[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]The world of data science is an interdisciplinary one involving mathematical and statistical skills along with high computational and programming knowledge and also the ability to understand business trends through large databases. The job of the data scientist is to analyse a massive amount of data and interpret them in order to help the organization make suitable decisions based on data prediction. Machine Learning being a newer technology therefore, can be put in the same plate as the data scientist based on their job importance. The field of machine learning involves the usage of big data and there analysis, narrowing them down through algorithms. Such values are created which can be put to further substantial use. The task is often repetitive and hence, the machines are taught to “learn” their work. In fact, the traditional hit and trial method of data analysis is becoming outdated and impractical as the need to interpret big data arises. Therefore, a data scientist can really not move ahead in the current organizational world if the person lacks the knowledge of Machine learning among several other skills.

Algorithms are an essential part of any ML training, and they serve to be necessary for data scientists. The data scientist has several overlapping skills with the machine learning expert namely adeptness in basics of computer, a credible knowledge of programming in several languages, through exposure to statistics and also skills in data modelling and data evaluation among others. As such machine learning can comfortably fit into the oeuvre of data science. The knowledge of the various techniques of machine learning- supervised and unsupervised, all come into being necessary for the data scientist. Nonetheless, data science has a higher perspective to look into more significant matters of applying the entire process into practical usage and hence involving the incorporation of experience in organisational trades. Ultimately data science involves parental branches like data analytics, software engineering, and business analytics and more including machine learning. Data science along with machine learning skills is quite in demand by organisations and gives you the very high prospect of improvement in your field.

Henceforth, machine learning is compulsorily added as a component of data scientist training. There are basics of ML which was already present in the course, but as the field has evolved, the presence of ML is more explicit. The filtering of data by algorithmic techniques comes in very handy for the data scientist to work on a specified collection of data. Classification of data is a crucial step for calculations and being able to detect essential predictions. Machine learning at this moment forms an explanatory core for the data scientist through which one can explain the successive discoveries. One can cite examples of the application of machine learning in the job of data scientist. A data scientist works at several organisational firms to analyse customer credibility based on the previous collection of data. The procedure could involve the customer’s transaction data and ratings per view. Next step would be to use an ML algorithm to work a prediction using any of the supervised techniques. One may use the decision tree to conclude that the customer was not creditworthy. The entire process can be showcased via using a visualisation of the decision tree with a stable structure which would be easy to explain.

Machine learning is a substantial extension of the field of data science such that it not only exemplifies the entire procedure but also makes further provisions for data analysis and data filtering. If you are well trained in ML, you can opt for a job as a data scientist.[/vc_column_text][/vc_column][/vc_row]

Artificial intelligence (AI) and Machine learning have a remarkable potential to accelerate, simplify, and improve many aspects of our everyday lives. Early results have simultaneously created colossal excitement and demonstrated its frightening potential. In the following infographics, we discuss the developing interest in Artificial Intelligence (AI) and Machine Learning in organisations. The data has been gathered by asking different questions to IT professionals of various levels.
The outcome as is as follows:

 

An Anatomy of a Data Scientist

The era of Big Data has created a talent gap for people who can pull actionable insights out of raw data. The data scientist – called “the sexiest job of the 21st century” by Harvard Business Review – is in demand, with a 5000% jump in job posts between 2013-2014. In India, the average salary for these sought-after scientists is around 650000 INR.

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Top 5 Data Visualization Tools

The advent of the internet, artificial intelligence, technology and vast amounts of data in our everyday lives has made the process of data visualisation, its use in the various means of buying, selling, post-selling and its presentation skills one of the prime tools in data analytics. The display itself has used every new device, beginning from charts, graphs to animation, virtual reality, augmented reality and everything in between to evolve in a chimerical fashion to make the process of presenting data at the opportune moment in an engaging, communicative manner the differentiator.
Should one compare the free and open-source alternatives with the paid data visualisation tools? The answer should be ‘yes’ because, in a world where speed, accuracy, flexible and robust solutions count, and data security could be an area of fatality, they best evolve with practice, use, development and customer retention which costs.

Let’s do a quick review of some of the visualisation tools and why they are regarded as the top ones.

Tableau

Tableau handles significant and rapidly changing data with ease. Integrating Hadoop, My SQL, SAP, Amazon AWS, and Teradata with AI and machine learning capabilities tweaks the presentation into dynamic visualisation which is easy to understand.

QlikView

Some of the most promising features are that it has a customizable set-up, a very widely used feature range, and a clutter-free interface. It does suffer in taking a little more time for full use of its potential but can be used for data discovery and exploration in conjunction with Qliksense for BI solutions with excellent reporting, analytics and intelligence capabilities.

Fusion Charts

Widely used over 90 chart types which integrate across platforms and frameworks give it flexibility. FusionCharts significant improvement is its use instead of starting a new visualisation from scratch by just using the “live” example templates and plugging in their data sources as needed.

Highcharts

Its focus on cross-browser support makes it an excellent choice when fast and flexible solutions with minimum need for specialist data visualisation training is needed.

Datawrapper

The package is widely used by media organisations to create charts, upload csv and present statistics in straightforward charts, maps etc. that allow quick embedding into reports.
Plotly and Sisense also find mention among the top few. In selecting the best data visualisation tools for 2018, the following devices have earned their place with Sisense and Tableau being apparent frontrunners.

Zoho Reports

With seamless integration, Zoho is user-friendly, has automatic report generation and impressive support availability. It is a business intelligence and analytics software that helps create insightful dashboards and data visualisations with reports on project status, burn down charts, utilisation of time, planned reports vs actual reports and so much more.

Domo

Domo offers storage, great sharing features and a range of connectors but is a little short when it comes to its interface and learning curve.

Microsoft Power BI

Known for its superior capabilities, compatibility and easy usability, Power BI has a horde of pluses including data preparation, data discovery and interactive dashboards. Users also can load custom visualisations. However, it only falters because the desktop and web versions divide the data prep tools with the refresh cycle being limited.

Google Analytics

This exceptional platform for the website and mobile app analytics relies on third parties for training and has way too much automation in customer support.
Chartio, SAP Analytics Cloud, IBM Watson Analytics and the evergreen Salesforce Einstein Analytics platform are also climbing the charts steadily.
Data is an invaluable resource and managing it, is a tricky task for business solutions. Larger enterprises can afford data analysis teams, but smaller firms rely on data visualisation tools. If one uses technology, infrastructure, big data, and visualisation tools, it is possible to streamline operations, internally to become leaner and more efficient. Last but not the least, it helps to understand your customers and earn their loyalty.
References:
https://in.pcmag.com/cloud-services/106561/guide/the-best-data-visualization-tools-of-2018
https://www.forbes.com/sites/bernardmarr/2017/07/20/the-7-best-data-visualization-tools-in-2017/#111b7fdd6c30

Predictive Analytics in Tableau

Success in most enterprises is reliant on the IT organization and technology to help in business operations, using big databases and predictive analysis for forecasting trends and finding solutions for real-time decisions. Tableau is one of the best tools available with its unique features like a built-in dashboard, requiring no R scripting or other scripting and being able to import your data in varied formats onto the panel.

Pillars of Predictive Analysis

The main components of Predictive Analysis are monitoring at any given moment of Big Data, the understanding of data analytics and effective utilization of data across the enterprise. Tableau can provide specific views of small events or co-relate information to present trends and forecasts in real-time. Tableau can thus ensure efficiency in allocating resources and increasing organizational effectiveness. The V8 version of Tableau allows you to set alerts and saves you and your clients from data disruptions and system downtime. Further, the Tableau package provides for security, improved governance, and reliable, flexible and robust solutions applicable across the entire organization by using data analytics, predictive analysis, and machine learning. Radically Tableau’s capacity as the best tool for predictive analysis reduces the time it takes to connect to your data, visualize, analyze, and ultimately find business solutions as in the New York City Health Solutions case.

Benefits of Predictive Analysis in Tableau

The takeaway illustrates that Predictive Analysis in Tableau has considerable benefits enumerated below.

• See all the facets of your IT organization
• Monitor in real-time – the utilization
• Allocate resources efficiently
• Deploy securely across the enterprise

Implementation Of Predictive Analysis Using Tableau

IT Architecture Building

The architecture of your analytics decides your data flowchart, where and when it is processed, which database to use, who will monitor the insights, how it is secured and most importantly how it will impact business goals. Data is the very life breath of an organization and its timely use in predicting trends and forecasts invaluable to business growth.

Become an Algorithmic business with Tableau

The algorithm can work, understand and process the data to give you gainful insights and trends. However, using these is the crux of the matter. Of course, the Tableau package is the most superior tool for predictive analysis and with its excellent features like
Examples:

• Online retail segment benefits significantly from data analytics in real time by catering to clients based on their purchase history, browsing habits and other demographics.
• The housing sector can predict trends, price products right, increase buying, selling, customer acquisition and retention.

Information can already be at your fingertips, to empower you and your organisation. However, this requires to be a data-driven business implementation of choices.

Align and Prioritize Analytics

An example of prioritizing is the Intel Corporation who evaluates analytics to meet and align with enterprise goals and grow trust and clients. The criteria they use to assess potential projects include

1. Executive sponsorship. Big Data analytics is limited by the stake holder’s involvement and dedication to business goals across all levels of the organization.
2. Finding and dealing with the right problem. Data and predictive analytics have no preferences and can quickly become a liability if the identification of the right insight aligned with business targets and growth is not identified and prioritized across the entire board.
3. Data needs to be used right. Quality of data and API availability are essential criteria that impact the feasibility and value of the project.
4. Resources. The skill availability, choice and use of tools, and processing power will decide how quickly the project gets underway and ends before deadlines.
5. Time to delivery
6. Projected benefits.

Conceptual use and Change Management

Big data and analytics of it need to serve the process. To prove the concept is to use the results effectively and, in this segment, it is entirely up to the organization to implement. Caution your data may grow, and your business will grow. Technology shall evolve and change. Hence at some future point changes to infrastructure may be needed.
Indeed the advent of the internet, artificial intelligence, technology and vast amounts of data in our everyday lives has made the process of predictive analytics and data visualization, its use in the various operations of buying, selling, after-sales and its presentation skills one of the prime tools in data analytics using Tableau.

An Overview on Natural Language Processing

Artificial Intelligence is a coherent system calibrated and fabricated in such a fashion that it resembles an epitome of analogy with that of natural Intelligence with an escalated structure and functionality where the room for evolution eclipses the natural intelligence by a cosmic margin. Germination and Evolution of Artificial Intelligence is a constant that establishes and sustains through a fundamental variable that is technology. As AI is disseminating the modern era through its nurtured growth there seems to exist some congruity in its caricature with respect to natural intelligence which needs to be comprehended to get the crux of this aborning technology.
Natural Language Processing (NLP) is one such approach used by the AI technology which renders the AI with a competency to interact with other foreign intelligent system using a common language so as to amalgamate and match the counterpart on a same altitude and platform.
Artificial Intelligence has garnered technological advancements such that harnessing said attributes becomes a convenient process in the future. Hence NLP process is designed with such a foundation that would enable and aid a robot to comprehend the language postulated by the user. To further cognize NLP, it certainly becomes imperative to assimilate components of NLP that aid in sufficing such functionalities.

Natural Language Understanding (NLU)

This domain deals with mapping a language into its comprehensive structure which can be assimilated conveniently. This rendition and mapping integrate a language with the desired language that could be deciphered by the machine.

Natural Language Generation (NLG)

NLP is not just a process that catalyzes the accumulation of words that a machine understands to get a certain task done, it is, on the other hand, a process that provides a meaningful structure to the word which than machine follows in the form of language. This meaning is exhibited by NLG which basically comprises three phases.

• Text Planning – Extraction of useful information out of the whole bank is introduced under this spectrum.
• Sentence Planning – This initiates the most crucial process where a proper meaning is formulated out of chosen words so as to form a phrase and an abstract structure which the machine takes into account to follow a certain paradigm.
• Text Realization – This phase marks the final script and formulation of a systematic structure is aggregated in this phase. This phase finally lends meaning to a command in the form of a language for the machine to follow.

NLP portrays a functionality and utility in a system by transitioning through various stages which fabricates the whole process into one spectrum. Hence bracketing those transitions is of critical importance as and when the process proceeds.

Lexical Analysis

Useful data is mined from certain sources which exist in an unstructured format in large chunks and pose a shallowness in comprehension by the machine. Hence to make the study easier the large chunk of data is segregated in the form of paragraphs, sentences and words which would further me oriented in a more orderly fashion so as to designate a meaningful outcome.

Syntactic Analysis (Parsing)

A Mere collection of words would not suffice a meaning to the accumulated data resulting in the desired output and a meaningful analysis of grammatical forefront is also of immense importance so that a proper communication is established for a smooth outcome.

Semantic Analysis

Post checking the sanctity of grammatical soundness in acquired data set further analysis is carried out to monitor the contradictory phrases and bifurcated them clearly.

Disclosure Integration

Final integration of sentence is carried out so that subsequent meaning arises out of sentences for the machine to assimilate.

History of Artificial Intelligence in 3 Minutes

Artificial Intelligence or AI is a concept that has been much talked about in the recent past. As the name suggests, Artificial Intelligence is not real, in the sense that it is simulated. Intelligence is a broad term that can mean different things. Generally, intelligence can be defined in different spectrums, ranging from logic to problem-solving abilities. Thus, Artificial Intelligence is a subfield of computer science that simulates machines and software to mimic the human cognitive functions. This leads to the production of machinery that observes, analyzes and outputs data just as the human brain would.
Here is a brief history of Artificial Intelligence that highlights how the concept has grown leaps and bounds in the recent years.
Ancient History: A fascinating fact about artificial intelligence is that it has been incorporated since as early as the 4th century B.C. According to Greek mythology, a blacksmith called Hephaestus manufactured mechanical servants to help him out with his work. With time, many models and toys were constructed with the same underlying principles. Examples of such models include the Archytas of Tarentum and the syllogistic logic model by Aristotle.
Also Read : The Promises of Artificial Intelligence: Introduction
History of AI since the last 100 years:

Rossum’s Universal Robots

In the year 1920, a Czech writer named Karel Capek wrote and directed a sci-fi play. This play introduced the concept of robots known as Rossum’s Universal Robots (R.U.R) The play depicted artificial people who are similar to clones in today’s day and age. This gave researchers an idea about Artificial Intelligence and Robotics and thus threw light on the importance of AI in society and research.

The Alan Turing Machine

Alan Turing, a renowned scientist, introduced a model called the Turing Machine. This machine is an abstract one that constructs the logic of any given algorithm. Up until today, the Turing Machine holds a special place in the field of Computer Science. Shortly after the World War 2, Turing introduced his famous Turing Test. This test was created to determine the intelligence of any machine. The test used one machine and one person. Both the machine as well as the person would communicate through a natural language. If a second person who is overhearing the conversation cannot differentiate between the person and the machine, then the machine is said to be intelligent.

The Dartmouth Conference

In the year 1956, the first recorded Artificial Intelligence workshop was held. This marked the start of research in the field where researchers from MIT, CMU and IBM met together and brainstormed ideas related to the concept. According to them, creating artificially intelligent machines will ensure that the work to be done by man will significantly reduce with time.

Expert Systems

Research in AI significantly decreased in the subsequent years due to lack of enthusiasm and funding by the US and British Governments. Thus, during the early 1970s, AI was a concept that was neglected. However, after a few years, expert systems were introduced. These systems are programs pertaining to a particular domain that answers as well as solves any problems posed to it. Thus, they emulate an expert in a particular domain and solve the posed problem according to certain rules that are set. The expert program used a knowledge engine that represented facts and rules with respect to a certain topic while the inference engine applied these represented facts to new facts.
Shortly after, in the 1990s, AI research suffered another setback due to lack of funding.
21^st Century: The 21^st century saw the growth of Artificial Intelligence that extended to Machine Learning and Big Data Analysis.
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Applications of Artificial Intelligence in Health Care

According to experts, a day will come when computers would be used to do most of the jobs, and healthcare is not an exception. Even in today’s world, computers, and machines are used in almost every walk of life. Advanced applications of technology featuring machine learning, artificial intelligence and automation have greatly affected the medical industry including hospitals and insurance companies. The impact is of a more positive nature when compared to other industries. 86% of companies are using AI in health care, including life science companies, and healthcare companies. It is estimated that these companies could spend upwards of $54 million by 2020 on AI.
Artificial intelligence in medicine can and is being used to manage records and miscellaneous data. AI can be used to re-format, trace, and store data which will provide faster access. This type of data management is one of the most widespread uses of AI in healthcare.
Also Read: 4 Industries Where Artificial Intelligence is Making a Huge Impact
It is also used to do repetitive jobs like analysing X-rays, CT scans and various other tests, along with routine tasks. These tasks can be done faster by machines than humans, as there is little to no variation in the pattern of the job.

As one of the main advantages of artificial intelligence is analysing data, things like reports and notes of a patients file, clinical expertise, and externally conducted research can be analysed by AI to select the best way to treat a patient. This can open up opportunities for customised treatments for individuals which might have been difficult in the past due to insufficient data management. In layman’s terms, AI can be used for treatment designs.
Digital consultation is also possible using artificial intelligence. There is an app called Babylon which is based in the UK and it uses AI for medical consultations. Common medical knowledge along with patients’ personal medical history is used to offer recommended actions. Symptoms are reported to the app, and the app uses speech recognition technology to compare the symptoms to a database of diseases, and illnesses.
Rather new and quite unheard-of use of artificial intelligence in health care are virtual nurses. This is mainly used by a start-up company Sense.ly, which developed Molly – the virtual nurse. The program of this virtual nurse employs machine learning to help patients by following up on treatments, and what to do with doctor’s visits.
Medication management can also be streamlined by using artificial intelligence. AiCure is an app developed by the National Institutes of Health, which is used to monitor the health of a patient. The app uses the front camera of smartphones to check if the patients are taking their medicines regularly.
AI can be of great use in developing new medicines. The primary way of making new medicine is to have clinical trials which take years and cost a lot of money. With the use of AI, the entire process can be completed much faster. Recently, during the Ebola outbreak, AI was used to scan medicines and find a way to redesign them to fight the disease.
AI can be used for precision medicine, which can be used to inform people of their potential health risks before they even happen. Genetics can be used along with AI scans of the body to spot eventual cancer and vascular diseases. Mutations can also be predicted by using AI in health care.
Digitising the healthcare system would make accessing data faster, and save a lot of valuable time. The best example of using AI in such a way can be found in the Netherlands, where 97% of all healthcare bills are digital. This can be used later on to create a treatment chart, and can also be used to find out any mistakes during treatment, and inefficiencies in the workflow.
Related Article:

• How is Artificial Intelligence Transforming Healthcare
• 10 Interesting Facts about Artificial Intelligence
• The Major Industries that Artificial Intelligence will Transform by the Year 2020

Technology is transfiguring itself at a brisk pace and amelioration is the only constant presence in the technological forefront. Business in the modern era is abetted via evolved tools and software’s and the life cycle of current practice in any business is diminishing by the dominion of even newer and evolved apparatus as they plummet in the cooperative circumference. Amidst all these transformed disruptive features there exists an entity that outsmarts in utility and efficacy by embellishing itself as the most valued asset of any organization generally known as Information. Information is the torchbearer of the inflecting dimensions in business which succours in establishing reforms for profitability. Information finds its exertion in the domain of forecasting and caricaturing an informed decision for the company’s subjects. Mining of information is articulated through large chunks of relevant data sets organized in any paradigm possible.
Also Read: What’s Big Data All About?
Big Data is one such bank and repository of immense data sets which requires a fine tuning and segregation to infer a surplus germinating through it. Apprehending, Garnering and finally Analysis of data sets are the eminent strides incorporated while deciphering Information. The traits and attributes incorporated in Big Data can be assimilated through the impending titles as follows:

Volume

Proportionality of refined information gets augment and amplified with the abundance of data present in the company. Business is accompanied in a more data-drove structure thus garnering the data from various sources such that a chunk of relatable bundles of data is sprouted which has an inherent ingenuity to extract potential insights and inferences which ensures the longevity of business in a thriving perspective.

Velocity

Rate of production of data is one of the most beguiling tasks arbitrated in the process and velocity at which the data is accumulated and processed for further discretion determines the potential of the company in the technological evolved forefront. Velocity also concerns itself with certain amendments and modifications related to data and more importantly pace at which the same is conducted so that an intuitive deduction is established for proceedings to follow in the industry.

Variety

Data accumulated and cumulated in the company’s credentials can irrevocably be considered as a valued asset. However, the value certainly gets escalated when the diversity and heterogeneity are incorporated in the data sets. As data is accumulated while referencing through various sources and spots, a sense of authenticity gets exhibited which ensures a differentiated yet potent data set that can be mined to revive some fruitful outcomes. The data sets accumulated from patent sources can sustain in various formats such as

• Structured Data Format: – The orientation of this data set is at the epitome of the organized structural spectrum where data sets are exhibited and portrayed in a user-friendly alignment which directly ensures segregation of useful data from various irrelevant data sets and the computing time to harness such information gets mitigated. The Structured format may also exist in the form of a Matrix structure comprising of rows and columns where credible data is easily maculated from the rest of the flock.
• Semi-Structured Data Format: – The Orientation exhibited is in the form of organized paragraphs where the useful inferences are drawn out through precise revision of each data set in the array.
• Unstructured Data Format: -No orientation is disseminated in the alignment and the data is dispersed comprising of videos, images and texts in the same structure.

Variability

With large data sets comes the inconsistency and issues of authenticity which needs to be constrained and bracketed in a closed sphere for an efficient and effective outcome through mining.
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• What is Big Data and Business Analytics?
• Job Roles and Responsibilities in Big Data
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