Data science is the study of data.It involves developing methods of reporting,storing and analyzing data to effectively extract useful information to make informed decisions.The goal of data science is to gain insights and knowledge from any type of data both structured and unstructured.Data science is being used across different industries already.
Stages involved in data science process
The following are the steps involved in the data science process.
Define the Problem
Before you get into data you need to first define what your business problem is.What are you trying to achieve and what are the different parameters on which your end results depend on to.
Obtaining the Data
Part of the process involves thinking through what data you’ll need and finding ways to get that data whether it’s querying internal databases or purchasing external datasets.Collecting data takes 19% of the total time spent in the entire process.
Cleaning the Data
This process is for us to clean and filter the data.In this process you need to convert the data from one format to another and consolidate everything into one standardized format.A few common things to Jack are missing values,corrupted values such as invalid entries,time zone differences and data range errors.Data cleaning itself takes around 60% of the time for the entire process.
Exploratory Data Analytics
All you will need to inspect the data and its properties: different data types like numerical data,categorical data,ordinal data and nominal data etc require different treatments and the next step is to compute descriptive statistics to extract features and test significant variables and observed patterns.
It is the process of producing a descriptive diagram of relationships between various types of information that are to be stored in a database something as simple as an online transaction can be broken into items sole, user demographics and time of sale etc.The goal is to create the most efficient method of storing i formation while still providing for complete access and reporting.It is a crucial skill for every data scientist.
It is a graphical representation of any data or information visual elements such as charts graphs and maps are the few data visualization tools that provide the viewers with an easy and accessible way of understanding the represented information.It allows you to easily grasp information,establish relationships between elements,intuitive personalized updatable data.
Reasons to become data scientist
The following are the reasons to become data scientist
Big Data Explosion
We are living in the age of information the internet has made it easy for anyone to gather whatever information they want to know.Did you know that you could even learn how to build your own plane or a car just online you can think of it in a way that would was only available to those who could afford higher education is now available to anyone who could get an internet connection.Now it is totally or new that how you are going to use this tremendous amount of data to your benefit.Now if you want to succeed in this world you must study the trends by looking at the analytical data available you can determine different ways through which you can make a good moves to achieve positive result.We have around six 39,000GBS of global IP data which are being transferred in every single minute.Just imagine how big the size of the data is and how much insights you can gain out of such tremendous amount of data but if you want to gain insights of your data first you need to study data science.
Multiple job designations
Once you have to become a data scientist you have an ample amount of opportunities that you can use it to get yourself a brighter future so here is a list of titles that you can apply for in any renowned companies an organization with vaccines so we have data scientist,metrics and analytics specialist,data analyst,big data engineer and data analytics consultant companies such as IBM,Oracle,Google,Amazon and Facebook etc need of data science analyst.
Start your own start-up
The ultimate goal of data science is to extract knowledge or insights from the data to inform and improve decision-making so the easiest way to go after a start-up ideas related to data science is to think of fields or industries where poorly informed decisions are currently being taken to the lack of better alternatives just for idea you can start data monetization business.The business executors knows the value of data and as the void continues to be instrumented with sensors and real-time analytics data will continue to be a booming business perhaps the most obvious way to monetize the data is simply to sell it to other organization which are in need of organized and insightful data for such business you need to embed the data along with a tool for analyzing it in the products and services they sell.
Uses of data science
Data science can be used in prediction of disease outcomes,efficient resource management,automation of self-driving cars and most importantly in education etc.
Salary of data scientist
The average salary for a data scientist is about $117,212 per year.