What is data science?

 

Data science is a discipline that integrates subject understanding, computer skills, and math and statistics knowledge to derive useful information from data. Beginners and those considering self-learning will find it challenging to grasp these concepts, so I suggest consulting with experts. Learnbay (an IBM-certified programming course) offers a wide range of data science classes, from beginner to advance.

Machine learning algorithms are applied to numbers, text, images, video, audio, and other data to create artificial intelligence (AI) systems that can perform tasks that would otherwise require human intelligence. As a result, these programmes produce insights that analysts and market customers will use to create real business value.Importance of data science

Mostly data science is used to forecast the sale and the growth of any firm by using data science types those are:

●       Descriptive analysis (used to analyze the past data)

●       Predictive analysis (used to analyze the future by analyzing  past data)

●       Prescriptive analysis (used to analyze the current data which helps in decision making)

Data technology, AI, and machine learning are becoming increasingly important to businesses. Organizations who want to stay competitive in the era of big data, regardless of market or scale, must build and incorporate data science skills quickly or face being left behind.

Steps to analyze or process the data:

●       Identifying the problem

●       Collect the data needed to solve the problem

●       Analyze the data

●       Clean the data

●       Create a model

●       Evaluation

●       Deployment

●       Monitoring

Major subset of data science

AI – Artificial intelligence

Artificial Intelligence enable the machine to think and act without any human intervention.

AI are into 3 types

1. ANI (Artificial Narrow Intelligence) which is goal-oriented and programmed to perform a single task.

For example – automatic washing machine 

2. AGI (Artificial General Intelligence) which allows machines to learn, understand, and act in a way that the machine is programmed

For example – auto pilot car

3. ASI (Artificial Super Intelligence) is a hypothetical AI where machines are capable of exhibiting intelligence that surpasses brightest humans.

For example – automation robots

ML- Machine learning

Machine Learning (ML) is a subset of artificial intelligence (AI) that uses statistical learning algorithms to build smart machine or model. The (ML) Machine Learning systems can automatically learn and improve without explicit being programmed.

Machine Learning (ML) is commonly used along with artificial intelligence (AI) but it is a subset of artificial intelligence (AI). Machine Learning (ML) refers to an artificial intelligence (AI) system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is Machine Learning (ML). 

For example - suggestions in youtube videos

DL- Deep learning

Deep learning is also a subset of artificial intelligence. The deep learning is inspired by the way a human brain analyze the information. Deep Learning Courses help the AI model to analyze the input data to forecast or classify the information.

Deep learning analyze the data in multi-layer neural network

            ANN(artificial neural network) information input in the from of numbers

            CNN( convolution neural network) information input in the from of images

            RNN(recurrent neural network)information input in the from of time series data

There are few simple facts about the data science subsets of AI, ML, and DL. I recommend taking a Learnbay course to create a model using data or software like Python. Learnbay in Bangalore offers a wide range of data sciences, artificial intelligence, machine learning, python, R-programming, and Data Analytics Courses.