For the management of information, data science and artificial intelligence are very much important in this modern age of business. To fulfill their roles, data scientists are using artificial intelligence, as well as machine learning. In this article, we will explore what role artificial intelligence and machine learning play in data science?
What is Artificial Intelligence?
Artificial intelligence (AI) is the simulation of human intelligence processes by machines like computer systems. These processes include learning, reasoning, and self-correction. Here learning means the acquisition of information and rules for using the information, reasoning means using rules to reach approximate or definite conclusions. Applications of Artificial Intelligence mainly include expert systems, speech recognition, and machine vision.
What is Data Science?
Data Science is mainly about extraction, preparation, analysis, visualization, and maintenance of information. Data science uses scientific methods to draw insights from data.
At present, Artificial Intelligence is complex and effective but nowhere near-human intelligence. Humans use the data which is accumulated in the past and also use the data which is present around them to figure out anything and everything. However, AI can’t accumulate past data. Artificial Intelligence just has huge data dumps to clear their objectives. This means that AI requires a huge pool of data to do something as simple as editing letters.
As said earlier, Data science is a cross-disciplinary field that uses scientific methods and various processes to draw insights from data. This means that data science helps AIs figure out solutions to problems by linking similar data for future use. Fundamentally, data science allows for AIs to find appropriate and meaningful information from those huge pools faster and more efficiently.
Machine learning is the process of learning from data over time and is likely the connection between data science and artificial intelligence However, it’s not the only thing connecting those two. But, machine learning is the main branch of Artificial Intelligence that works best with data science. Machine learning is a subset of artificial intelligence.
What does Data Scientists do?
As Artificial Intelligence is embraced by a lot of organizations, a lot of people are interested to become a data scientist.
Data scientists work in a variety of fields. Data Scientists are very crucial in finding solutions to problems and they require specific knowledge. The variety of fields include data acquisition, preparation, mining and modeling, and model maintenance. Data scientists take raw data, with the help of machine learning algorithms that answer questions for businesses seeking solutions to their queries they turn it into a goldmine of information.
Each field can be defined as follows:
Data Acquisition: Here, data scientists collect data from all its raw sources, such as databases and flat-files. They integrate and transform it into a homogenous format, collects it into a data warehouse. It is a system by which the data can be used to extract information easily. This is also known as ETL
Data Preparation: This is the most important stage, where they spent the maximum amount of time filtering out data because must be scalable, productive and meaningful. There are five sub-steps in data preparation.
- Data Cleaning: To improve business productivity data cleaning is very important because bad data can lead to bad models, this step handles missing values and null or void values that might cause the models to fail. Ultimately, data cleaning improves business decisions and productivity.
- Data Transformation: Data scientists take raw data and turn it into desired outputs by normalizing it.
- Handling Outliers: Using exploratory analysis, a data scientist quickly uses plots and graphs to determine what to do with outliers and thoroughly checks why they appear here. Outliers are often used for fraud detection. Data Integration: Here, the data scientist makes sure that the data is accurate and reliable.
- Data Reduction: This compiles multiple sources of data into one, reduces costs increases storage capabilities, eliminates duplicate, redundant data.
Data Mining: To take better business decisions data scientists uncover the data patterns and relationships. Data mining is useful for predicting future trends, customer pattern recognitions help in decision making, detects fraud and choosing the correct algorithms. Tableau works nicely for data mining.
Model Building: The model is built by selecting a machine learning algorithm that suits the data, problem statement and the available resources. There are two types of machine learning algorithms like Supervised algorithms and Unsupervised algorithms.
- Supervised: Supervised learning algorithms are used when the data is labeled.
- Unsupervised: Unsupervised learning algorithms are used when the data is unlabeled.
Model Maintenance: Data scientists must maintain the model accuracy, after gathering data and performing the mining and model building.