Do you think IIT Guwahati certified course can help you in your career?
Introduction
Hey, Ninjas! You must have heard of the term 'Data Science'. It is because of its diverse applications, which range from early detection of diseases in the human body to recommending new web series on Netflix. It can analyse student data, it can also help in predicting congestion on city roads. With enough data in hand, we can use data science to create predictive models to predict future conditions.
But what exactly is data science, and where can we see it? Well, don't worry! In this article, we're going to explore what data science is all about and where it's used. So, Let's start our journey.
What is Data Science?
Till the 60s, data science was just used as an alternative name for statistics, but now it involves three aspects: data design, collection and analysis. Moreover, it is used to extract meaningful insights from business data. To give you an overview, the business data includes statistical information, raw analytical data, customer feedback data, sales numbers and other sets of information.
Data science uses principles and practices from mathematics, statistics, AI and computer science.
Data science applications range from discovering unknown transformative patterns to innovating new products and solutions based on data.
Let us have a look at the Data Science Process.
Data Science Process
It is a systematic approach towards a data science problem. It provides us with a structured way to formulate, solve our problem and finally present the solution to the stakeholders.
Typical data science process involves:
DataCollection: Data is collected from various sources as per the problem requirement.
Data Cleaning: Collected data then gets cleaned. For example, If collected data has a record that contains some missing attribute then the such record will be removed.
Data Exploration: It provides an initial statistical understanding of the data.
Data Modelling: The model is chosen by minimising the error.
Interpret Result: The result from the model can be obtained and used for multiple purposes.
Data Science Applications
There are many industries in which data science has applications.
Following is one such non-exhaustive list of industries:
Sr. no.
Industry
Application
1.
Healthcare
Early disease detection using predictive modelling
Medical Image Analysis for Diagnosis
2.
Finance
Using anomaly detection techniques to detect fraud
Using credit risk analysis for loan approvals
Optimising portfolio for better investment
3.
Retail
Personalised marketing based on customer segmentation
Forecasting of demand and inventory management
Product recommendation system
4.
Transportation
Route optimisation
Real-time analysis of traffic and prediction of congestion on the road
Predictive maintenance for vehicles and equipment
5.
Energy
Forecasting of energy demand
Detecting faults in power grids using anomaly detection technique
6.
Education
Analysis and Prediction of student performance
Personalised learning
Curriculum optimisation and course recommendation system
7.
Manufacturing
The optimising supply chain for Production
Defect detection in the manufacturing process
Predictive maintenance for equipment and machinery
Let’s go through each application one by one:
Health Industry
Electronic medical records, genomic information, and digital body data from wireless health devices are used to prepare data-science models in the healthcare industry.
Following are some of the applications where generated model can be applied:
Early disease detection using predictive modelling It is possible to provide early intervention for diseases like diabetes or cancer by developing a data science model. The model will analyse the patient data. After analysis, It will predict the likelihood of developing such a disease.
Medical Image Analysis for Diagnosis We can use deep learning techniques to analyse medical images, such as X-rays, MRIs and CT scans, to diagnose various conditions.
Finance Industry
Company Reports and Regulatory Filings to the government along with financial databases from organisations like Bloomberg, Capital IQ, and Thomson Reuters are used to prepare data science models in the finance industry.
Following are some of the applications where generated model can be used:
Using anomaly detection techniques to detect fraud We can develop machine learning models to analyse financial transactions and identify suspicious activity. It can flag potential fraudulent transactional activities.
Using credit risk analysis for loan approvals We can analyse customer expenditure behaviour and customer credit history to assess credit risk. This analysis will determine whether to approve or deny a loan application.
Optimising portfolio for better investment We can develop a model for analysing market trends and past performance of different stocks to optimise our portfolio, which can give us maximum return while minimising risk.
Retail Industry
The transaction history of the customer, description of the products, customer reviews, geographical location, etc. are key components to consider while preparing data science models.
Following are some of the applications where generated model can be used:
Personalised marketing based on customer segmentation Data science can be used to analyse customer data. We can segment customers into different groups based on their behaviour. We can craft personalised marketing campaigns for each group.
Forecasting of demand and inventory management Sales data can be analysed using data science models. It will help us forecast demand for different products. Based on the forecast, we can optimise our inventory levels. Stockouts can be avoided and hence will minimise waste.
Product recommendation system A data science model can analyse customer purchasing behaviour and preferences. This analysis will help us in curating personalised product recommendations for customers.
Transportation Industry
Systematic data collection from different data sources like cameras, GPS, and geo-location is done to prepare data science models.
Following are some of the applications where generated model can be used:
Route optimisation We can optimise delivery routes by considering factors such as traffic, road conditions and delivery priorities. This optimisation will help us minimise the time and cost required for delivery.
Real-time analysis of traffic and prediction of congestion on the road Real-time analysis of traffic can be done using data science models. This analysis will help us predict traffic congestion and rerouting in advance, resulting in better service delivery.
Predictive maintenance for vehicles and equipment We can analyse data from vehicle equipment sensors to predict when maintenance is needed. This analysis will result in reduced downtime, and also it will bring down maintenance costs.
Energy Industry
Sensors that collect operational time series data (data accompanied by a timestamp) such as voltage & current, Location (through GPS), Timestamp (in microseconds), and Device ID are used to prepare data science models.
Following are some of the applications where generated models can be used:
Forecasting of energy demand Sensor data from different power generation equipment can be analysed and used to make data science models. This analysis can result in reducing downtime, and maintenance in appropriate duration. It will reduce maintenance costs.
Detecting faults in power grids using anomaly detection techniques Proactive maintenance of the power grid is possible by collecting and analysing data from sensors in the power grid. This analysis can detect anomalies and predict potential faults in advance.
Educational data from K-12 schools, digital archives of instructional materials and grade books, as well as student responses on course surveys, are collected to prepare data science models.
Following are some of the applications where generated models can be used:
Analysis and Prediction of student performance Student performance can be evaluated using data science techniques. We can identify students who are falling behind or who may be at risk of dropping out.
Personalised learning We can find a student's weak area by feeding its data to a data science model. Using this, we can develop personalised learning experiences for students. We can also make our teaching methods more adaptive.
Curriculum optimisation and course recommendation system Course performance of various students can be analysed to improve the curriculum. Personalised course recommendation for each student or group of students is also viable.
Manufacturing Industry
data pertaining to material selection, machinery, tools required, manpower, processes, quality assurance, packaging, and supply chain after manufacturing is collected using sensors or from written records and later used in creating data science models.
Following are some of the applications where generated models can be used:
Optimising supply chain for Production It is possible to optimise the production and distribution process by analysing supply chain data. We can reduce overall costs and improve our efficiency.
Defect detection in the manufacturing process It is possible to predict or identify the defect in production by analysing past records. We can ensure top-level product quality using this.
Predictive maintenance for equipment and machinery We can analyse the data from manufacturing equipment sensors to predict exactly when maintenance is needed. This analysis will reduce our downtime and maintenance costs.
Frequently Asked Questions
Why should we use data science?
Data science can be used to do predictive modelling. We can extrapolate historical data to predict future conditions. It can segment our dataset into different groups and finally use a different strategy for each group.
What are the most common techniques used in data science?
Data cleaning, exploratory data analysis, data visualisation, statistical modelling, and predictive analytics are the most common techniques used in data science. They are required to identify patterns in data and make predictions for various applications.
What are some of the ethical considerations related to the use of data science?
We must obtain consent for data use. It is our responsibility to use data for social good and ensure responsible AI. Complying with relevant laws and regulations of geographical units is also necessary.
What is the future prospect of data science?
More and more businesses are looking to incorporate data science into their respective field of applications. We seek new and exciting healthcare, finance and entertainment innovations. New emerging AI and Machine learning technologies are also improving data science's future scope.
Conclusion
To conclude, data science applications are present in every field. Data science is continuously leveraged in fields such as healthcare, education, finance etc. Data science models can predict future trends after analysing historical data. Data science is also helpful in providing more personalised attention to customers. In short, in the coming days, data science is going to touch every aspect of human life and we all are going to reap the fruits of data science.
85+ registered