Main que:What is Data Science? What is the difference between Data Science, Big Data and Data Analytics? How does Machine Learning relate to this? What is the difference between Machine Learning and Statistical Learning? What is the difference between AI i.e. Artificial Intelligence and Machine Learning?

Provide replies to below student posts each in 150 words.2 student posts.

Rame:The amount of data in the modern world is overgrowing, changing how people live as well as how scientists conduct researches. Data seems everywhere. As digital data increases, various concepts have emerged. Notably, many people use multiple data concepts interchangeably. Therefore, understanding the different methods associated with data is vital.

Data science, big data, and data analytics are the first concepts to consider. Data science involves the actual activities that deal with both structured and unstructured data. The field encompasses all practices associated with the preparation, cleansing and analyzing data (Agarwal & Dhar, 2014). In contrast, Big data is the large volume of information that scientists cannot process effectively through traditional approaches. Subsequently, Data analytics entails the actual science of evaluating raw data to produce particular concrete finished information (Agarwal & Dhar, 2014). The differences in data science, big data and analytics enhance machine learning succeed. Machine learning can never be effective without the three concepts.

Machine learning and statistical learning also confuses many people. Usually, it is always hard to differentiate the two terms due to how people view data science. Notably, the two concepts are dependent. Nonetheless, statistical learning focuses on rule-based programming. Statistical learning relies on various variables. In contrast, machine learning studies data without any programmed instructions (Hothorn, 2019). Moreover, statistical learning depends on assumptions such as homoscedasticity and normality. Conversely, machine learning does not operate on-premises (Hothorn, 2019). Equally, statistical learning is more math-intensive, while machine learning focuses on identifying patterns of the set data.

It is also fundamental to examine the difference between artificial intelligence and machine learning. Artificial intelligence refers to human knowledge. The concept comprises of complex machines and computers that act like human beings. Conversely, machine learning is typically a section of artificial intelligence (Ghahramani, 2015) Machine learning uses algorithms to learn, get and analyze data. Thus in a nutshell, machine learning is the method of machines studying data from various analyses to make artificial intelligence succeeds.

Naga:Data Science is an interdisciplinary way to deal with information science. It lies at the crossing point of math, insights, man-made brainpower, plan thinking and programming building. Data Science is worried about information assortment, cleaning, investigation, perception, model development, model approval, expectation, analyze structure, speculation testing, and the sky is the limit from there. Every one of these moves are planned for picking up knowledge.

Differernce:
Data Science is the blend of insights, arithmetic, programming, critical thinking, catching information in clever ways, the capacity to take a gander at things in an unexpected way, and the action of preparing, and aligning the data.

Big Data used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. Big Data is something that can be used to analyze insights that can lead to better decisions and strategic business moves.

Data Analytics is used in many industries to enable better decision-making by organizations and companies as well as to validate and disprove existing theories or models. Data Analytics focuses on inferencing, which is the process of drawing conclusions based solely on what the researcher already knows.

What is the difference between Machine Learning and Statistical Learning?
Data Science is the blend of insights, arithmetic, programming, critical thinking, catching information in clever ways, the capacity to take a gander at things in an unexpected way, and the action of preparing, and aligning the data.
Machine learning promotes data science by offering a collection of algorithms for data modeling / analysis (through training of machine learning algorithms), decision-making and even data preparation.

When Machine Learning comes to life and moves beyond simple programming and can reflect and interact with people, even on the most basic level, this is where AI comes into play. AI is evolving. We are currently perceiving that most things called “computer based intelligence” in the past are just best in class programming stunts. For whatever length of time that the developer is the one providing all the knowledge to the framework by programming