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Data science is an interdisciplinary field with wide verities of applications like we discussed in our previous articles data science help scientist to find solutions for their problems. It is one of the main applications of data science, it can be used to solve the common issues that humanity faces like weather change, pandemic management, etc. In this article let’s see how do scientists inspect data and other real life applications of Data Science .
How Do Scientists Inspect Data About A Habitat
In the field of habitat investigation data science can be implemented to learn about the relationship between habitats and biodiversity, Identify common local habitats, Understand the problems with measuring diversity. These are some of the problems solved by habitat investigators.
Traditionally there were many steps involved in habitat investigation, like making a list of different habitats found in the area finding the sub habitats that reside within another habitat, then we make our manual field journal datasheet and then we make valuable insight from the data collected using our common sense and brainwork.
But now things have changed work has become much easier, we even have a python module for habitat investigation. No need for field study as tons of datasets is available on kaggle.com, thus saving money and time.
Crisis Management Using Data Science
We might have noticed the application of data science during COVID-19 Crisis management by nations. During times of crisis, a huge amount of data will be produced like the name of the patient, place of patient, age, and most importantly the patient’s contact data with others. As the number of patients increased inflow of data also increased, which is very hard for a group of people to organize.
Think about WHO they need to monitor the whole world. If data science wasn’t there none of us will ever know the daily report of covid cased, or the most affected region in our country. Data science will help the nation and organizations to categorize regions according to the severity of pandemic or epidemic. Thus instead of giving all the regions the same consideration, more affected regions can be concentrated more, and thus we can make use of our resources efficiently.
Use Of Data Science In Cosmology
This is a field of data science where data is subjected to complex physical and mathematical models and equations. Primarily there are very limited amount of real data available about universe right now, the data that is collected by satellites and other space exploration vehicles are main constituents of it. The remaining amount of data that we have is made up using mathematics and physics.
Back in 2019 NASA published the first image of a black hole taken through an Event Horizon Telescope. The image was similar to an image predicted by a simulating model based on data science. The data used in the simulations was purely made up using physics, and mathematics but the degree of accuracy was really awesome.
Here Scientist lay fundamental rules which all data should obey, it can be laws in physics like gravitation and other stuff. All the data is supposed to obey these rules and primary levels of data will be created after which these data will be used to mine secondary data and this goes on until they meet the demand of simulation. Data collected through satellites can also be used to add extra parameters.
In the future, if the earth becomes obsolete for life or some random events trigger a catastrophic disaster making humans in search of another habitat, we can make use of data science to find a perfect habitat that will meet our needs. But that’s a long way to go.
Application Of Data Science In Marine Studies
One of the main applications of data science is in the study related to bathymetric charts. The term bathymetry primarily refers to the depth of the ocean with respect to the sea level, it is primarily used to predict the depth and shape of underwater terrains. variation in depth is represented using change in color and contour lines known as depth contours or isobaths.
Following are some of the use cases of bathymetric data
To Ensure The Safety Of Mariners
Bathymetric data is analyzed using data science to and the insights produced are efficiently made use to make a nautical chart, which is a chart that is used by mariners, underwater enthusiasts, and literally anyone who goes into the sea. It guides them in the same way road maps guide car drivers. It has accurate information about the depth of the sea and potential underwater hazards.
It is also made use in ships to ensure that the port that they are planning to dock has enough depth so that bottom of the ship will not touch the seafloor.
To Study The Changing Coastline Features
Using data science and marine data we can continuously and efficiently monitor the changes in the ocean. Scientists use this to study the effects of climate change and to monitor beach erosion, sea-level rise, etc. Continuous monitoring of these parameters helps us identify natural calamities in advance and give us enough time to do the preparations to face them.
To Study The Marine Habitats
Bathymetric data along with data science can be used to study marine lives and habitats. Bathymetric data is also used to create maps of coral habitats to assist in conservation and monitoring.
Monitoring Water Body Conditions
Bathymetric data can be used to make hydrodynamic models which can be used to calculate currents, tides, water temperature, and salinity in an area.
How These Data Inspections Are Done
As we can see the application of data science is literally unlimited. Data science can be applied almost anywhere to find solutions to any questions provided there are enough data available to be analyzed. But we haven’t discussed how scientists do these analytics or what programming language do they make use of.
Firstly the data that we have will be made into a tabular form it is known as a data frame. operations are performed in this data frame later to make valuable insights. Each column contains values of one variable and each row contains one set of values from each column. Data scientists mainly use a Programming language called R.
R is a programming language that is designed and used mainly in the statistics, data science, and scientific communities. It is quickly taking a toll over the industry, even its overtaking IBM’s Own SPSS.
The traditional workflow was the raw data was made into a spreadsheet and that spreadsheet was analyzed using SPSS then this is fed into text processing and final results are produced. But with R the steps involved become less and fast. R can be used directly over raw data without the need of converting it into a spreadsheet. This made things much more easier and efficient and more importantly, more iterations can be done over raw data since there is no need for multiple steps. As the number of iterations increases more curated insights can be made.
How Data Is Inspected Using R
The basic workflow of data analysis involves four main steps. They are mainly importing, manipulating, visualizing, and reporting.
Lets take a deeper look into each one of them
Importing the data is the first and basic step involved. We need to obtain the required dataset from sources like Kaggle, or any other dataset-providing platforms and import it into R.
Manipulating data primarily means cleaning it to remove inaccurate, illogical, and duplicate data. It is very important since removing data that will not help us save more time cost and computing power in the later steps.
Visualizing data – This is the 3rd step involved it include visualizing, transforming, and modeling data. This is the step where valuable insights are made by data scientists. The insights are recorded and validated.
Reporting – This is the final step involved in the data science procedure. This step is also known as communication since in this step the data scientist communicates the insights to required authorities.
Realtime Data Analysis Using R
R has a feature called RODBC( R Open Database Connectivity) to extract data from SQL databases. R can be used in real time applications if we can set up a technology stack that can support real-time interactions with models developed in R. Following are the 5 steps involved in real-time predictive analysis using R
- Data Distillation
- Model development
- Model validation and deployment
- Real-time model scoring
- Model refresh
As we can see real-time data analysis is tougher when compared to normal data analysis. The number of steps involved is higher and complex. All the steps in normal data analysis were self-explanatory but here things are complicated.
The complexity is mainly due to the fact that the amount of data is keeping on increasing per second. Consider an example of a predictive model which monitors market data and makes predictions about the stock price. Here the model will be running on market hours and price fluctuations has to be recorded, this creates a continuous inflow of data and we also need to process that too. While in normal data analysis we deal with a specific amount of data that was preset even before data analysis begin.
In this article we have discussed some critical practical implementations of data science, we have discussed the application of data science in various fields like habitat investigation, crisis management, cosmology, and marine life. A lot of effort was put into selecting this wide variety of fields, as a token of appreciation, we request you to kindly share this knowledge with your friends and family.