Introduction to Data Science

What is Data Science?

Data science is actually a blend of various disciplines. These disciplines are data inference, computing systems, machine learning, algorithms and various other scientific methods. All these disciplines work together in order to extract useful information from the pile of data available. It can be said that data science is a form of data mining. In short, this is the process of gaining knowledge about a particular aspect from structured or unstructured data. To put it in very simple terms, data science would be making use of various tools in order to understand a particular actual phenomenon. The input here is a bulk amount of raw data and the output would be processed information. The techniques employed in this field are the ones drawn from various fields, such as computer science, mathematics, statistics, etc. Data science can be broadly divided into two sections:

1. Discovery of data insight:

This section deals with finding out useful trends and patterns from the midst of a lot of data. This would allow companies to make smart business decisions and also companies and individuals would be able to decide where to invest in order to get the maximum returns. For example, a channel like Netflix would mine for what interests their viewers and hence they would be able to launch programmes that are able to attract viewers.

2. Development of data product:

This is the second section of data science. The classic example of this section is the recommendations that everyone would receive on various sites. Also, the companies would be able to take the products directly to the people who actually need them. This would increase the sales and hence the products. You can find an extensive list of Free Data Science Online Courses to learn and master data science skills.

Programming languages used in Data science:

There are numerous languages used in the vast field of data science. Since analyzing data is a process that cannot be done without the help of a computer, there must be some method in order to tell the computer how to analyze the data. Data scientists would write programs in various programming languages and these would take care of the analyzing of the data. Once the program is run completely, the computer would be able to come up with results which are the processed information. The most common programming languages are Python and R programming:

Python:

Python is a high-level programming language. It has automatic memory management feature and it can have different programming paradigms. It was created in 1989 by Guido Van Rossum and was released in the year 1991. rnthe major advantage of python is that the programs would have a natural style. Or in other words, it is easy to read and understand the instructions.

R programming language:

The key advantage of the R programming language is that it is a free software that can be used in order to analyze the different kinds of statistical data available. This is actually a software environment and its source code is written primarily in C, Fortran and R. This environment is used commonly to unlock the different patterns present in the data.

What is data exploration and visualization?

Data exploration is considered the first step in the field of data science. It is actually the process of summarizing the major properties of a particular dataset. This would help the data scientists to understand what the variables are and also how many cases there are in the entire datasheet. In short, the basic aim of data exploration is to get familiar with the data sheets. And data visualization is the process of communicating the data or information in the form of visual objects like bars, pie charts etc. Some of the common data exploration techniques are:

Filtering:

This is the most common exploration technique. This is to analyze a whole data sheet and then develop a subset that has the data points that are related to each other. There are raw filtering techniques and also there are very specific techniques.

Text searching:

This technique is used to bring out all the required data points with ease. This technique is very useful if the data is arranged in the form of a table.

Granularity toggling:

This is useful when the data needs to be viewed at different granularities. Usually, this is used along with date filtering. Date filtering is the process of filtering the data on the basis of the date of acquiring them. The granularity toggling is changing the granularity from day to month or from month to year. This would help to view the trends over a period of time.

Overview of statistics for data analysis:

Statistics is an integral part of data science because, without statistics, one will not be able to confirm that whether or not the pattern they found is real, if that is predictive, what assumptions they are relying on in their analysis, and more. As a result, data scientists need to be well-familiar with every aspect of statistics. There are numerous techniques in statistics used in order to extract information from the data available. Some of the common techniques are binomial distribution, Poisson Distribution, etc. Also, data can be represented in the form of probability density function, continuous and discrete variables and hence the scientists would be able to identify the patterns in the data. Moreover, there are algorithms like clustering methods that can be employed so as to group the data for easy analysis.