Data Science vs Artificial Intelligence-Key Differences

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Introduction

Artificial intelligence and data science are the new forms of study that play with scientific methodology, processes, and techniques taken from different domains like statistics, cognitive science, and computing information science to extract thinking from modified and non-modified data. Data science makes use of artificial intelligence in its operations.It does not show AI. Data science is to make better products. Nowadays society is full of data made. It is to make great decisions for the industry. Data is always made for extraction, alterations, imaging, and maintenance of data to occur in future events.

Data science depends on the tools like SQL, Hadoop WEKA, R, and Python. Artificial intelligence is presented by the machine absorbed by the animals and humans.AI makes the algorithm is for its own actions to perform. Data science vs artificial intelligence is totally dependent on data-driven solutions to existing problems. The number of data scientists has extended by 650% since 2012. About 11.5 million jobs will be materialized by 2026 according to the U.S bureau of labor statistics.

Data Science

It is a well-known field in the IT industry and had conquered almost a free industry today. It is a big field that mainly pertains to data processes and data systems and its aim to work is data set to derive valuable information for them. It is totally excerpting useful and meaningful thoughts about companies and current market status. You should know about several steps about the technology. The revolution on facebook which is a social media of today’s world to extract information on social interactions on its customers.

It is the heart of business decision making having realized the enormous value in data processing and analysis. It is a regular industry capitalizing the data science every day and they can generate more business outlooks like Airbnb. Use data science and process to analyze customer-generated data to predict customer behavior.It develops new features, products, and services for its consumers. Companies and banks also extracts data with scientific method.

When solving a real-world problem the first thing we have to know is cleaning and pre-processing because data is given to that man in an unstructured format. Optimizing the data and erasing the enormous information makes it easier to analyze and draw insights.It is a statistical procedure.

• Descriptive statistics
• Inferential statistics

In order to do so, you have to take a thorough look at the Data and understand various patterns involved using graphs and charts. To find out the data outputs and classes they should have a binary classification algorithm and multi-variate classification algorithm to resolve the problem. In this process, there may have been unloaded data. There is no segregation of output in the fixed classes mentioned above. It is totally dependent on customer leads and socio-economic background clustering of the algorithm for also the potential clients.

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Tools for data science

Tools for data science are the backbone of machine learning. The deep learning algorithm is to generates predictions.

R is a scripting language

In today’s world R programming language explored different and essential concepts. It is to transform the data science industry through editors and the environment. You can run the R code. R was conceived at the bell laboratories by John chamber in 1986. Graphical libraries, job scenario, portability with support and community it is open-source programming language that pervades statistical computing and graphical libraries.R is an interpreter-based language and is widely popular across multiple industries.

Python-

Like R python is the worldwide programming language.It has its versatility. It is mostly used for software development and code readability.The data analysis, natural language processing and computer vision make it worldly popular. Python comes through many statistical packages like MATPLOT LIB, NUMPY, SKYPAI, and some other packages like–TENSOFLO,PI TORCH, KERAS. Python is used for wrangling, visualization and developing predictive models.

SQL–

It si paraphrased by structure, query, and language. Data scientists use SQL for managing and storing databases, extracting from databases organized in levels. It is to manipulate the data.It is working under bank industries. There is no data rational or distributed database. The database is an open-source nature like–MongoDB, RADIUS, and CASSANDRA.

Hadoop–

There is huge storage and management of data either structured or unstructured. Scientists may accelerate complex data and huge storage of data sets.The storage tools are modeled by map-reduce, Apachepig, Hive, H base, etc.

Tablean–

It is a data visualization software having a graphical outcome. It provides interactive imagery and dashboards.It also offers charts such as a treemap, histograms, boxplots, etc.It also interlinked spreadsheets, rational databases.

WEKA–

For medical writing, weka is an ideal option. Weka is generally used for data mining operations.

Data Science in Health care-

Data science has a pictorial role in the healthcare industry.By the algorithm, doctors are able to detect cancer and tumors of an early stage with image detecting software. Genetic industries use data science for analyzing and classifying of genomic sequences. Various virtual assistance is also helping patients to solute mental and physical diseases.

Data Science in E-commerce

Amazon recounts the product based on its historical purchase. Data scientists have developed the machine learning procedure.

Data science in manufacturing

Industrial robotics concern us with data science technologies such as reinforcement learning.

Data Science as a conversational agents

Amazon’s Alexa and siri by Apple use speech recognition to convert human speech with an appropriate response.

Data science in transport

Self-driving cars are useful to detect algorithms. It is no longer a story that is now in our hands.

Artificial Intelligence

Artificial Intelligence(AI) is the ability of the computer or a robot-controlled computer to do tasks because they require human intelligence. Human intelligence speech recognition, decision making, and visual perceptions are the key feature of AI.

Example-Maps and navigation,facial detection and recognition text editor or auto-correct,chatbots,social media,e-payments.”Theory and development of computer systems able to perform the tasks that normally require human intelligence such as visual perception, speech recognition, decision making and translation between languages.

”Artificial intelligence is to gather data from specially designed algorithms. It can encourage consumers. It also serves as a form of entertainment that man kinds seek.

The important are machine learning and natural language processing.(NLP).NLP is a tool through which human language process.

History of Artificial Intelligence

The idea of a machine first came in Greece. Electronic computing is as follows-

1950-.Alan Turing publishes computer machinery and intelligence for breaking the Nazi’s enigma code during world war.

1956-John Macarthy first goads the term AI at Durt Mouth College and Herbert Symon create the logic theorist the first-ever running this software program.

1967-Frank Rosenblatt first experienced Trial and error

1980- Backpropagation algorithm is used in AI applications.

1997-IBM Gary Casrav first brought in the scenario.

2015-Bids Menwa categorizes images with the accuracy of an average human being. Automated stock trading use AI even millions of trade per day without human intervention.

Artificial Intelligence and IBM cloud

IBM has been a leader in advancing AI-driven technologies for enterprises has pioneered the future machine learning systems for multiple industries generally depending on AI research, years of experience working with organizations of all sizes like collecting, organizing, analyzing, infusing and modernizing.

Deep Learning vs Machine learning

Deep learning and machine learning are used interchangeably its value is nothing the nuances between the two. Deep learning is actually comprised of Neural networks. It has more than three layers including inputs and outputs having a deep learning algorithm.It differs how the algorithm differs how algorithm learns. Deep learning automates eliminating some of the manual interventions enabling the use of leather data sets. Deep machine learning can leverage level data sets known as supervising learning which distinguishes different categories of data from one another. It allows us to scale machine learning in more interesting ways. It automates learning and discovery through data.

Why AI is important

Instead of automating manual tasks, AI performs frequent digitalized tasks without fatigue. It will be improved with AI capabilities much like SIRI added as a feature to a new generation of Apple products. Automation conversational platforms, bots and smart machines can be combined to improve technologies.AI finds structures and regularities in Data so that algorithms can acquire skills just as an algorithm to play chess. So AI analysis the feeling of data with hidden layers. Building a fraud detection system with five hidden layers used to be impossible. All that has changed incredible data and big data. The AI achieves incredible accuracy through Neural networks. Google is all based on deep learning.  And these products keep getting more accurate the more you use them.

Data Science and artificial intelligence differences

• Artificial intelligence is the only bound to use ML algorithms whereas data science involves various underline operations of data.
• Artificial intelligence is that kind of data in the form of vectors but data science will have structured, unstructured, and semi-structured data.
• The tools used in artificial intelligence are Mahout, Shougun, Tensor, Flow, Py Torch, Kaffe, Scikit learn and data science are dependent on Keras, Spss, Sas, python, R, etc.
• Artificial Intelligence is used in those sectors like the healthcare industry, transport industry, robotics industry, automation industry, on other hand, data science applications are used in internet search engines like google, yahoo, Bing, Marketfield, banking, advertising field, and many more.
• In the process of AI future events are forecasted using the predictive model but data science is involved in the prediction, visualization, and pre-processing of the data.
• Artificial intelligence will in usages of algorithms in computers to solve problems whereas data science will involve many different methods of statistics.
• 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 in the data.
• In artificial intelligence, the models are built which are expected to be similar to and cognition of humans. In data science models are constructed for insights that are theoretical for decision making.
• Artificial intelligence will use a very high degree of scientific processing and data science is a low level of scientific processing.

Data Science courses and artificial intelligence courses in India.(Henry Harvin)

Mtech data science is the two years full-time course designed by corporates in the domain of data science and AI. The course is open for Btech, BE, MCA graduates and graduate with P.G Diploma in Data Science.CAndidate can get practical knowledge in programming and statistical techniques for data science along with data scrapping and data wrangling. There is also expertise in cloud computing, machine learning, deep learning, AI, and big data technologies.

Job Roles in the field of Data Science

The data science roles are evolving from business to government, healthcare to academia. There are a quite variety of roles in data which are search-oriented, engineering, and business-oriented.To sustain in today’s world it is very essential to collect, clean, and analyze data. Strategical intelligence and data literacy are the most important skills in this field. Students must be adaptable in business analytics where knowledge trains are changing at a fast pace. Data analyst and enterpreuenship roles are changing.

Top job roles in data science are-

• Data Analyst
• Data engineer
• Data scientist
• Data administrator
• Machine learning engineer data architect
• Business analyst
• Statistician 
• Data analytics manger
• Product analyst

Conclusion

We have discussed two major branches of information technology.

Surn laboratory of Geneva made in a 600 ft underground tunnel between France and Switzerland keep all data in computer and delivered to 170 computer centers of 135 countries. How much data can you imagine? If all this data is printed on paper the pages will be 15 lac crores. This is the picture of data science.

The world population is increasing rapidly. More population needs more energy. To meet the increased demand clean energy fusion is necessary for mankind and the storage of conventional energy will be exhausted within a short period. As the temperature of the plasma in fusion research is extremely high artificial intelligence is used in this research work.

By the above examples, it is proved that the necessity of data science and artificial intelligence cannot be denied.

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