Artificial Intelligence and data science have become two buzzwords in the business world. Data science is an interdisciplinary field that involves study of a huge volume of research data using modern, robust tools and technologies to derive valuable insights that help organizations make better decisions. Data science makes use of powerful and complex machine learning algorithms to create predictive models.
The life cycle of data science
The life cycle of data science consists of 5 important stages. These include:
1) Capturing of data
It involves capturing raw data, either in structured or unstructured form.
2) Maintaining the data
It involves converting the raw data into a form that can be used for problem-solving.
3) Data mining
In this stage, data scientists classify and summarize data to identify patterns, biases, and ranges to determine how useful the data will actually be in predictive analytics.
4) Analyzing the data
This stage involves data science experts performing analysis on the data.
5) Communication and reporting
The final stage in the life cycle of data science involves preparing analysis of the data in the form of graphs, charts, and reports that can be easily readable by the common person.
Data science vs data analytics – Understanding the difference between the two
- It involves extracting useful information and insights by applying various algorithms, scientific methods, and processes to data.
- It deals with both data, structured and unstructured.
- Machine learning algorithms are used.
- Scope of data science is large.
- It involves deriving conclusions by processing raw data.
- It deals with only structured data.
- Machine learning algorithms are not used.
- Scope of data analytics is limited.
Purpose of data science
- The main purpose of data science is to identify patterns within data.
- Data scientists make use of various statistical techniques to analyze and generate insights from data.
- From data extraction to data processing, data scientists can draw conclusions based on the research data, and assist businesses in making smarter decisions.
Why does data matter?
In the 21st Century, data is the new fuel. Be it any business or industry, the importance of data cannot be overlooked. Huge volume of data flows every minute. Most businesses are unaware about what to do with data. In today’s competitive world, data is crucial. Data is more crucial now than ever before.
Here are the reasons why data matters to organizations.
- Predict future outcomes
- Optimization of performance
- Identify market trends
- Make informed decisions quickly
- Risk assessment
- Measure customer satisfaction levels
Prerequisites of data science
For those looking to make a career in data science, they need to be familiar with the following concepts.
- Machine learning
- Mathematical modeling
Applications of data science
The application of artificial intelligence and data science can be seen in various businesses and industries. Some of these include:
Banking and Finance
- Risk modeling
- Customer segmentation
- Fraud identification
- Predictive analytics (real-time)
- Customer lifetime value
- Network planning
- Demand analytics
- Procurement analytics
- Transportation analytics
- Analyzing customer reviews
- Product recommendations
- Identifying prospective customers
- Customer sentiment analysis
- Drug discovery
- Virtual assistants
- Medical image analysis
- Vehicle monitoring system
- Autonomous cars
- Enhanced safety of passengers
- Preventive maintenance
- Anomaly detection
- Optimization of supply chain
- Predictive analytics of potential problems
- Monitoring systems
Research data can also give a wrong solution sometimes
- Most well-established organizations make use of data science and data analytics on research data to get valuable insights to drive decision-making.
- However, research data does not always give the right results. Although the importance of data cannot be overlooked, many times it is possible that data may give wrong results.
Here is a case study of how research data gave wrong solutions at the time of World War II.
- During World War II, a lot of American aircraft were being shot down by Germany.
- For problem-solving, the Center for Naval Analysis decided to conduct an examination of the problem.
- For this purpose, an examination of the bullet holes and damage to the aircraft that returned to the base after the mission was done.
- Research data clearly indicated a pattern in the majority of the aircraft.
- Most of the damage was caused to the wings and main body of the airplanes.
- As per the initial engineering recommendation, an increase in armor under the wings and main body of the airplanes was supposed to be made.
- However, there was a fundamental (major) flaw in the research data used for problem-solving.
- The research data only included information of the aircraft that had returned to the base, not those that had been shot down.
- Initially, the researchers failed to realize the fact that the research data they were studying was actually those areas of the airplane that had sustained bullets yet managed to survive.
- In reality, reinforcement was required in other parts of the aircraft which had actually got damaged and never managed to return.
- The engine, cockpit, and tail of the aircraft were most vulnerable that needed reinforcement.
- This example is of survivorship bias in action.
- In this example, it can be seen that there is exclusive focus on events, people, or objects that succeed, without considering the research data of failure cases.
With the amount of data that is being generated, artificial intelligence and data science form the mainstay of data-intensive companies. Through the power of data analytics and data science, businesses can make valuable decisions and create appropriate strategies. The main goal of data science is to help businesses grow and unleash their real potential.