What is Data Science

Data science is a blend of data, technology, algorithms and machine learning in order to find out actionable data insights. Data science is used in making decisions by taking the assistance of predictive casual analytics, prescriptive analytics and machine learning.

Who is Data Scientist

A Data scientist is one who practices the art of data science. Data scientists are capable of solving complex data problems with their expertise in various technological and scientific disciplines. They are called data scientists because they draw a lot of information from numerous scientific fields and applications related to statistics and mathematics.

The skills needed for data scientists are:

• Mathematical knowledge – Acumen of mathematics is as important as statistics itself because you may have to apply various quantitative techniques for finding solutions for the business. There are various data sets which need to be evaluated through a quantitative lens. Summing it up it will be helpful for data scientists if they have prior knowledge of mathematics
• Technological skills – Excelling in data science is not a piece of cake as it takes a lot of patience along with a deep understanding of various technological aspects in order to find out clever solutions to the problems. Data scientists will be needing technology to unleash the information from massive amounts of data set. Knowledge of coding is a must and various other programming languages can also be an additional feather in one’s cap. One can learn to code if there is a passion in a heart to do but one thing to note here is that learning a programing language is not a piece of cake as it requires a lot of patience as in the beginning mistakes will happen but everything will be good at last
• Business acumen – Shrewd in devising new ways of dealing with different aspects of technology with a keen interest in the business should be the priority for data scientists as they should be able to develop a business strategy as data once obtained must be put in effective use. Apart from it, the statistical background must be solid and the ability to comprehend between graphs, charts should be quite good as in dealing with various clients you will be needing it

Apart from these major skills some other skills which data scientist needs are:

• Innovative in applying various techniques to extract the data
• Ability to locate data sources
• Research statistical methods
• Knowledge of data mining techniques
• Deep understanding of trends in data analysis

There are majorly three components of data science and which are-

• Organizing the data – The data needs to be organized before handling it as it makes the task a lot more easier
• Packaging the data – After organizing the data, the data needs to be modified in a presentable from
• Delivering the data – Last one is to deliver the data to the required set of audience

Introduction to R programming

It is a programming language developed by Ross Ihaka and Robert gentleman in 1993. It includes machine learning algorithms, regression, time series. It is used for performing statistical operations and it is a command line driven program and each command is executed one at a time.

Benefits of R programming language

• It is taught in various academic institutions so that students should be industry ready
• With the learning of the R programming language, one can able to create packages, objects, functions
• Can be applied to all operating systems and it is a statistical language which is an open source
• It is used for statistical, data analysis and machine learning. Moreover, it allows hypothetical testing to validate statistical models and assists you in developing predictive models that make use of machine learning algorithms to find the occurrences of future events
• Its features includes database input,exporting data,variable levels,etc.
• It has effective data handling and storage facilities
• It can print the reports of the data analysis performed either in hard copy or in graphical formats
• It assists in various aspects of finance and other major fields.
• It can be used anywhere it is an open source but it is low-level programming language which is not that easy to master if you don’t have any solid background in statistics. Moreover, it can save cost for many businesses

Introduction to the Python language

A programming language created by Guido van Rossum in 1989. It is one of the simplest programming languages which one can learn. It is an open source and free programming language. Python uses newlines to complete a command and is separate from other programming languages which use semicolons.

Features of Python

• It is an open source programming language which can run on various platforms like windows, raspberry, Linux etc
• It has a large standard library which has some useful codes
• It supports generators and list comprehensions and has automatic memory management which means that the memory is cleared and free automatically
• It supports structured, functional as well as OOP
• No compilation step in python and testing is fast
• It is a readable language which is free to download and use

Benefits of Python

•  It can be used on a server to create web applications and can also create workflows alongside various software
• It can handle big data and can perform complex mathematical problems.
• Also, be used for prototyping.
• By using Python, data analysis and data visualization can be developed.
• Embedded Applications and games can also be developed using python.
• It can assist in building artificial intelligence algorithms.
• Can perform various statistical programs .

Many machine language learning applications can be developed using python