WHAT IS DATA SCIENCE? 



  • Data Collection 
  • Data Preparation 
  • Exploratory Data Analysis
  • Data Modeling 
  • Data Visualization
  • Model Deployment


Data science is a process combined with multiple fields, including data collection, cleaning, preparing, manipulating, exploring, artificial intelligence, and advanced analysis. It searches the hidden problem statement and uses them to create a solution.

Data Science uses the techniques like Machine Learning, Deep Learning, and Artificial Intelligence to extract meaningful information and predict future outcomes.
The data science field is becoming more sophisticated and exceptional with the rapid increase of Data in the world.

For instance, Collecting data from an organization, analyzing the performance of the employees, and see the achievement professionally.

The different steps of Data Science are as follows.

Data Collection 

Firstly, we search the different types of data like structured, semi-structured, and unstructured data in the Data collection process. We collect data from API, web search, online repositories, and databases.


Data Preparation

Secondly, we clean the inconsistent data, duplicate and missing values, misspelled words in a systematic format. Then, the cleaned data prepare for further data analysis. 

Exploratory Data Analysis

When Data reaches to perform statistical analysis, predictive analysis, and many more useful insights, it is free from missing values and errors.

Data modeling

In the Data modeling stage, machine learning tests and trains the dataset and performing the best model. Here, we can apply machine learning techniques like KNN, decision tree, and Naive Bayes.
 

Data Visualization

In the data visualization process, We present our data into the visual context like graphs, charts, and maps.

Model Deployment

Model deployment is a process that makes a data-based practical decision integrating a machine learning model into an existing environment.
These are the entire process of data science. 

TOP 10 Real-World Applications of Data Science

  • Social Media Platforms  
  • Email spam 
  • Health care 
  • Banking & Finance 
  • Search Engines  
  • Website product Recommendations 
  • Airline Route Planning 
  • Gaming 
  • Education 
  • Space exploration

Nowadays, various sectors are using Data Science to extract the information they need to create different services and products.

Social Media Platforms

Social media platforms like LinkedIn, Facebook uses data points from its users, to provide them relevant digital services and products.

     Email spam 
     The unwanted emails are called spam. Machine learning methods are used to detect and filter spam emails successfully. For example, we have a data set of 10 thousand emails, some are classified as spam and some are not spam. The Machine learning model train and tests the data then predict whether the email is spam or not.

Health care

The healthcare sector gets great benefits from data science applications like medical image analysis, Genetics & Genomics, Drug Development, Virtual assistance for patients, and customer support.

Data Science helps to detect tumors, cancer, artery stenosis, and organ delineation using Machine learning methods and support vector machines(SVM).

Data science application has enabled an advanced level of research in genetics and genomics. The biological connection between genetic issues, DNA, diseases, and drug response can be understood using Data science. Data science can be predicted advanced genetics risk.

Data science helps to simplify the complicated process of drug discovery and reduce the time of testing, and expenditure.

Chatbots provide basic healthcare support using Artificial Intelligence-powered mobile applications. It simplifies your medical condition, question, and receive key information about your medical condition. It helps to remind you to take medicine in time and assign an appointment with a doctor easily. This approach provides you a healthy lifestyle and saves time to take an appointment in the queue.




Banking & Finance 

We solve complex real-time financial and banking problems like stock market predictions using analysis and forecasting in  Data science. We use Deep learning methods also with LSTMs for obtaining accurate predictions on the future of businesses. We can prevent fraud and thieves using artificial intelligence in financial systems.
The many applications of Artificial intelligence include Algorithmic Trading in finance. The use of complex Artificial intelligence systems solves trading decisions at a glance that is much faster than any human. Artificial Intelligence is capable of making millions of trades in a day without the intervention of any human.

Search Engines 

Search engines like Google, Yahoo, Bing, Ask, AOL uses Data Science and provides relevant search recommendations when the user types a query. 

Website product Recommendations 

     If we take the example of Amazone, we get the suggestion of the same product that we have searched for before. Thus Amazone recommends the related product list and converts the customer to buy that product. This is possible because of the use of Data Science algorithms. Another example, Netflix recommends movies according to the user's watch history.

Airline Route Planning

Airline industries are known as loss Industries across the world except for a few airline services. Now using Data Science, Airlines can predict the following services.

  • the delay of the flight
  • Identifying potential customers and offering discounts to them
  • Decide to buy the class of the airplane
  • setting the cost of the flights as per seasons

Gaming

Most Games are now designed by using Machine Learning  Algorithms. Now machine learning algorithms improve/upgrade the player moves up to the next level.

Education


Using the Data Science model in the education systems, we solve their problem and understand the different types of students. There are enormous student data like academic records, results available in schools, colleges, and universities. Data science methods help to enhance student learning.



Space exploration

We require a vast amount of data in space expedition and discovery. Artificial intelligence and machine learning are the best models to process data. Now astronomers are used Artificial Intelligence to identify the distance between eight planet solar systems. The future of astronaut research is artificial intelligence which is a part of Data Science.