open source language. The language divides the development time in half by its simplistic as well as makes it easy to read the syntax. With the help of python, one can perform manipulation of the data, analysis of the data as well as carry out data visualization. Python brings to one libraries that are essential for the applications of machine learning as well as other scientific processing of data.

The best part about learning Python is that it is a high level language that is quite easy to learn and is procedure oriented along with being object oriented.

What are the basics in Python?

For delving deeper into the programming part, one needs to have a basic understanding of some topics so that they can reach a mastery over programming. Some of the topics required for this are inclusive of - Variables: This term 'variables' refer to the memory locations that are reserved just for the purpose of storing values. In case of Python, one does not need to announce the variables even before making use of them or even announcing their type.

Operators: With the help of operators, one can push around the values of the operands. Python comprises of a list of operators, they are inclusive of - Logical, Arithmetic, Identity, Membership, Bitwise, Assignment, as well as Comparison.

Loops: For the purpose of going over the small parts of coding again loops are used. There are about three kinds of loops, these are for loops, while loops and lastly nested loops.

Types of Data: Python backs up numerous kinds of data types, these describe the operations that can be possible onto the variables as well as the storage method. The different types of data types are inclusive of sets, numbers, dictionary, strings as well as lists.

Functions: The purpose of functions is to break down the code into blocks that are useful. Thus, allowing one to authorize the code, as well as transform into a form that is readable, so that it can be reused from time to time. Thus, a lot of time is saved in this process.

Conditional Statements: Conditional statements are the type of statements that assist in the carrying out of a set of standards. All of these sets of standards are based upon a certain condition. There are about three conditional statements, these are inclusive of Else, If and Elif.