Scientists across the globe are looking forward towards formulating new methods to realize ‘quantum internet’, an unhackable internet, which connects particles linked together by the principle of quantum entanglement. In simple terms, quantum internet will entail multiple particles striking information at each other in the form of quantum signals – but specialists are yet to figure out what it actually does beyond that. The term ‘quantum internet’ is quite sketchy at this moment. There’s no real definition of it as of now.
AI is handling insurance claims and basic bookkeeping, maintaining investment portfolios, doing preliminary HR tasks, and performing extensive legal research and lot more. So, do humans stand a chance against the automation apocalypse, where everything, almost everything will be controlled by robots?
What do you think? You might be worried about your future job opportunities and universal basic income, but I would ask you to draw a clearer picture about this competing theory – because, in the end, this question might not even be a plausible and completely valid question. Why, I will tell you now.
Televisory, a start-up based out of India and Singapore, has launched its data analytics and operational benchmarking platform. The platform can measure real-time operational and financial performance of companies. While the firm has chosen to launch its platform from the US, its services are available globally.
The internet has killed some newspapers’ lunch, but it also presented them something truly remarkable – Data Journalism.
Introducing Data Journalism
Data journalism is an amalgamation of a nosy reporter’s news sniffing capabilities and a statistician’s fondness for data analysis. By scrounging through vast amounts of data sets that are available through extensive connectivity, data journalists are using this data to etch out interesting stories.
HBO’s fiery fantasy saga Game of Thrones Season 7 premiered last Sunday – by now the ardent stalwarts of this epic series have figured out the characters that matter the most. Just recently, a reputable data analytics firm Looker revealed some interesting facts, based on the data accumulated. So, want to know who secured the topmost rank?
Well, human-AI relation needs to improve. Amazon’s Alexa personal assistant is operating in one of the world’s largest online stores and deserves accolade as it pulls out information from Wikipedia. But what if it can’t play that rad pop banger you just heard and responds saying “I’m sorry, I don’t understand the question,”!! Disappointing, right?
All revered digital helpmates including Google’s Google Assistant and Apple’s Siri are capable of producing frustrating coups that can feel like artificial stupidity. Against this, Google has decided to start a new research push to realize and improve the existing relations between humans and AI. PAIR, for People + AI Research initiative was announced this Monday, and it would be shepherded by two data viz crackerjacks, Fernanda Viégas and Martin Wattenberg.
Virtual assistants don’t like to be defeated – they get infuriated when they fail to perform a given task. In this context, Viégas says she is keen to study how people outline expectations regarding what systems can and cannot outperform a command – which is to say how virtual assistants should be designed to prick us toward only asking things that it can perform, leaving no room for disappointment.
Making Artificial Intelligence more transparent among people and not just professionals is going to be a major initiative of PAIR. It also released two open source tools to help data scientists grasp the data they are feeding into the Machine Learning systems. Interesting, isn’t it?
The deep learning programs that have recently gained a lot of appreciation in analyzing our personal data or diagnosing life-threatening diseases is of late said to be dubbed as ‘black boxes’ by polemicist researchers, meaning it can be trickier to observe why a system churn out a specific decision, like a diagnosis. So, here lies the problem. In life and death situations inside clinics, or on-road, while driving autonomous vehicles, these faulty algorithms may pose potent risks. Viégas says “The doctor needs to have some sense of what’s happening and why they got a recommendation or prediction.”
Google’s project comes at a time when the human consequences of AI are being questioned the most. Recently, the Ethics and Governance of Artificial Intelligence Fund in association with the Knight Foundation and LinkedIn cofounder Reid Hoffman declared $7.6 million in grants to civil society organizations to review the changes AI is going to cause in labor markets and criminal justice structures. Similarly, Google announces most of PAIR’s work will take place in the open. MIT and Harvard professors Hal Abelson and Brendan Meade are going to join forces with PAIR to study how AI can improve education and science.
Closing Thoughts – If PAIR can integrate AI seamlessly into prime industries, like healthcare, it would definitely shape roads for new customers to reach Google’s AI-centric cloud business destination. Viégas reveals she will also like to work closely with Google’s product teams, like the ones responsible for developing Google Assistant. According to her, such collaborations are great and comes with an added advantage, as it keeps people hooked to the product, resulting in broader company services. PAIR is a necessary shot to not only help push the society to understand what’s going on between humans and AI but also to boost Google’s bottom line.
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With state-of-the-art technology looming on the horizon, the $150-billion Indian IT industry has a high appetite for workers accomplished in the fields, like AI, Data Science, Big Data, and more.
Soon, it wouldn’t be enough to flash an engineering degree or some minor knowledge in Java or Python – the need for data science and artificial intelligence is on the rise. Automation is going to be the key to change. Globally, 12% of employers have started thinking of downsizing their workforce owing to technological advancement. Amidst all this, don’t think India would be spared. Indian bosses fear automation will reduce their headcount too. But fret not, it’s not all a bad news – there is always a silver lining after rains and that is Big Data jobs.
Shine bright with Big Data
In India, the number of job openings in the Analytics field almost doubled from the last year. Digital natives, like Amazon, Citi, HCL, IBM, and Accenture are waiting to fill close to 50000 positions, according to a study conducted by Analytics India Magazine and Edvancer. All these definitely signify parting off the dark clouds, and I can’t agree more!
Artificial Intelligence and Machine Learning are building a base of its own. Moreover, AI is deemed to be the hottest technical sector in the next 5 years and would beam in success. Along with top-of-the-line tech firms, more than 170 startups have transfixed their gaze on this field. To surf on the next wave of IT jobs, candidates need to step aside from low-in-demand stale skills to excel on budding Analytics skills. Every single HR Manager out there is seeking professionals who can manipulate algorithms and work wonders in various machine-learning models and you can be one of them!
Get better, get evolved
Expertise in languages, like Java/C/C++ gives you a certain edge, but to enter the dominating field of Big Data, techies will be asked to master intricate languages, such as Scala and Hive that are less conventional. Millennial recruiters are also looking out for those who have a keen insight for good design and flawless code architecture. “Programmers who focus on good design principals are always preferred over programmers who can just code,” Rajat Vashishta, founder of Falcon Minds, a resume consulting firm, says. “User experience matters a lot more than it used to, say, five years ago.”
Where skills in technology, like business intelligence, artificial intelligence, machine learning and DevOps are flourishing, minute attention need to be given on proper implementation of these skills, according to Aditya Narayan Mishra, chief executive officer of CIEL HR Services, a recruitment firm, otherwise all of it would be a total waste.
It’s all in the layout
Presentation matters, you agree or not! Make your resume ready to strike the job criteria you are applying for. For example, if a user interface developer wants to become a full stack developer, he must mention back-end programming skills in the profile. This will give an instant boost to the resume. The design of a resume has also changed over the years. Now, the shorter your resume the better response you get. “Most techies write pages and pages of projects in their resumes. While it is important, in most cases, the same information gets repeated. Anything above two pages is a big no,” says Vashishta.
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DexLab Analytics is proud to announce that Tanmoy Ganguli, a proficient Data Analyst who has a long standing experience in Credit Risk Modelling, SAS and regression models is joining our Gurgaon institute as Program Director. Here are some excerpts from an interview we conducted, where he talks about the various challenges he faced in his career and the rapid development of Data Analytics.
Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL
Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.
Machine Learning:You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python. These algorithms show to employers that you have exposure to how data science can be used in more practical manners.
Data Wrangling: You should be able to clean up data. This basically means understanding that “California” and “CA” are the same thing – a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and and correcting/deleting them.
Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)
Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.
What they look for? @ Mu-Sigma, Fractal Analytics
Most of the analytics and data science companies, including third party analytics companies such as Mu-sigma and Fractal hire fresher’s in big numbers (some time in hundreds every year).
You see one of the main reasons why they are able to survive in this industry is the “Cost Arbitrage” benefit between the US and other developed countries vs India.
Generally speaking, they normally pay significantly lower for India talent in India compared to the same talent in the USA. Furthermore, hiring fresh talent from the campuses is one of the key strategies for them to maintain the low cost structure.
If they are visiting your campuses for interview process, you should apply. In case if they are not visiting your campus, drop your resume to them using their corporate email id that you can find on their websites.
Better will be to find someone in your network (such as seniors) who are working for these companies and ask them to refer you. This is normally the most effective approach after the campus placements.
Key Skills that look for are-
Love for numbers and quantitative stuff
Grit to keep on learning
Some programming experience (preferred)
Structured thinking approach
Passion for solving problems
Willingness to learn statistical concepts
Technical Skills
Math (e.g. linear algebra, calculus and probability)
Statistics (e.g. hypothesis testing and summary statistics)
Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
Software engineering skills (e.g. distributed computing, algorithms and data structures)
Data mining
Data cleaning and munging
Data visualization (e.g. ggplot and d3.js) and reporting techniques
Unstructured data techniques
Python / R and/or SAS languages
SQL databases and database querying languages
Python (most common), C/C++ Java, Perl
Big data platforms like Hadoop, Hive & Pig
Business Skills
Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
Industry Knowledge: Understanding the way your chosen industryfunctions and how data are collected, analyzed and utilized.
Interested in a career in Data Analyst?
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