5 Must-Haves On Your Artificial Intelligence Resume

Robots, self-driving cars, and chatbots have swapped man for machine with artificial intelligence (AI). At the same time, they have created a huge marketplace for a diverse range of job roles with varying skill sets. While traditionally, analytics job roles required applicants with statistics or math background, the increased use of computation and sheer volumes of data now demand technical knowledge. So whether you are an engineer wanting to make your mark in an AI-driven environment, or a data analyst seeking to make a career in AI, you need to have certain core competencies.

These are the top 5 must-haves on your AI resume:

1. Languages – Java, Python, C, and C++

High-level object-oriented programming languages such as Java, Python, C, and C++ are languages navigated by most engineers during the course of their education. However, if you have come to data science from statistics or a non-engineering background, you need to learn these languages that are fundamental to AI. At the same time, learning server-side scripting languages such as PHP, ASP, JSP, Ruby or Perl is of immense use.

Learning any or all of these is not very time-consuming. You can easily learn them online, use their libraries to practice projects, or take short certificate courses. At the end of the day, the more languages that feature on your resume, higher the chances of your CV being considered.

2. Artificial Intelligence Competencies

Fuzzy Logic, Neural Networks, Cognitive Computing, Natural Language Processing (NLP), Data Analysis and Search technologies, APIs, enterprise architecture and security architecture; are some technological underpinnings of AI. Learning or mastering any of these will take you a step further towards landing a job role in AI. Each of these tools and technologies has applications in various scenarios or problem-solving. So wait no more. Go ahead and learn these AI skills-sets to score higher in the job application process. As your knowledge is expected to bring value to the company, you are more likely to be selected with these competencies on your resume.

AI jobs require a blend of expertise and skills in programming, data management, technology platforms and business development. However, these traits continue to be in short supply. So developing a balance between these skills in demand and work experience with computer science disciplines; enhances your prospects of getting the AI job you covet.

3. Machine learning

A part of AI, machine learning is increasingly considered indispensable for trends-spotting and iterative self-learning. With every industry applying AI, machine learning expertise is highly sought after. Its wide range of cutting-edge applications, emerging areas of machine learning-as-a-service, and applications overlapping with AI; have made it central to data-driven hiring. A study of machine learning can help you get a good job in companies leveraging machine learning and AI in their business intelligence process.

So look out for an institute in your city that trains you in machine learning, and provides expert help to train you on projects. Your machine learning certification will surely be a feather in your cap and lay the foundations of your AI career graph.

4. Computer Science, Programming Languages, and Coding

Engineers and specialists in AI require technical skills for designing, maintaining and repairing technology and software programs. Understanding your data, open source frameworks, computing platforms, supporting programming languages and coding; ultimately help you prepare for the rapid pace of technological innovation in data-driven insights. For instance, AI platforms like Microsoft Azure require knowledge of cloud, machine learning, and custom R or Python coding, while TensorFlow open source library requires deep knowledge of architecture.

As AI is a way of making the machine or software, intelligent, it is a foregone conclusion that degrees or certifications in computer science disciplines or tools are a plus factor for any AI resume.

5. Physics, Engineering, Robotics

As a student with a science or engineering background, physics, and engineering applications will be the mainstays of your knowledge-base. However, learning AI based add-on courses will strengthen your foundations in math, logic, and engineering. For instance, knowledge of sensors is a sought-after talent for its various applications of AI.

With AI robots being used across industries, like logistics and manufacturing, a short course on robotics enhances your Artificial Intelligence resume. Companies always look for multi-talented multi-functional professionals who can work across technologies and platforms, and grow in sync with the company visions. So the secret here is to look around and understand the industries and businesses implementing AI and the various use cases. Find which among these interests you and set about learning the tools that contribute to your resume, when you apply for the target job role.


While the technical skills on your resume will surely land a good job in Artificial Intelligence, you cannot ignore the supporting written and verbal communication skills that are needed to convey how the AI tools and services are deployed within the business or industrial processes.

So learn the technologies and programs required for an artificial intelligence job role, during your spare hours or over the weekend and build a great resume that takes you places!

To explore a career in Artificial Intelligence, click here.

Machine Learning and Its 5 New Applications

In less than 4 years your own personal devices will be so sophisticated that they’ll know more information about you than the closest people around you. This is just one of the many scenarios that are in front of us thanks to the advancements made by artificial intelligence. Machine learning is moving towards these advancements and for better or worse it is going to play a huge role in the world’s most important industries. And more and more IT professionals are shifting their careers in this sector that is only destined to grow.

Can we give a definition to machine learning and why do we have to pay attention to it? On a large scale, we can say that machine learning models are an application of AI in which algorithms independently predict outcomes. To get an idea of what we are talking about, machine learning takes advantage of these models that are capable to process large datasets extract insights and make accurate predictions without the need for much human intervention.

Many of this models implement machine learning with r and it’s no wonder that a lot of programmers choose to take a machine learning course to advance in their career or change their professional ladder altogether.

Going back to what machine learning is, we can state that many value-generating implications result from the accelerated development of this technology, and many are poised to radically streamline the business world.

In this article, you will find 5 new applications that are certain to change our day to day life in the nearest future, at least our business life at the beginning.

Reviews that can make or break businesses

Now more than ever people go online and browse sites such as Yelp to look at the reviews other customers left for a certain restaurant and make up their mind and choose which venue they will spend the evening at. Yelp knows that and in the last couple of years, it has implemented machine learning to better its picture classification technology. Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently. It will surely make a big difference when choosing a restaurant and when complaining about it!

The moment an algorithm understands your diagnosis.

Big Data and data science are already making a revolution by leaps and bounds in the healthcare sector. But what if incredibly accurate machine learning algorithms were capable to process large sets of healthcare data without breaching confidentiality contracts? what if a couple of lines of code could give you the results of your analysis or diagnosis, could tell you if you are at risk for particular diseases or genetic issues?

Now, this scenario may become a reality in the near future as Dr. Ed Corbett says in a recent scientific paper: “It’s clear that machine learning puts another arrow in the quiver of clinical decision making.

“Machine learning in medicine has recently made headlines,” said Corbett, the medical officer at Health Catalyst. “Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer.”

The retail store that already knows what you’ll buy tomorrow

 Do you need another incentive to learn machine learning? Just think for a minute about this statistic: the international retail sector has consistently generated over $20 trillion in sales a year for the past few years. This not only testifies that well being of this industry but also tells you that retail and online retail stores are gathering millions of raw and consumer-behavior data that could be split into consumer shopping patterns and tendencies. And yet, many enterprises are not taking advantage of this incredible amount of information, that’s why your role as programmer or engineer with a machine learning course come to play. There is a massive opportunity to implement machine learning models that enable retailers to better understand their customers and provide a more personalized customer experience.

Thanks to this well of data, you could enable any retailer with the ability to provide recommendations for the end user or better impact the whole customer’s journey, from his first visit to purchase, to the subsequent follow-up.

Separating chaff from the wheat in content and news delivery.

The case of fake news invading our social media feed has escalated to a point where Facebook announced that they will hire 3000 employees with the only purpose to monitor the platform’s newsfeed. The curation of content is not only a problem for Facebook and Twitter though. Other Silicon Valley giants like Google are creating teams of experts with the only intent to stop fake news from spreading.

Emerging machine learning and AI platforms, such as Orions Systems, are providing proprietary systems to “grow and adapt the interactions between humans and artificial intelligence” for tasks like moderating content at scale.

Taking a new stand against cybercrime

Recent studies have found out that cybercrime will cost to enterprises and companies more than 6$ trillion per year by 2021. It says that companies will invest over 1$ trillion in cybersecurity within that same year to prevent cyber attacks and data breaches. Researchers are developing clever ways to implement machine learning models to detect fraud, prevent phishing and defend against cyber attacks.

It is clear that machine learning is opening the doors to an exciting and interesting field for programmers and IT professionals. Taking a machine learning online course will benefit your career and give you the possibility to work in an ever-changing field and in different business verticals.

Top 10 Emerging Data Analytics Startups in India

Powered by specialized software and real-time analytics systems, big data & analytics caters numerous benefits for companies worldwide. Thus, the demand for big data Hadoop firms has been increasing exponentially. Moreover, it has led to escalated opportunities for budding data scientists, data professionals, statisticians, and analysts. Keeping that in mind, we bring you the ten emerging analytics startups in India to look out for applications.


Turing Analytics resorted to machine learning as the main technology to provide business solutions across the globe. With a team of 7+ intellectuals, TA has successfully established big data analytics solutions for national clients, like Shopclues, and global clients such as Tata, Kimberley Clark, etc.

Establishment year: 2015
Founder(s): Divyesh Patel and Aditya Patadia
Headquarter: Bangalore

USP: Implementation of neural networks and machine learning to provide visual recommendations and visual searches with quick and real-time data analytics solutions.

9. The Math Company

TMC emerged as a collaborative venture of big data analytics specialists, who recognized the demand for long-term Hadoop, big data, and analytics requirements among firms globally. With 50+ employees and growing, this data analytics company uses ML, visualizations, design thinking, and domain expertise to serve its clients, more than 15 of which are a part of the Fortune 500 list.

Establishment year: 2016
Founder(s): Anuj Krishna, Aditya Kumbakonam, and Sayandeb Banerjee
Headquarter: Bangalore

USP: The Math Company commits to end-to-end decision-making analytics and data-driven tactics for companies by collaborating with them at ground level.

8. Razorthink

Razorthink established as an emerging enterprise AI solutions provider. Funded by individual investors, the company targets adept computing through a distinct accumulation of AI optimization, automation, dynamic models, and data science. Their team of 100+ professionals has exhaustively supported big and small businesses to utilize AI-based customer services, risk management, fraud detection, intellectual process automation, and business predictions.

Establishment year: 2015
Founder(s): Gary Oliver, Dr. Nandu Nandakumar, Murali Mahalingam, Tom Drotleff, Harsha Nutalapati, Barbara Reichert, Rupesh Rao
Research Lab Headquarter: Bangalore

USP: Razorthink’s BigBrain is the main AI-based data analytics system that offers a simple drag-and-drop layout for dealing with model deployment, optimization, and data analytics.

7. G Square

G-Square sprouted as a collaboration between Gopi Suvanam and Gurpreet Singh in 2014. G Square Solutions targets B2B aspects where they provide clients with risk management, data marketing, sales, and financial technology. The firm was also awarded at CYPHER 2016 as the ‘Emerging Analytics Services Startup of the year.’

Establishment year: 2014
Founder(s): Gopi Suvanam, Gurpreet Singh
Headquarter: Mumbai

USP: G Square is proud to present its SaaS business model for data & analytics using unique plug and play solutions in financial services for family offices, NBFCs, banks and other wealth management firms.

6. Hiddime/Lead Semantics

Hiddime, or Lead Semantics, is a one-of-a-kind Cloud Business Intelligence company that focuses on data science solutions integrated with deep semantics via the internet. The firm created their tool for facilitating IT growth among entrepreneurs with little knowledge of the analytics world. Their internet-based data analytics model is a user-friendly interaction tool integrated with semantics, machine learning, graphs and NLP. Their achievements also awarded them with the “Analytics Product of the Year” award at CYPHER 2016.

Establishment year: 2013
Founder(s): Prasad Yalamanchi
Headquarter: Hyderabad

USP: Hiddime has successfully implemented point-and-click narrative analytics, which businesses can utilize for business predictions, cognitive analytics, and deeper insights.

5. Realbox

Realbox aims to deliver a distinct experience to its clients, mainly comprising of modern-day physical businesses.  The firm has been solving real-time issues related to big data analytics resulting from large quantities of customer data in India and abroad. The firm holds great pride in its flagship tool known as Pulse that works similar to Google Analytics and takes care of big data Hadoop analytics for physical enterprises.

Establishment year: 2015
Founder(s): Saurabh Moody, Arjun Sudhanshu, Preksha Kaparwan
Headquarter: Delhi

USP: Realbox uses Pulse as its primary predictive analysis and BI tool for utilizing risk management and boosting ROI.

4. Recosense Labs

Reconsense Labs have enabled an independent interpretation tool for generating optimized meta-data for users. This automated feature personalizes the content for individual users, promoting revenue generation, content exploration, and increase engagement. Their data analytics solutions incorporate ML and NLP frameworks to derive data based on user IPs, which is a more efficient model, unlike conventional clickstream data analytics.

Establishment year: 2014
Founder(s): Amith Srinivas, Shivkumar Janakiraman, Raghunandan Patthar
Headquarter: Bangalore

USP: Recosense have proven data and analytics solutions for travel, eCommerce, and financial services, which are generated based on real-time user interactions.

3. 3LOQ

3LOQ formulates NLP and ML frameworks to optimize the Fintech industry. The firm targets building AI-driven data analytics for permeating big data and consumer psychology so that they can extract revenue through BI. 3LOQ strengthens money and asset management systems in terms of digital banking services so that the end customers get streamlined and personalized communication. 3LOQ is growing stronger every day with 15+ employees.

Establishment year: 2014
Founder(s): Anirudh Shah, Sunil Motaparti
Headquarter: Mumbai, Hyderabad, Bangalore

USP: 3LOQ offers its Habitual AI platform that offers data-driven solutions for enhanced customer engagement in the financial sector.

2. Lymbyc

Lymbyc has a dedicated team of intellectual data scientists and analysts that have developed sturdy analytical tools accompanied with advanced technology. Their primary purpose is to enforce practical and optimized decision-making system for business leaders so that they can structure business foresight through natural language-based data inputs. Lymbyc works with 52+ employees to provide solutions in India and overseas.

Establishment year: 2012
Founder(s): Ashish Rishi, Satyakam Mohanty
Headquarter: Bangalore

USP: Lymbyc offers its unique expert analytics tool through its patented application MIRA (Machine Intelligence for Research & Analytics), which filters contextual data and formulates the necessary actions for converting them into data insights. MIRA works as a virtual data analytics scientist like none other in the commercial world.

1. CropIn

CropIn has proven its worth in the terms of interpolating SaaS model with the agricultural sector. Keeping the crisis in the agrarian business in mind, CropIn started providing solutions for farmers concerning financial analytics, weather foresight, data interpretation, satellite monitoring, geo-tagging, AI, big data analytics, etc.

Establishment year: 2010
Founder(s): Krishna Kumar, Kunal Prasad (Great Lakes alumnus), Chittaranjan Jena
Headquarter: Bangalore

USP: CropIn provides with ML- and AI-driven weather analytics, satellite monitoring, proprietary algorithms, for the agricultural technology. It provides with improved farming sales, risk management, data security, etc.

Data analytics is the future where every data company requires proficient scientists to handle the huge chunks of data that needs to be filtered for maximizing output. Therefore, you can learn big data online through reliable courses that commit to tutoring in various data and analytics courses at reasonable rates.

To explore a career in Data Analytics, click here.

Artificial Intelligence Decoded

One of the major technologies disrupting industries and business processes is Artificial Intelligence (AI).  It has become so pervasive in everyday life that you come across AI applications in almost every aspect of your life. Complex real-world solutions like tumor detection, to internet search and industrial robots; AI redefines the way things work. Design intelligent agents use machine learning and logic to solve problems. The world of gaming has undergone a massive transformation with the use of AI to enhance the human players’ gaming experience. Messaging apps have also witnessed wide AI implementation with image recognition, filtering, and other functionalities.

AI has thus emerged as a game-changing technology in as diverse areas as healthcare, medicine, agriculture, education, banking and finance, e-commerce, industrial processes, logistics, customer engagement, social media and various internet activities. As AI reshapes our world, we need to understand what the term means and how the technology adapts works in different environments.

So what is Artificial Intelligence (AI)?

Artificial intelligence is defined as a branch of computer science dedicated to making intelligent machines and programs. The father of Artificial Intelligence, John McCarthy, established artificial intelligence as The science and engineering of making intelligent machines, especially intelligent computer programs.

In other words, AI is the theory and practice of applying science, logic, and engineering to machines and computer programs in a way that they exhibit the characteristics associated with intelligence in human behavior – perception, language processing, reasoning, planning, problem-solving, learning and adaptation.

Knowledge representation and reasoning are closely linked components of AI used to create artificial models based on the information. While knowledge representation analyses the various types of knowledge used in everyday life; reasoning is the process that enables us to make judgment, decisions, and prediction.

To get the essence of AI, you need to have a basic understanding of knowledge and how the various knowledge types are used to map relationships and establish rules. Declarative knowledge is facts about objects. Structural knowledge refers to relationships between objects and concepts. Meta knowledge is the understanding of knowledge. Procedural knowledge covers the rules, methods, and procedures. Heuristic knowledge represents the rule of thumb. These knowledge types are ultimately “represented” as symbols, images, numbers, graphs, and networks (relationships); and make up the umbrella word knowledge representation.

Reasoning is the mechanism of applying “reason” or logic on the “knowledge” once it is “represented.” In other words, reasoning is the process of deriving logical conclusions from given knowledge.

Perception applies the “sensing” element of humans to the machine systems where data is acquired by sensors. So the systems can acquire, interpret, select, and organize sensory information in a meaningful way, as in aircraft sensors.

Learning refers to the process where knowledge or skill is gained through study, practice, experience, or by being taught.

Problem Solving is the application of perception to decide upon the best path or alternative result, from a given set of possible alternatives to reach the desired goal.

Linguistic Intelligence refers to the ability to use, understand, speak, and write a verbal or written language for two-way communication.

Planning concerns identification of goals, formulation of strategies or action sequences for implementing a task. In AI this typically translates into choosing and executing a set of actions as taken by intelligent agents and unmanned vehicles.

Motion and manipulation is the human ability to move based on knowledge, reasoning, perception or other decision. AI uses this for object manipulation in autonomous robots.

The ultimate research goal of AI is to create intelligent systems that display intelligent behavior and think and reason like humans.

How does Artificial Intelligence work?

AI can be manifested in physical machines like robots and self-driving cars, or virtual machines like programs. In both, the machine displays intelligent behavior – recognizes objects or voices, senses changes, applies reason, learns from data, makes decisions, or plans – just as any intelligent human being would do.

How do you measure the intelligence level of the machines?

By using the Turing test.

Just as psychometric and aptitude tests measure the cognitive or logical thinking and reasoning capacity of a human; the Turing Test measures the intelligence of the machine.

The Turing Test is a must-know terminology for every wannabe AI engineer or professional. The term was coined by Alan Turing in 1950, who is also credited with pioneering machine learning in the 1940s. The test was devised as a rudimentary method of finding “whether or not a computer is capable of thinking like a human being.”

How does AI work in different scenarios?

AI integrates seamlessly with the analytics program, for deep data-driven insights. It takes into account the convergence of people, process and technology, and their relationships. In marketing, AI is used to automate real-time offers, engage in chatbot conversations with the customer and sift through huge data to improve the accuracy of personalized offers. In banking, one of the most powerful AI function is in the fraud detection system where linkages can be established from many hidden and complex layers for discovering suspicious transactions. In the medical field, AI techniques in deep learning, image classification, and object recognition can detect cancer from MRIs and X-rays with the accuracy of a highly trained medical practitioner. By leveraging automation, conversational platforms and bots, machines are mining large amounts of sensor and other data to improve technologies at home, like security intelligence. Deep learning is combined with analytics engine in a high-performance computing environment for making instant market predictions in high-frequency algorithm trading.

These are some examples of how AI can work in multiple roles and different industry applications.

Why is Artificial Intelligence important?

Everyone around you is talking about AI and how it has invaded every aspect of our lives and beyond. This is true. Decades of extensive research has made the applications of AI technologies diverse and more useful.

Here are some reasons that will satisfy you why AI is important, and why you should think of a career in AI engineering:

  • – AI automates iterative learning through data.It performs frequent, high-volume, tasks with precision.
  • AI embodies intelligence in existing products.
  • AI adapts to progressive learning algorithms to allow the data do the programming. Through training and more data insights, it provides the best answers.
  • – AI can analyze hidden layers of data, for intelligent analysis in real-time.
  • – AI provides high accuracy through deep neural networks, getting more and more accurate with use.
  • AI gets the most out of the data as its algorithms are self-learning, making the data itself an intellectual property for competitive advantage.

The Artificial Intelligence technology cluster

Over time, specialized research has extended the functionalities of AI across business processes and domains. New fields have evolved to examine ways how to apply rules and logic to make a program or machine “highly intelligent.” There are various offshoots of AI finding prolific use as listed below.

Knowledge Engineering is a key offshoot of AI that creates knowledge-based systems. It is based on large amounts of knowledge, rules and reasoning methods to provide best answers to real-world problems. Advanced research has developed an expert system that can be designed to imitate the reasoning processes of a practitioner or expert in the domain under consideration.

Machine learning gives machines the ability to “learn” using algorithms to discover patterns. By automating the analytics engine using neural networks, statistics, operations research and physics; machine learning technology discovers hidden insights in the data without being programmed. Instead, it learns from iterations to look for insights and make decisions and predictions, side-stepping the need to be programmed.

A neural network is a type of machine learning that uses interrelated units to process and relay information between each unit. The process is aimed to discover connections and meaning from unknown data.

Deep learning is a subset of machine learning and the most advanced of AI technologies. Deep learning uses advanced techniques to make machines mimic human intelligence most closely. It makes use of massive neural networks, high computing power and enhanced training to learn complex patterns in huge amounts of data. Pattern Recognition and Image Processing are examples.

Cognitive computing is a subfield of AI that aims at a natural, human-like interaction with machines. The ultimate goal is for a machine to replicate human processes through images and speech and intelligent response.

Computer vision deals with pattern recognition using deep learning to identify given elements in a picture or video. With machines being able to understand, visualize and process images, the ability to capture images in real time is used to interpret the setting. This finds use in imagery and video analysis, for crime and military purpose.

Natural language processing (NLP) is the talent of computers to understand natural languages in their native forms, for further analysis and processing. It supports conversational interface in natural language creating plenty of user data for further analysis.

Soft Computing is a field that is used to build intelligent and wise machines. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty or partial truth, just as a human; which makes it the perfect tool to solve real-life problems.

Additionally, several software programs and techniques support the AI technology cluster for more diverse and robust applications.

Graphical processing units (GPU) is core to AI as it provides the computing power required for iterative processing, as in training of neural networks.

The Internet of Things (IoT) is an indispensable element of the connected world. This generates voluminous data from connected devices and sensors. AI implementation in the IoT network allows using the data for insights and action.

Advanced search algorithms are key to AI implementation, with the intelligent processing of data. The capability of algorithm design is limitless, and its advantages of understanding complex systems to identify unique scenarios are considered indispensable. AI sifts through massive of data using fast, iterative and intelligent algorithms, which allow the program to self-learn from patterns or features in the data.

In summary, AI leverages the power of computing algorithms to model exactly how humans think, act, and agents should think or act

APIs, or application processing interfaces, are packages of code critical to AI functionality in products and software. They can add more value to AI capabilities with descriptions, and call outs.

Use cases of Artificial Intelligence

  • – Post Office – automatic sorting of mail for address recognition, knowledge-driven image interpretation for handwritten envelopes
  • – Credit card companies – automated fraud detection, scoring
  • – Utilities – automated voice recognition for inquiries
  • – Healthcare – personalized medicine recommendation, highly accurate x-ray and scan readings for diagnosis of critical and genetic illnesses
  • – Retail – personalized recommendations
  • – Automotive – self-driving cars
  • – Manufacturing – analyze factory IOT data to forecast load and demand; use sensor data for predictive maintenance especially of high-value assets like aircrafts
  • – Banking – track customer activity for red-flagging unusual or suspicious transactions; use of chatbots like HDFC “Eva” for handling customer queries; robotic AI software for automating various banking functions; automated loan application scoring; automated signature verification system
  • – Finance – analyze historical and real-time market data to predict trends in a fast-paced trading environment; identification of rogue trading practices and other misconduct; automation of underwriting and risk management
  • – Insurance – pattern recognition in user data to identify business opportunities and help reduce fraud; use client data from IoT network for developing best-fit insurance products
  • – E-commerce – identify high-value targets/customers to create leads, autonomous forklifts and robot warehouse workers are already retrieving boxes for shipment.
  • – Human Resource – applications can be filtered and contextual reasoning added for smart recruitments of the best-fit candidate for a job
  • – Home appliances – learn from user behavior and trends to perform smartly without human intervention
  • – Smartphones – personal assistants to provide answers
  • – Customer service – use chatbots for powerful customer engagement
  • – Business processes – with use of cognitive capture, records are stored digitally and processed for intuitive intelligence in case of management, for instance, contracts and FIRs.
  • – Video Games – generate responsive, intelligent behavior in non-player characters, similar to human intelligence
  • – Sports – use image capture for optimizing filed position and strategy
  • – Military – analyze military drone footage
  • – Internet – intelligent, pre-emptive and intuitive searches

The Future of Artificial Intelligence

The future of AI is marked with a race against time, as man strives to make machines more intelligent than humans! What was a fascinating aspect of science fiction has now become the most powerful technology disruptive everyday processes in industries and businesses, and human touchpoints? With continuous breakthroughs in AI research, across domains and use cases, AI is being implemented by one company after another, at a breakneck speed.

The Career Scope

Thus, AI is based on several disciplines that contribute to intelligent systems – mathematics, biology, logic/philosophy, psychology, linguistic, computer science, and engineering. You need to have a certain level of expertise in math, probability, statistics, algebra, calculus, logic, and algorithms.

Besides, these are areas, where you need to boost your AI learning, with courses or certifications in

  • Bayesian networking or graphical modeling; neural nets.
  • Physics, engineering, and robotics.
  • Computer science, programming languages, and coding.
  • Cognitive science theory.

Some of the popular job roles in AI in demand are

  • Software analysts and developers.
  • Computer scientists and engineers.
  • Algorithm specialists and trainers.
  • Mechanical engineers.
  • Manufacturing and electrical engineers.
  • Research scientists
  • Engineering consultants.
  • Military and aviation imagery analysts or engineers working with flight simulators, drones and armaments.
  • Robot personality designer and trainer
  • Autonomous vehicle designer

So retrofit yourself for a career in artificial intelligence. Learn artificial intelligence and make yourself ready for an AI-driven future.

Non IT? No Problem! You’re Still Welcome In Business Analytics

Does it sound too good to be true?  Entering a competitive job market such as business analytics is not an easy path. You could feel even more discouraged because you don’t have job experiences in IT or because you don’t hold a degree or an executive MBA in this area. Fear not, you are still eligible for a great career in business analytics and companies are demanding a great number of business professionals that can blend their skills with analytics skills even though you don’t have prior experience!

Without going too further in the past, business analytics training was all about sifting through data of corporate databases, downloading the data into a desktop spreadsheet tool such as Excel, and creating pie charts, bar charts, columnar reports. “The tedium of the process generates challenges for our business partners, who might run many different types of promotions with both flat and tiered offer structures, but are still expected to rapidly turn around the results of their efforts,” said John Stanisic, manager of customer and marketing analytics for Points, a company that provides analytics tools for business loyalty programs and on the other side offers the end customer the ability to manage the loyalty points they earn.

Thanks to a business analytics certification even non-IT people can manage big data without losing weeks or even months of business hours. This is the answer to a big knowledge gap that people without an engineering background are filling up quickly, thanks also to the skills they developed in their past job experiences.

Let’s dive into some solid statistics now. With a simple research on business analyst course, you can find out by yourself that at the moment there is a huge demand for business analysts. There are more than 26000 open positions for this job highlighted by Google alone. What if you could enroll in a business analytics certification and find yourself within 6 months in a brand new position, with an even increased salary? And yes, this could become a reality within this year if you act quickly.

Business analytics is such a multidimensional role within the realm of big data analytics because you have to deal with the IT department as well as the customers. Having programming skills is not central in the business analyst role (as you can find out in a business analyst training), since providing soft skills and knowing how to analyze data is much more important than taking on the job that belongs to IT engineers and programmers. In a  business analyst course, you won’t be bothered with any programming at all. Your main requirement will be understanding, developing, managing, and functional testing. So as an entry level business analyst in a business analyst training you have to master 4 business analytics skills. Let’s see what they are.

The first skill you want to hone is requirement analysis and modeling. These techniques and processes are related to the core IT processes. This skill may need a learning curve for professional with no prior job experience in IT. Which is why you should take a business analyst course (in Bangalore you will find plenty of them). Don’t be discouraged by it because that’s one of the first things you will learn in a high-quality business analyst course in India.

A break-up of these skills are as follows:

  • – SDLC & Requirements understanding (User Stories, Use cases etc)
  • – Process Modelling (Activity diagrams & flow chart)
  • – Data Modelling using E-R diagrams
  • – Developing requirements specifications (SRS, Backlogs etc)

Another crucial skill you want to get the grips of in a business analytics training is functional testing. Functional testing is the last step before a software is delivered to the customer for acceptance testing. This is the part where you have to interact with a customer to gather their needs and observations. So this stage of the process requires you to make sure the software can match the standards of your customers and can meet or go beyond their expectations.

When it comes to functional testing you have to foresee every possible scenario where your product could be employed. At every point of the customer’s journey, the interaction between them and your product has to run smoothly. There must be no mistakes on your part.

Talking about the customer’s journey, as a business analyst, it is of paramount importance that you understand how your customer behaves and interacts with your end product. Gathering all this knowledge will require some time while at the same time you keep making more and more tests. For non-IT professionals, this is an advantage because you have already gained this skill in previous jobs. It can’t be said the same for programmers or IT engineers.

Finally, you have to get a deeper understanding of how to communicate with your customers and your development team as well. These two parties don’t talk the same language, so you play the part of an intermediary between these two worlds.

Going from one job environment to another which is completely different from the previous one is no easy task. And yet you have great chances to succeed. It takes courage, perseverance and the will to study a new skill set, and you will be on your way to becoming a well-rounded business analyst.

How To Solve The Biggest Industry Problems With Big Data Analytics

Picture this scenario: you’re at the bank for a normal transaction. Even before it’s your turn an employee informs you that you are eligible for a special agreement on your loan. And that’s because you are one of the few bank clients that have an excellent credit score and your business venture has been categorized by your bank as reliable and successful.

Does it sound too good to be true? How is it possible that your bank has real-time data about your business? It all happens thanks to big data analytics and its smart employment. Some decades ago it would have taken the effort of hundreds of people to gather and measure data that is now available at your fingertips with a simple click.

Big data analysis doesn’t stop at bank transactions, it is revolutionalizing the healthcare industry, as well as IoT devices, and the study of customer behavior.

Here you will find the biggest industry problems data and analytics are solving right now.

More often than not, patients in hospitals and other healthcare facilities are the victims of identity theft, fraud, and abuse. Big data is playing a huge part in decreasing the number of fraud cases. Big data analysis helps financial service firms to recognize behavioral patterns and anomalies so that they can protect their customers’ security while reducing loss due to fraud.

Talking about the healthcare sector, sentiment analysis and big data are capable to collect data about the customer behavior and the customer journey. Big data tools such as Hadoop can sift through millions of tweets to determine customers’ opinions about a certain drug and enable a pharmaceutical company with better advertising decision about their product. Hadoop and big data make easier to gather information about a customer and create targeted ads accordingly.

Besides drugs and fraud, more and more hospitals are now embracing RFID chips and sensors to track the patient’s experience within their facilities. The patient is now seen as a customer, not just someone looking for a treatment. These chips can tell the hospital how the patient is behaving in the structure, how he or she is interacting with doctors and the hospital staff, what the pain points are during his visit and what can be improved during his stay. Within a relatively short period of time, the hospital can gather a well of information about the customer experience and can create an experience that is satisfying for the patient. The healthcare facility can shape a workflow for its visitors and solve business problems with real-time data.

Another interesting revolution big data analytics is leading is how databases are changing the way they used to work. Unverified, untagged data was all over the floor once. But now thanks to data and analytics more and more companies are building structured and functional databases. For example, the U.S. Patent Office is managing now five million patent applications by entering two million pages of data every month with OCR scanning and tagging. Their database now is user-friendly since users now can enter a specific query to find what they’re looking for in a matter of seconds.

Another sector closely related to big data analytics is SaaS solutions. SaaS clients can collect real-time data about their customers’ experience and performance. Collecting this data allows them to recognize in time unusual behaviors. This is used to recognize abnormal patterns of network activity or behavior, such as service degradations, bandwidth events, and security incidents such as DDoS attacks. Big data analysis for network routing and peering lets customers understand how their traffic is behaving as it transits neighboring networks, and plan network changes to optimize cost and service quality.

Going back to customer experience, online retailers and e-commerce are relying on data analytics more than ever. To personalize their customer’s journey on their site, they have to provide solutions in a timely fashion, they have to constantly improve their interaction with the consumer. Big data analytics offer an answer to all these questions. And thanks to the improvement brought by big data, a customer is more likely to become a returning client for the e-commerce site.

What about retailers that interact with customers every day? What’s in it for those vendors who have to deliver the physical products to their clients in time and in a perfect state? Big data plays a big role for them as well. By crossing traffic and weather data, a retailer can predict and overview how and when the package will be delivered. Retailers are tracking when the truck is due so they can have the necessary people to unload the truck.

Let’s go back to the bank example, shall we? Many banks hit a roadblock when legitimating a small business information. Many struggle to identify whether that business exists or not. They may hire hundreds of people to get that piece of information. Now, thanks to big data, banks can access a structured workflow platform where every subscribed business has a name, search on Google, maps, and website attached.

To wrap this up, we can fairly say that big data analytics goes well beyond some fleeting numbers flowing in the air. Data & analytics solve real-world problems, and in a big way!

Which Career Path Does a Supply-Chain MBA Hold for You?

According to an ASSOCHAM report, India spends close to 14% of its GDP on transportation and logistics, which is almost double of what other countries spend – around 8%. Besides, a Business Standard report on the Indian logistics market shares that India is growing by leaps and bounds in this sector and is expected to become a US$ 307 billion industry by the year 2020.

The Growing Demand for Skilled Supply-Chain Specialists

Amy Cathy, Executive Director of the supply-chain MBA program at University of Tennessee, Knoxville, gives a very precise version of what supply-chain is all about. According to her, in a supply-chain, one has to source goods, make them, and get them to consumers.

Apart from the growing demand of consumers, other contributing factors for skilled supply-chain specialists are technology and the global market.

Michael Hugos, the author of the book Essentials of Supply Chain Management, states that projected job growth is one of the major reasons for the booming of the industry along with the impact of technology and dynamic changes in the global market. He adds that the life of electronic goods is not measured in years but months, pushing companies to evolve their supply-chain systems.

If you are a supply-chain MBA aspirant, this breakdown of in-demand job roles will give you an idea of the scope of your career path in this field.

Industry Analyst – Responsible for interviewing personnel from manufacturing, logistics, warehousing, and procurement divisions to set up business processes and optimize supply-chain workflows.

Project Manager – Liaise with a team of consultants to ensure everyday operations take place without hassles, supervise the work of analysts, and ensure projects are delivered under agreed cost and time metrics.

Global Logistics Manager – From warehousing and distribution operations to planning, forecasting, managing customer service personnel, and taking care of logistics information systems, the global logistics managers have a number of tasks to oversee. Besides, they also come up with supply-chain metrics, strategize, negotiate and initiate contracts with suppliers and vendors, and supervise everyday operations.

Transportation Director – They oversee outbound and inbound delivery of materials and products from distribution centers, budget transportation costs, and maintenance carriers, supervise third-party transport vendors, and manage invoicing. They are also responsible for the smooth moving of the carriers and freights across frontiers.

Supply-Chain Sales Manager – They outsource tasks and work to third-party vendors who offer logistics solutions, connect and sell supply services, and manage accounts.

Supply-Chain Consultant – A consultant works with several companies, comes up with strategies for coordinating supply-chain processes, provides tips and insights, and optimizes processes. Consultants are in demand owing to logistics companies liaising with decentralized distribution centers in different countries.

Procurement Analyst – They work closely with a company’s purchasing department, analyze historical data, assess purchasing cost of materials, estimate future costs, and research and find prospective vendors. They also negotiate costs, initiate contracts, and manage suppliers once they are on board.

The pay scale of supply-chain specialists is lucrative in India and other international markets. The annual Salary Survey conducted by Logistics Management revealed that the base salary for supply-chain managers on average is $111,994 in the United States. In India, according to a PayScale study, the median average salary for supply-chain managers is Rs. 8 lakhs and that of a supply-chain consultant is Rs. 9 lakhs, both of which increase with experience.

How Globalization has Opened Up New Avenues for Supply-Chain Management Graduates

John Flower, Carey’s supply-chain management division’s chairman, shares that MBA graduates in the supply-chain sector will need to keep in mind the global market when taking up the course. In his words,”They will be working a global network of suppliers and a global network of customers.” When it comes to the required skills, he adds, “It requires the mental agility of a good stockbroker. You’re always weighing things. You’re always watching world markets.”

The statements hold weight as India is expanding rigorously in terms of infrastructure, supply-chain network, and working on waterways, railways, and cargo to meet the increasing demands. According to Business Standard, the ‘Make in India’ campaign and the estimated growth of the Cargo and Logistics in India by CAGR of 16% in the coming years are bound to create more opportunities in this sector. This indicates a promising and rewarding career path for an MBA graduate in supply-chain.

Difference Between Data Science, Machine Learning, and AI

Even though the terms data science, machine learning, and artificial intelligence (AI) fall in the same domain and are connected to each other, they have their specific applications and meaning.

Driving the success of Data Science

We will start with the term Data Science, as it assumes the top-most position in the hierarchy of data-related technologies.

Data Science

Data Science is an interdisciplinary field of systems and processes to extract information from data in many forms. It builds and modifies Artifical Intelligence Softwares to obtain information from huge data clusters and data sets.

Data science covers a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, etc.

Data scientists are exceptionally skilled professionals whose expertise allows them to quickly switch roles at any point in the lifecycle of data science projects. They can work with AI and machine learning both.

Sidetrade, a leading company in the domain of data-science, realized, early-on, the data exploitation challenges its clients faced and immediately set up a dedicated Data Scientist team to work with its Product Managers, aptly put it:

“Data Scientists, of course, have to work closely with IT development teams to guarantee the usability of any solution once it’s in production”

Jean-Cyril Schütterlé VP Product & Data Science, Sidetrade Group

Data Science and AI

Artificial Intelligence represents an action planned feedback of perception.

Perception > Planning > Action > Feedback of Perception

Data Science uses different parts of this pattern or loop to solve specific problems. For instance, in the first step, i.e. Perception, data scientists try to identify patterns with the help of the data. Similarly, in the next step, i.e. planning, there are two aspects:

a) Finding all possible solutions,
b) Finding the best solution among all solutions

It is data science that creates a system for part b above using part a.

Data Science, Machine Learning, and AI

Although it’s possible to explain Machine Learning by taking it as a standalone subject, it can best be understood in the context of its environment, i.e., the system it’s used within.

Simply put, machine learning is the link that connects Data Science and AI.

That is because it’s the process of learning from data over time. So, AI is the tool that helps data science get results and the solutions for specific problems. However, machine learning is what helps in achieving that goal.

A real-life example of this is Google’s Search Engine.

  • Google’s search engine is a product of data science
  • It uses predictive analysis, a system used by Artificial Intelligence, to deliver intelligent results to the users
  • For instance, if a person types “best jackets in NY” on Google’s search engine, then the AI collects this information through machine learning
  • Now, as soon as the person writes these two words in the search tool “best place to buy,” the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most probable suffix to the query that the user had in mind.

Visual representation of the linkage between AI, Machine Learning, and Data Science

The diagram above is a helpful visual representation of the linkage between AI, Machine Learning and Data Science.

To be precise, Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning.

Deep Learning is a form of machine learning but differs in the use of neural networks where we stimulate the function of a brain to a certain extent and use a 3D hierarchy in data to identify patterns that are much more useful.

To explore a career in Artificial Intelligence, click here.

Top 5 E-Learning Trends in 2017 and Beyond

The last few years have been exciting for the e-learning industry as it evolved dynamically to accommodate the latest advances in technology and changing user expectations. It experimented with different e-learning tools, delivery methods, and platforms. Looking ahead, this space is headed for further transformation as the conventional, one-size-fits-all approach of learning becomes passé; and personalized, interactive content becomes the order of the day.

Let’s look at the key trends that will drive the e-learning transformation in 2017 and beyond:

  1. Keeping it Short: In 2017, lengthy, content-heavy course modules will give way to short, bite-sized courses. Survey results indicate that human attention span has been shrinking and Millennials have an average attention span of mere 90 seconds. Thus, the mantra for e-learning institutes is to keep the content short and crisp. Given this fact, microlearning, a methodology of delivering content in very specific bursts, is projected to gain momentum. Expect short learning nuggets of 4-6 minutes, focusing on just one learning objective, which can be accessed on-the-go on smartphones and tablets.
  2. Thinking Mobile First: Mobile learning has been a top e-learning trend for almost 5-6 years now and this year is no different. In fact, it is here to stay and grow bigger. Major efforts in the last couple of years have been directed towards offering courses that have the ability to run seamlessly on multiple devices—from tablets, smartphones to laptops. As we look ahead, the emphasis would be on adopting a mobile first approach while designing an e-learning course.
  3. Personalized Content: Personalization has become a buzzword today in every industry and is also slated to receive its fair share of attention in the e-learning domain. As we look ahead, leading institutes will start analyzing data to target learners with specific content customized for them. Armed with data insights, institutes can provide a personalized learning experience for learners based on the skill level. For instance, different content can be served to learners based on their scores in the assessments.
  4. From Passive to Interactive Video: Recent years have seen a phenomenal rise in the usage of video content thanks to mobile devices with powerful display coupled with high-speed connectivity. According to the findings of a Cisco study, 60% of mobile data traffic today is video, which is expected to account for 78% of the world’s mobile data traffic by 2021. Nearly a million minutes of video content is expected to cross the network every second by 2020. The trend of increasing video usage can also be seen in the e-learning domain. Reputed institutes are increasingly integrating video lectures and tutorials in their courses to make learning fun and engaging. Going ahead, we will see the focus moving from passive videos towards making videos more interactive. Typical video courses today cover an entire topic content, which is then followed by an assessment. In order to add more value to the learning experience and measure student comprehension, the emphasis will now be on engaging students within a video.
  5. The Rise of Social and Collaborative Learning: Social learning is more than just a fad. Forward-thinking institutes are increasingly using platforms where learners can network, share ideas and learn others’ perspective on a particular topic. Going forward, social interaction and collaborative learning through live chats, message boards, or instant messaging to enrich the overall learning experience will gain further traction. The emphasis would be on moving away from one-way learning to collaborative learning, which will encourage teamwork and exchange of ideas for problem-solving.

With all these changes underway, interesting times lie ahead for the e-learning industry. One thing is quite evident, modernizing the learning experience to meet the needs of today’s learners will be the core factor in shaping the future evolution of the industry.

Also Read: 

How Gamification Can Transform Education

Adoption of technology in online education