Artificial Intelligence, also known as AI, and Data Science, has become the two most important sought after technologies in today’s time. Many a time, people think of it as the same thing, but they are not the same thing in reality. Artificial Intelligence is used in the field of Data Science for its operations. Here now we will discuss the different concepts of Artificial Intelligence vs Data Science.
What is Data Science?
There has been significant growth in need of the data processing to the industries after the explosion of massive data collected by them through various means of the internet like a laptop, smartphone, tablet, desktop, etc. The companies are now relying on data to make any decisions related to almost everything about the organization. These decisions are used to make better services and products, enhancement and modification, elimination and addition of different things, etc.
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Data Science has thus brought forth a massive revolution in almost all industries. Modern societies are all data-driven, and that’s why Data science has become a crucial part of the contemporary world.
There are many subfields in Data Science such as Programming, Mathematics, and Statistics. A Data scientist should be very proficient in understanding the patterns and trends of the data. One should possess this skill of understanding to become a good Data Scientist. There are many procedures and steps in Data Science which are:
- Extraction of Data: The Data has to be extracted by the Data Scientist from Big Data which is the first step in the processing of data. Data extracted should be able to give insight into a specific problem which will be later used by the leadership, management or other decision making authorities in the organization.
- Manipulation: A Data Scientist should be able to manipulate the data by applying specific filters. Using filters, one should be able to get the desired level of data filtration, which is going to be analyzed further for decision-making.
- Visualization: The Data Scientist shall create a display of data that can be easily understood. The Data can be represented in the form of Tables, Diagrams, Charts, Graphs and many more. When the Data is visualized, then it is straightforward to understand which is the best form of anything to understand.
- Maintenance: The data extracted has to be maintained for future purposes as well so that it can be used again in future decision-making to predict various things in the businesses.
Hierarchy of Needs in Data Science
As now we already know that Artificial Intelligence is a part of Data Science, Now we will discuss the six different hierarchy of needs in Data Science:
- First Need: Artificial Intelligence and Deep Learning
- Second Need: A/B Testing, Experimentation and Simple ML Algorithms
- Third Need: Analytics, Metrics, Segments, Aggregates, Features and Training Data
- Fourth Need: Cleaning, Anomaly Detection, and Prep
- Fifth Need: Reliable Data Flow, Infrastructure, Data Pipelines, ETL, Structured and Unstructured Data Storage
- Sixth Need: Instrumentation, Logging, Sensors, External Data and User Generated Content
What is Artificial Intelligence?
Artificial Intelligence is a field where algorithms are used to perform automatic actions. Its models are based on the natural intelligence of humans and animals. Similar patterns of the past are recognized, and related operations are performed automatically when the patterns are repeated.
It utilizes the principles of software engineering and computational algorithms for the development of solutions to a problem. Using Artificial intelligence, people can develop automatic systems that provide cost savings and several other benefits to companies. Large organizations are heavily dependant on Artificial Intelligence, including tech giants like Facebook, Amazon, and Google.
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Data Science vs Artificial Intelligence: Difference Between Data Science and Artificial Intelligence
- Scope: Artificial Intelligence is only limited to the implementation of ML algorithms, whereas Data Science involves various underlying operations of data.
- Type of Data: Artificial Intelligence contains the kind of data that are standardized in the form of vectors and embeddings but, on the other hand, Data Science will have many different kinds of data such as structured, semi-structured and unstructured type of data
- Tools: The Tools used in Artificial Intelligence are Mahout, Shogun, TensorFlow, PyTorch, Kaffe, Scikit-learn and the tools that are used in Data Science are Keras, SPSS, SAS, Python, R, etc
- Applications: Artificial Intelligence applications are used in many sectors like the Healthcare industry, transport industry, robotics industries, automation industries, and manufacturing industries. On the other hand, Data Science applications are used in the field of Internet Search Engines like Google, Yahoo, Bing, Marketing Field, Banking, Advertising Field and many more.
- Process: In the process of Artificial Intelligence (AI), Future events are forecasted using the predictive model. But Data Science involves the process of prediction, visualization, analysis, and pre-processing of data.
- Techniques: Artificial Intelligence will use algorithms in computers to solve the problem, whereas Data Science will involve many different methods of statistics.
- Purpose: The primary purpose of Artificial Intelligence is to automate the process and bring autonomy to the model of data. But the primary goal of Data Science is to find the patterns that are hidden in the data. These both have their own set of purposes and goals which are different from each other.
- Different Models: In Artificial Intelligence, Models are built which are expected to be similar to the understanding and cognition of humans. In Data Science, Models are constructed to produce insights that are statistical for decision-making.
- Degree of Scientific Processing: Artificial Intelligence will use a very high degree of scientific processing when compared with Data science which uses less scientific processing.
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Artificial Intelligence is yet to be explored very much, but on the other hand, Data Science has already started to make a big difference in the market. Data Science transforms the Data, which can be used for visualization and analysis.
With the help of Artificial Intelligence, new products are created which are better than before, and it also brings autonomy by doing many things automatically. With the help of Data Science, Data is analyzed based on which careful business decisions are taken that provides many benefits to the companies.
There are many companies based on Artificial Intelligence that offer pure AI job positions such as NLP Scientist, Machine Learning Engineer, and Deep Learning Scientist. Various operations on data are performed using the Data Science algorithms implemented in languages like Python and R. Key decisions today are taken based on the Data that is processed by Data scientists. Thus, Data science has to play a vital role in any organization.
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Why do we need to keep our databases up to date?
The goal of database maintenance is to keep the database clean and well-organized in order to prevent it from becoming unusable. Simply backing up the data so that another copy is available in the case of a disaster is one of the most important parts of database management.
What are AI's primary objectives?
Artificial planning aids agents in determining the best course of action to take to attain their objectives. Reasoning, knowledge representation, planning, learning, natural language processing, vision, and the capacity to move and control things are all traditional AI research aims. The artificial intelligence-assisted process of building robots that can read and comprehend human languages is known as natural learning processing.
What role does data visualization play in the development of AI projects?
Data visualization helps us comprehend what data means by putting it in a visual context, such as maps or graphs. This makes the data easier for the human mind to comprehend, making it easier to see trends, patterns, and outliers in large data sets. Data visualization is an important assessment criterion for deep learning since the ultimate goal of artificial intelligence is to create a machine that can grasp and respond to data even better than a person. Data visualization has shown to be important in AI development since it may help both AI engineers and others concerned about AI adoption understand and explain these systems.