Data science and Artificial Intelligence are the fields that are penetrating many companies and industries all over the world. The connection between data science and AI was established through the data scientists. Earlier days, data scientists work was to isolate and primarily for R&D research purpose, but later on, the scientists moved to the new innovations of artificial intelligence.
What exactly Data Science and Artificial Intelligence are?
Data science is a discipline where it can obtain information and insights that are anything of value. In reality, data science is growing so fast and has shown various possibilities of spreading that has essential to understand it. It is an interdisciplinary field system and process to extract knowledge from the data in many forms.
Artificial Intelligence is the term that makes a possibility for machines to learn from the experience. AI is different from robotic automation, hardware-driven. AI can perform high-volume, frequent, computerized tasks without weariness. In other words, artificial intelligence dumps huge data to clear the targets.
The Connection between Artificial Intelligence and Data Science:
Data science is the field of interdisciplinary systems in which it observes information from data in several forms. It is also used to modify and to build Artificial Intelligence software in order to obtain the required information from the huge data sets and data clusters. Data-oriented technologies like Hadoop, Python, and SQL are covered by using data science. Data visualization, statistical analysis, distributed architecture are the extensive uses of data science.
Whereas Artificial Intelligence represents an action plan in which in starts from perception which leads to planning action and ends with the feedback of perception. The data science plays a major role in which it solves specific problems. As we discussed in the first step data science identifies the patterns then finds all the possible solutions and then finally choose the best one.
Both Artificial Intelligence and data science are the fields from the computer science that penetrate several companies all over the world. Their adoption corresponds with the Big-data rise in the past 10 years. In recent times the advanced data analytics can transform companies understand organize an activity, insights and create value. Progress with open source libraries, cloud computing, and programming languages have also made it very simple to get effective data.
Data Science produces insights:
Data science goal is to reach the human one especially i.e. to achieve insight and understanding. The very classic definition of data science is that includes a combination of software engineering, statistics and domain expertise. The main difference between AI and data science is that data science always has a human in the loop: someone seeing the figure, understanding the insight and benefiting from the conclusion.
This data science definition can emphasize:
- Data visualization
- Experiment design
- Statistical Inference
- Domain knowledge
Data scientists report percentages and based on the SQL queries they can make line graphs by using simple tools. They can build interactive visualizations, analyze trillion records and develop the techniques of cutting-edge statistics. The main goal of data scientists is to get a better understanding of data.
Artificial Intelligence produces actions:
Artificial Intelligence is the most widely recognized and older than the data science. As a result, it is the most challenging one to define. This term is surrounded by journalists, a great deal of hype, startups, and researchers.
In some systems, Artificial Intelligence includes:
- Reinforcement learning
- Robotics and control theory
- Robotics and control theory
- Game-playing algorithms
- Natural language processing
Here, we have to discuss one more term called deep learning. Deep leaning is the process in which it makes the straddle of both fields Artificial Intelligence and Machine Learning. The use case is that training on particular and to get the predictions. But it takes a huge revolution in the algorithms of game-playing like AlphaGo. This is indifference to the previous game playing systems. For example Deep blue, which concentrated more on optimizing and exploring solution future space.
Business and Social impacts of Data Science and Artificial Intelligence:
As we discussed above the field of data science is one of the traditional modes to find how the latest and modern technologies are being used to resolve business problems in terms of strategic advantage. Data scientists will conduct their business as IoT, Big data, cloud continue and algorithm economics in the near future. All these are to become an influencer across global enterprises.
The below are the features of AI-Powered Data Science:
- Automatic analytics processes
- Analytics’ platforms domain specialization
- Predictive analytics
There are many innovations are happening across industries all over the world. Computers are learning to identify the patterns that are too massive, too complex, too subtle for software and also for humans.
We have witnessed over the last few years that Artificial Intelligence playing a major role in the present generation. AI has the capability of transforming many companies and they can create new types of businesses. Infosys in its survey report said that most of the Artificial Intelligence businesses were predictive analysis and big data automation. AI can bring benefits like progress improvement, good customer service, management, business intelligence etc.
The below are the major use cases for AI in business:
- Predict behaviour and performance
- Pattern recognition
- Improve business process
- Business insight
- Improve efficiency by using job automate functions
Apart from the advantages, AI has some disadvantages like expensive, time taking, needs to be integrated, may disrupt employees.
Data science is termed as the secret sauce in which it enhances the business by driven-data. The projects of data science can be investment multiplicative returns both from data product development and data insight guidance. The key factor in hiring a data scientist is to nature and engage them first. Autonomy should be given to their architects to solve problems. Whereas in the case of Artificial Intelligence it is the intelligent agents’ design in which the actions can maximize the success chances.