Know the Differenece Between Data Science, Data Analytics and Machine Learning

There are many different types of artificial intelligence — from robotics to natural language processing. Artificial intelligence is a blanket term that covers all machines that can learn and react to stimuli. From an analytical perspective, artificial intelligence can be divided into two categories: Machine Learning and Artificial Neural Networks. While AI and Data Science are often interchangeable, they’re not the same. You see, AI is short for artificial intelligence, which focuses on machines that simulate human behavior through data. Data Science is a set of techniques, tools, and processes used to analyze data and extract useful insights from it. However, there is more to AI than just the difference between Machine Learning, Artificial Intelligence, Data Analytics and Data Science. Let’s get started.


What is Artificial Intelligence?


Artificial Intelligence is the ability of machines to mimic human behavior. AI is often used in the context of machine learning; an algorithmic approach used to create software systems that can learn from data. AI is also used when software is able to make decisions by itself. In other words, AI is the intelligence exhibited by machines that enables them to perform tasks that require human-like intelligence. The terms artificial intelligence and machine learning are often used together as they are closely related. For example, artificial intelligence is used in machine learning when the computer makes decisions for itself. This is because artificial intelligence allows computers to be creative and make their own decisions. These computers can be used in different industries like healthcare, retail, etc. Artificial intelligence is not a new concept. It has been present in society since the 1950s. Many people have used the term computer science, software programming and artificial intelligence interchangeably. This is because they are often used in the same context.


Machine Learning


Machine learning is an application of Artificial Intelligence where computers can learn without being explicitly programmed. Computers can be trained and programmed to find patterns in data and make predictions based on that data. Its algorithms can be applied in different scenarios like forecasting the stock market, predicting the weather, or providing medical diagnosis. It’s a bit difficult to explain the concept of machine learning in simple terms. If we talk about machines, they can understand and interact with humans in many ways. For example, an ATM machine can process a transaction, an elevator can take you to the desired floor, and a car can drive itself to your destination. All these are possible because of machine learning.


Data Science


Data Science is a set of techniques, tools, and processes used to analyze data and extract useful insights from it. It is a field of study that uses methods from computer science, statistics, and artificial intelligence. Data Science spans many different disciplines, and tools like machine learning, AI, data analytics, etc are used within it. Data Science can be used in areas like engineering, business, and computer science. Data science is not confined to a particular domain. It can be applied in a variety of fields. Data Science is used in all sectors of the economy — finance, healthcare, education, and many other areas. Analytics is primarily concerned with the “what” of a problem and applying data science methods to answer it. As an example, a retailer might want to know what time of the day is best for selling skateboards. Data science tools can be applied to analyze the transaction data and find the best time for skateboard sales. This is one of the many examples where data science is used, and the main objective is to find patterns in the data.


Data Analytics


Data Analytics is the process of converting raw data into meaningful information through the use of various tools, algorithms, and processes. Data analytics is a subset of data science that focuses on extracting insights from data and producing meaningful information. Data analytics focuses on the “what” of a problem and applying data science methods to answer it. The difference between data analytics and data science is that a data analyst is more concerned with the process of obtaining data. They often ask questions such as “where can I find the data?” or “how can I obtain the data?” Therefore, data analytics focuses more on the “how” of a problem and applying data science methods to answer it.


Data Science vs. Data Analytics vs. Machine Learning



● Machine Learning vs. AI: The difference between machine learning and artificial intelligence is that ML is a subset of AI. Artificial intelligence is a broader term that can be used to describe many different algorithms like ML. AI can be used as a broad term to describe any algorithm that can learn from data.
● Machine Learning vs. Data Analytics: The difference between machine learning and data analytics is that a data analyst is more concerned with the process of obtaining the data. They often ask questions such as “where can I find the data?” or “how can I obtain the data?” Therefore, data analytics focuses more on the “how” of a problem and applying data science methods to answer it.
● Data Science vs. Machine Learning: Data science is a set of techniques, tools, and processes used to analyze data and extract useful insights from it. Data science can be used to obtain the data, clean the data and then apply algorithms like machine learning on the data to make predictions and find patterns in it. Data science is the process used to apply machine learning on data to make predictions. It is also important to note that many machine learning algorithms require a clean dataset. Therefore, it’s important to know how to clean the data before applying machine learning algorithms on it.
● Data Science vs. Data Analytics: Data analytics is a subset of data science that focuses on extracting insights from data and producing meaningful information from it. Data analytics focuses on the “what” of a problem and applying data science methods to answer it.

Conclusion


Even though AI and Data Science are often interchangeable, they’re not the same. Both have different interpretations and more importantly, they also have different applications. While data analysis simply focuses on analyzing data to extract meaningful insights, data analytics is used to convert raw data to extract meaningful insights. But both have Artificial Intelligence (AI) as their focal point. Data science, data analytics and machine learning are all used for problem-solving across several industries.

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