Data Science vs Artificial intelligence

The most advanced technologies in the world today are data science and artificial intelligence. These technologies are making a mark in every field. Technology has been redefined today by shaping the future with modern solutions. In this article, we will highlight the concept of data science vs artificial intelligence.

Certain factors differentiate between data science and artificial intelligence. Data science involves many different data operations, while artificial intelligence is limited to using just machine learning programs. Data science, on the other hand, involves both structured and no structured data, while artificial intelligence uses standard embedded data and has limited sources. Data science applications are mostly marketing, advertising, or internet search engines. Whereas artificial intelligence applications are based on manufacturing, automation, robotics, etc. Data science currently is the most happening and in-demand field in the world. Data science and artificial intelligence are conversable. Data science does contribute in a certain way to artificial intelligence; artificial intelligence does not help data science in any way. 

Well, it has been observed that many don’t have a clear understanding of data science and artificial intelligence, as they confuse about both thinking of data science and AI. In this article, all your assumptions and confusion will be cleared. Let's get our basics right.

What is Data Science?

Data science is a rising technology, spreading its light all over the world. The world is full of data and how in this data-driven world we can make the most of it. Be it industries or corporate in every sector data is ruling and playing a very important role. Data science involves detailed knowledge of statistics, mathematics, and programming. A data scientist should have a solid hand in all these aspects to be an expert in the data field. It is a deep field covering various complicated aspects. It takes deep knowledge to understand data science skills. Furthermore, it is a technique that extracts the data, it is involved in data manipulation, visualization, and data maintenance to study and pronounce better business insights. It is preferable to have good knowledge of machine learning, for a data scientist.

Industries that run on huge data require data scientists, as most of the company decisions are data-driven. 

What is Artificial intelligence?

Artificial Intelligence is defined as the ability of the program to process and learn from the experience. Parallel Human intelligence when processed by machines this process is called Artificial Intelligence. Artificial Intelligence replicates human efficiency and intelligence.

AI applications are playing an amazing role in almost all business sectors. By simplifying the technical process and coming up with better innovative business solutions. AI applications make our lives simple with accurate performance.
Here is a practical example of how the application of AI has influenced our lives positively.

The emendations and installations of human intelligence in machine learning in the form of programs that mimic human actions in the computer system, this is all made possible by artificial intelligence.

Here is a practical example of how the application of AI has influenced our lives in many ways:

• Google Assistant, Sir, Alexa

• Netflix and Amazon recommend similar content.

• Chatbots

• Automated vacuum cleaners

• Self-driving cars

• Voice/face recognition software

These are just some examples from the huge list we have. It is noticeable fact that artificial intelligence is everywhere. A huge amount of data is been processed and developed into useful content.

What are the Functions of Data Science?

Data science is a complete analysis of information. A data scientist plays an impressive role in taking important decisions for the company. Data cleaning and data transformation are the main job responsibilities of the data scientist. Analyzing the data and understanding the data pattern with analytical procedures and tools are detailed tasks that data scientist performs.

The business reports for future predictions are generated based on the data cleaning activity. A data scientist uses artificial intelligence as a technical tool to process data. Data science helps the company to derive data-driven decisions for future benefits. Data scientists extract the data from large data stores, study and define the various data patterns, analyze the data and apply statistics to predict better prospects. Based on the project requirements, at times it becomes necessary to make use of AI tools, deep learning, and algorithm process to predict future business results.

How is Data Science Different from Artificial Intelligence?

• Data science involves big data analysis, prediction, and visualization. Artificial Intelligence is the implementation of an insightful model to predict events.

• Data science is known for statistical techniques, design methods, and development techniques. Artificial intelligence has to do with algorithm design, development, innovation, conversions, and the assessment of these designs and products.

• Python, Oracle BI, tableau, and R are the tools used in data science, whereas TensorFlow and scikit-learn are tools used in AI. Data science is primarily concerned with making use of data. Artificial Intelligence is all about machine learning and algorithms.

• Data science was developed to fetch and extract the hidden patterns and trends in data. The discipline involves collecting and cleaning the data, processing it, refining it, and ultimately making it meaningful output for business reports.

• On the other hand, artificial intelligence is used to handle data autonomously, with human intervention and involvement. The tasks are all automated.  

• In Data Science, complex models can be built for extracting various facts, statistical techniques, and insights. On the other hand, artificial intelligence is meant for building models that transform human understanding to a certain level in the system. The aim is to create independence, meaning the machine would no longer require any human input.

• The quick short points that explain the guidelines for data science and artificial intelligence key differences are:

• You’ll use data science when: 

• Identification of patterns and trends

• Statistical insight is a must

• Exploratory data analysis (EDA) is required

• Quick mathematical processing is a call of the time

• Predictive analytics access is a must

You’ll use AI when:

• Accuracy is required

• Fast decision-making should be the priority

• Repetitive tasks are involved

• You need to test high-risk analysis

• Must make logical decision making without emotional involvement

Are Data Science and Artificial Intelligence the Same? 

There is a fine difference between data science and artificial intelligence; the following are some technical and procedural differences between the two:

Data Science	Artificial Intelligence
Data science involves data prediction, visualization, data processing	Artificial intelligence  depends on statistical strategies Complete use of algorithms
Multiple tools are used in data science	Involves algorithms and machine learning to develop the program extract
Extract the various layers embedded in the data	The goal of AI is to develop data model autonomy
Data science builds models based on statistics	AI builds models that emphasize human interpretations & understanding
Data science does not involve any scientific processing	Detail scientific processing is a very essential part of artificial intelligence

Data Science vs Artificial Intelligence

Data science and AI are the best techniques we have today. The development and technological advancements offered by both are immense. There is still confusion about the two technologies as many think they are the same. Hence it is necessary to understand how different both technologies are. So get into it:

• Definition: Data science is all about data collection and data analysis. AI is a process where advanced improved technologies are brought into action and futuristic vision is analyzed.

• Scope: Artificial Intelligence has a precise set of actions with Machine learning and algorithms, whereas Data Science is all about data operations.

• Data: Artificial Intelligence contains standardized data with a well-defined vector type but, on the other hand, Data Science has both structured and unstructured data with no standard pattern.Tools: AI tools are, Scikit-learnPyTorch, CNTK, Caffe, Apache MXNet, Keras, OpenNMS, and the Data Science tools are Keras, SPSS, SAS, Python, MATLAB, Excel, ggplot2, and Tableau.Applications: Artificial Intelligence applications are used in many sectors like the manufacturing industry, Healthcare, Security, Scientific and innovative industry, robotics, and automation. Data Science applications are used in Business, marketing, banking, and Internet search engines like Google, Bing, etc

• Mechanism: Artificial intelligence involves a process of anticipating perspectives using a predictive prototype. Data science involves the process of analysis, visualization, and prediction of data. Artificial intelligence involves tedious and complex methods as compared to data science.

• Techniques: Artificial intelligence is all about using machine learning programs and algorithms to resolve the issue. Data science will always be about statistics, calculations, and data.

• Goal: The basic objective of Artificial Intelligence is to automate the task. Data Science on the other hand discovers various patterns in data and predicts insightful vision. Both have different perspectives and purposes to resolve the problem.

• Science: Artificial Intelligence has detail and very deep scientific processing whereas DataScience has more operational processing.Essential Skills: When it comes to the difference between Artificial Intelligence and Data Science, the AI professional must have good knowledge of machine learning programs and algorithms. Whereas the Data Scientist should be well experienced in data analytic tools like Python, R-programing, BigML, D3, MATLAB, Excel, ggplot2, Tableau, etc