Data Science - Extract data from insight
Data Science - Extract data from insight
Before diving into data science, we first understand what is data?
Data is some sort of information. It can be the name of a person, images, videos, audios and even social networking log and web records are data.
We know that datalog is everywhere and data is increasing day by day. Now the question arises how to handle these data?
Okay let’s make it more clearer ... Have you ever heard of Uber?
Yes you are going right .The cab service provider ... Uber.
I am sure many of you have used Uber. It seems very simple to book an Uber…
You just have to press a button, set the location, request a vehicle then go for a ride and pay with a click.
But there is a lot going behind all this.
Have you ever thought about what makes Uber a multi billion dollar worth company? And Whether it is availability of cabs or their services?
Well it is Data that makes them rich. But is data enough for a company to grow ? Obviously there must be something else … and that's where Data Science comes into picture.
In simple words we can say that Data Science is the field where we use different- different techniques, algorithms, processes to extract some information from the raw structured or unstructured data.
Data science is just surfacing hidden insight that can help many companies to make smarter business decisions for their profit.
For example - Youtube data mine viewing patterns to understand what drives user interest, and uses that to make decisions on which user interest videos to produce in a sequence.
It can help many companies to grow as they can target interested audiences based on their previous interests.
For example - what are major requirements, your unique shopping behaviours, your choices, what kind of movies do you like etc.
Let us understand this
We all know that there are days like Monday morning when everyone is in a rush to reach their workplaces.It would be difficult to book the cab.
Uber implements Data Science to find out which place is the busiest so that it can implement surge pricing algorithm (the goal of such an algorithm is to increase the number of rides that can be provided).
The Data Science Process - Through Uber
Data Science process always starts with the business requirement or the problem you are trying to solve
Step 1: Understand the business requirements
Well in the case of Uber we need to build a dynamic pricing model that can be implemented when there will be low supply and high demand.
Step 2: Data Science helps to get information very quick
Uber collects the information such as traffic, weather conditions, drop and pick up locations, time, the person who booked the cab etc.
Step 3: Data Science helps to maintain data - cleaning of unnecessary data
Sometimes unnecessary data is collected and such type of data only increases the space as well as time complexity of the problem so such data have to be removed at this step.
Applications of Data Science in real world
Data science is used in search to provide all the relevant results fastly.
Even to read this article may be you have searched for what is data science...and all the results relevant to data science were there on your screen in less than a second ...all this is because of data science.
Do you remember when you upload your images with your friends on instagram you start getting names ideas to tag your friends?
This feature is provided by an image recognition algorithm .
I am sure all of you know about google assistant, alexa and siri and another bot like jarvis, conversational agent etc. Chatbots enable enterprises to make data-driven decisions with ease and efficiency. Instead of having to depend on human analysis for a report, bots can be used to quickly generate analytics responses.They are the best example of data science in our real world.
Health is a relatively new movement that integrates data directly from consumer wearables (Fit bands, Muse headbands, Pedometers etc.), blood pressure cuffs, glucometers, and scales into EMRs through smartphones (Google Fit, Apple’s HealthKit, and Samsung Health etc. ), and can pick up on warning signs faster by tracking changes in behavior and vital signs.
Data science is used in different different fields - in drug discovery, disease prevention, diagnosis and many more.
Data science is used in detecting tumors, artery stenosis, organ description employs various methods and frameworks like Map reduce to find ideal parameters for tasks such as lung texture sorting.
Medical Imaging is the most effective feature of data science in the medical field.
Well this is the app we use every time we go out. Without any obstacle we reach our destination with the help of google maps which are working with the help of data science.
Have you ever wondered that whenever you have searched for something on amazon, flipkart or any other commercial platform? And you just start seeing that search related ads then and now. Amazon's recommendation engines suggest items in your account for you to buy, determined by their algorithms and techniques. Netflix recommends movies to you to watch. Spotify recommends music to you to listen to. Well data science algorithm works behind these advertisements.
Check this out: Top 10 applications of Natural Language Processing
Different areas of Data Science - Data Mining
Data Warehousing or engineering refers to transforming raw data into useful information for further analysis. This involves managing the source of data, storage, quantity and quality so that it can easily be analyzed by other data analysts.
Cloud Computing refers to designing and implementing the enterprise platforms required for cloud and distributed computing all over the world wide. The main role of cloud computing is to analyze system requirements and ensure that systems will be securely integrated widely with the current applications.
Big Data is a field where data can be analyzed and extracted information from, or otherwise deal with collection of data that are too large or complex to be dealt with by traditional data processing approach.
Data Mining refers to the application of statistics in the form of preliminary data analysis and predictive models to reveal patterns and trends in data from existing data sources.
There are many other areas of data science such as cognitive computing, healthcare, finance, insurance data analysis , machine learning, data warehousing etc.
Data science is emerging as a field that’s revolutionizing science and industries.
In every field work is becoming more data driven, affecting the jobs that are available as well as the skills that are required. As more data and ways of analyzing them become available, more sectors of the economy, society, and day to day life will become more dependent on data.
It’s imperative that educators, administrators, and students should begin today to plan how to best prepare for and keep pace with this data driven era of tomorrow.