Smart Cities - India Perspective

Smart Cities – Indian Perspective                   

India is a country of rich physical and human resources. It has flourishing green fields and green farms, high rising mountains which hover over our cities, long elegant rivers which keep the nations watered and plains, never-ending natural resources etc. In short, it can be said that that a mini world residing in India.

The rise in population, unconventional use or wastage of natural resources and rising corruption has led in many of our countrymen losing trust in the nation’s true prospective. Our countrymen’s vision of an urbanized India is still limited to the hopes of 24 hour running water, continuous power supply and good housing. After nearly 7 decades of an autonomous government rule, the dreams of Indian should have tangled bullet trains, rising economy and a sophisticated technology. But these things still remain a daydream for a common Indian.

Why we need a Smart City:

The attraction of more job opportunities and quality of  service made many of the india people slowly migrating  from Rural areas to cities. In days to come the migration to cities would  be on a vast scale for better living  and opportunities .

As per the study imparted by McKinsey Global Institute, by the year 2030, 70% of jobs and service opportunities will be in the cities. The study also finds that Indian cities will fabricate 70% of the nation’s GDP and will raise the countries per capita income fourfold.

Improved urban population will mean more power spending and complexness in city management. It means that the government will face tough job in dealing with everything from bylaw and order, health and security; to power, waste and transportation management.

All these things make it clear that India should gear up itself to administer this rapid rise in urban population creatively and make certain that the affect of this trend is utilized for the nation’s richness and growth.

Factors necessary for Smart Cities:

The idea of smart cities can be more easily interpreted by some cautionary smart cities around the world. Copenhagen (Denmark), Amsterdam (Netherland), Vienna (Austria), Barcelona (Spain), Paris (France), London (England), Berlin (Germany) etc

•             Improving or protecting the environment is one of the main aims of a smart city. Say for example Copenhagen has one of the smallest carbon footprints /capita in the world (less than two tons / capita).

•             Traffic reduction and managing is also a fundamental element in a smart city. In Amsterdam, 67% of all trips are done by cycling or walking.

•             Paris is also famous for their grand and broadly used bikesharing network which has led to a 5% reduction in vehicle congestion.

•             Use of solar energy for the 100 % power generation is also an important factor for a smart city.

Role of Technology in Developing Smart City:

The idea of a smart city is a moderately new one. Cities in the urbanized world are developing technology master plans and then using these plans to develop a citywide authority and control network that supervises and optimizes the delivery of services like power, water, traffic and healthcare. The fundamental principle of a smart city is making infrastructure network and release of services more capable across, logistics, water supply, telecommunication and gas supply.

Indian cities, in a small way, are using sophisticated technology within sections to solve problems. These include traffic control, by means of sensors to monitor water leaks, chasing garbage trucks through GPS to guarantee they put their waste at chosen landfills, energy management in smart buildings and complexes. Also under progress are smart townships that are prohibited centrally, and entire cities along the Delhi-Mumbai Industrial Corridor.

Typically in a smart city, sensors will allow real-time inputs to a control centre on fresh water, energy, civic transport, communal safety, edification, and healthcare. Intelligent communication tools will let executives manage and react to emergencies quickly as well as provide residents with steady real-time inputs.

Role of IoT (Internet of Things )Technology in Developing Smart City:

The Indian approach for Smart Cities:

The cities with constant or projected 100 smart cities include surat, delhi, vizag  etc. Many of these cities will comprise special investing areas or special economic zones with customized policy and tax structures to make it eye-catching for foreign investment.

With numerous reviewed laws and rules for the real estate sector in India, the above strategy of the administration will also prove to be a huge advantage for the real estate developers as well as the builders. Because, the construction of smart cities will need the capability of builders and more significantly, the prudence of real estate developers.

There are many ways to make housing, commercial and public spaces sustainable by ways of applied science, but an elevated proportion of the total energy consumption is still in the hands of end users and their doings. For instance, the success of such a city depends on inhabitants, entrepreneurs, visitors and their participation in energy saving and accomplishment of new technologies.

However, it should also be recalled that every smart city has two more main facilitators apart from the main enabler which is technology. The other two significant enablers are: the inhabitants of the city and the management. Even with all the technology a smart city gets, it’s the people and the management that are at the centre of the smart city.

So, a smart city is built by these three facilitators on the following six columns: Smart governance, Smart populace, Smart mobility and move, Smart livelihood and housing, Smart environment and smart economy. If we want smart cities, we should make sure that all the six pillars are significant enough to assume the weight of the stargazed smart city.

City leaders all over the world have bosomed the smart city perception with ebullience. They are acclaiming ground-breaking projects and putting out a vision for how cities can use technology to meet sustainability goals, enhance local economies, and ameliorate services. This promise to changing how cities function is driving the constant interest in smart cities. Moreover, the smart city model is evolving as more cities set out their own schedule and a growing range of suppliers deliver solutions to meet their rising needs.

How to become Data Scientist

past year, interest in data science has soared.Nate Silver is a household name, companies everywhere are searching for unicorns, and professionals in many different disciplines have begun eyeing the well-salaried profession as a possible career move.

In our recruiting searches here at Burtch Works, we’ve spoken to many analytics professionals who are considering adapting their skills to the growing field of data science, and have questions about how to do so. From my perspective as a recruiter, I wanted to put together a list of technical and non-technical skills that are critical to success in data science, and at the top of hiring managers’ lists.

Every company will value skills and tools a bit differently, and this is by no means an exhaustive list, but if you have experience in these areas you will be making a strong case for yourself as a data science candidate.

Technical Skills: Analytics

1. Education – Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).

2. SAS and/or R – In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred.

Technical Skills: Computer Science

3. Python Coding – Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++.

4. Hadoop Platform – Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such asAmazon S3 can also be beneficial.

5. SQL Database/Coding – Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.

6. Unstructured data – It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds or audio.

Non-Technical Skills

7. Intellectual curiosity – No doubt you’ve seen this phrase everywhere lately, especially as it relates to data scientists. Frank Lo describes what it means, and talks about other necessary “soft skills” in his guest blog posted a few months ago.

8. Business acumen – To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.

9. Communication skills – Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately. Check out our recent flash survey for more information on communication skills for quantitative professionals.

The next question I always get is, “What can I do to develop these skills?” There are many resources around the web, but I don’t want to give anyone the mistaken impression that the path to data science is as simple as taking a few MOOCs. Unless you already have a strong quantitative background, the road to becoming a data scientist will be challenging – but not impossible.

However, if it’s something you’re sincerely interested in, and have a passion for data and lifelong learning, don’t let your background discourage you from pursuing data science as a career. Here are a few of the resources we’ve found to be helpful:

Resources

1. Advanced Degree – More Data Science programs are popping up to serve the current demand, but there are also many Mathematics, Statistics, and Computer Science programs.
2. MOOCs –Coursera, Udacity, and codeacademy are good places to start.
3. Certifications – KDnuggets has compiled an extensive list.
4. Bootcamps – For more information about how this approach compares to degree programs or MOOCs, 
5. Kaggle – Kaggle hosts data science competitions where you can practice, hone your skills with messy, real world data, and tackle actual business problems. Employers take Kaggle rankings seriously, as they can be seen as relevant, hands-on project work.
6. LinkedIn Groups – Join relevant groups to interact with other members of the data science community.
7. Data Science Central and KDnuggets – Data Science Central and KDnuggets are good resources for staying at the forefront of industry trends in data science.
8. The Burtch Works Study: Salaries of Data Scientists – If you’re looking for more information about the salaries and demographics of current data scientists be sure to download our data scientist salary study.

Smart Cities in India

Smart City  offers economic activities and employment opportunities to a wide section of its residents, regardless of their level of education, skills or income levels


By 2050, the world will witness a mass exodus of people into cities. 2 out of every 3 people will be living in urban areas which translates into 6.3 Billion urban dwellers. . By 2050, Asia and Africa will account for 86% the world’s urban population. India alone will add more than 400 Million people to its cities – that is twice the population of Brazil today. The consequence of this migration - especially in a fast growing economy like India - is a significant increase in the demand and consumption of resources. The coal reserves in the existing coal mines in India are likely to get exhausted in little more than 50 years at the current rate of consumption. India’s oil imports account for the biggest share in the Current Account Deficit. Cities contribute to 70% of India’s GDP. The growth of cities therefore is inevitable.

However, unplanned growth and distribution of resources could prove to be catastrophic to the economy and impede progress. Building sustainable and Smart cities from scratch and retrofitting sustainability features in already existing cities is the only way out.
Smart Cities are the only perceivable solution to urbanization of this scale.

A Smart city includes a structure that is resource efficient and has a minimal impact on the environment. Among other things, a smart city reduces the energy and water requirement by employing technology and smart construction & design techniques, reduces the generation of solid waste and uses renewable sources to meet energy requirements.

Additionally, to promote a more convenient way of life, a smart city incorporates a sophisticated Information and Communication Infrastructure. A robust transport network to move people is also established.
While retrofitting smart city-like features into an already existing city is a possible solution, it comes with its limitations. Retrofitting is more expensive and inconvenient primarily because of high replacement costs and limitations of the existing structures. The market for retrofitting is still in its nascent stages and therefore not fully understood. While areas like lighting, air conditioning, etc. have seen some technological innovation, other areas like disaster resistance are untouched. Permits and legal requirements are additional challenges.

Although building smart cities from ground up is a more feasible solution, it is a daunting task. The smart city concept has been experimented with several times in the past in different parts of the world. However, some have fallen victim to failure because of various reasons. Many unsuccessful attempts in the past were characterized by ambitious sizes of these cities, bold investments, technological misfits, poor urban planning, etc.


The Indian smart city landscaped must be engineered to encourage developing smaller and more realizable cities with technologies adapted to the Indian ecosystem and innovative financing. Building a strong support infrastructure, recreation options and promoting thriving businesses to flourish will make the city more habitable and desirable. Formation of communities must be allowed to follow a natural path.
Building smart cities is investment heavy and time consuming. However, India has to embrace sustainable methods soon to avoid eventual chaotic circumstances. The success of smart cities cannot be attributed to technologies alone. People must be educated about the importance and necessity of sustainable practices and the advantages of investing in sustainable settlements.

SMART CITY , GIS and FIVE PILLARS

What is smart  city

People migrate to cities primarily in search of employment and economic activities

beside better quality of life. Therefore, a Smart City for its sustainability needs to offer

economic activities and employment opportunities to a wide section of its residents,

regardless of their level of education, skills or income levels. In doing so, a Smart City

needs to identify its comparative or unique advantage and core competence in

specific areas of economic activities and promote such activities aggressively, by

developing the required institutional,physical, social and economic

infrastructures for it and attracting investors and professionals to take up

such activities. It also needs to support the required skill development for such

activities in a big way. This would help a Smart City in developing the required

environment for creation of economic activities and employment opportunities.

Smart City  GIS and Five pllars

GIS  five “pillars” namely Power, Water, Transport, Solid Waste Management and Safeguarding (Public Safety) and identifies enablers to better utilize Information and Communications Technologies (ICT). Governance, Planning, Infrastructure & networks, Data analytics, Geographic Information Systems (GIS) and Cyber Security have been identified as enablers. In reality GIS is similar to any other IT enterprise component, but for some reason GIS has been identified as a separate enabler. Ideally a “secure” GIS based IT enterprise should have been considered which - offers capability for “analytics”, can be used for “planning” and thus support in effective and efficient “governance”.

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Geographic Information Systems

The report refers to GIS as a “…system that involves superimposition of several layers of geo-data and information systems in a specific sequence to create a comprehensive geospatial / geographic information system”. Technically this statement still holds good, but gone are the days when GIS was used for viewing thematic maps and little bit of spatial analysis. Today GIS systems offers much more than that. A GIS can be integrated with – non-spatial data, multiple databases, multiple systems, real-time sensors and devices and so on and can be made available on cloud, web, mobile or desktop environments. I would prefer calling it “A system / solution that can capture, store, manipulate, analyze, manage, and present all types of data in a geographical context”.

Some observations specific to the way forward :

Power – Smart Grid does not find any reference to GIS. GIS is critical component of a smart grid facilitating effective and efficient - network management, asset management, consumer Information management, workforce management and outage management. Integration of call centres, billing, payments and other sensors from SCADA with GIS in a real-time scenario can offer actionable intelligence for plugging pilferage's, outage management and restoration and so on.

Solid Waste - Long term proposal recommends applying of GIS and GPS solutions to enable route optimization and process improvement. With GIS deployment and GPS enablement proposed as a medium term plan, ideally route optimization and process improvement can be accomplished at this stage itself. Most of the GIS softwares come with route planning and optimization tools now a days.

Water– While it addresses GIS integration, the report misses on pipeline distribution management, asset management, water quality monitoring and management etc. which are key components of water systems. With percentage of Non-revenue water (NRW) high in the Indian context, GIS can offer actionable intelligence to bring down the NRW.

Traffic – Smart traffic management could be accomplished on a GIS based platform. The smart surveillance can be integrated with such system and graduated to Intelligent Traffic Management System. In addition such system can also be used for planning, monitoring and maintenance of transport networks, asset management etc. which are critical components of traffic management.

Safeguarding (Public Safety) – City Surveillance, Command Control and CAD are addressed as separate entities. Ideally this should be an integrated system. It describes “CAD vehicles”, “GIS & GPS enabled vehicles” - in reality CAD is a software solution / system. On command control, a GIS based CAD system can be scaled up by integrating video feeds and multiple sensor data to offer enhanced locational awareness of the incident location. This can further be graduated to City Surveillance systems.

Data Scientist in 8 easy steps

Data Scientist in 8 easy steps

Six essential skills for Big data

Analytical Skills

Analytics involves the ability to determine which data is relevant to the question that you are hoping to answer, and interpreting the data in order to derive those answers.

If you have a knack for spotting patterns, and establishing links between cause and effect, then these skills will prove invaluable if you’re tasked with turning a business’s data into actionable plans of operation.

Creativity

There are no hard and fast rules about what a company should use big data for. It is an emerging science, which means the ability to come up with new methods of gathering, interpreting, analysing and – finally – profiting from – a data strategy, is a very valuable skill.

The corporate data superstars of the future will be people who can come up with new methods of applying data analytics in innovative ways. Often they will be solving problems that companies don’t even know they have – as their insights highlight bottlenecks or inefficiencies in the production, marketing or delivery processes. In particular, creativity is important for anyone hoping to make sense of unstructured data – data which does not fit comfortably into tables and charts, such as human speech and writing.

Mathematics and Statistics

Good old fashioned number crunching. Despite the growing amount of unstructured data being incorporated into data strategies, much of the information being gathered and stored, ready for analysis, still takes the form of numbers.

And even when dealing exclusively with unstructured data, the objective of the exercise is often to reduce elements of the data – emails, social media messages etc – to figures which can be quantified, in order for definite conclusions to be drawn from them. This means candidates with a strong background in maths or statistics are ideally placed to make the leap into big data enterprise.

Computer Science

Computers are the workhorses behind every big data strategy, and programmers will always be needed to come up with the algorithms that process data into insights. This is a very broad category which covers a whole range of subfields, such as machine learning, databases or cloud computing, which will be great additions to any budding data scientist’s arsenal. In particular you should be familiar with the range of open-source technologies – Hadoop, Python, Pig etc. – which make up the foundations of most big data enterprises.

Business skills

An understanding of business objectives, and the underlying processes which drive profit and business growth are also essential. The idea that a company will hire an “egg head” data scientist who will be locked away in a basement lab, to work their magic on data fed to them through a slot in their door, is dangerous and wrong. They should have a firm grasp of the company’s business goals and objectives as well as an understanding of the indicators which let them know if they are heading in the right direction.

Communication ability

Both inter-personal and written – an essential part of a data scientist skillset is the ability to communicate the results of the analysis to other members of their team as well as to the key decision-makers who need to be able to quickly understand the key messages and insights.

This also includes the skills of visualising and reporting data in the most effective manner. You can have the best analytical skills in the world, but unless you are able to make your findings understandable to everyone else you work with, and demonstrate how they will help to improve performance and drive success, they will be of little use to any business.

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Smart Grid Analytics

World Market for $1 trillion is being invested this decade in upgrading the power infrastructure globally to make the devices in the power grid remotely machine addressable. These devices include meters, thermostats, home appliances and HVAC equipment, factory equipment and machinery, and transformers, substations, distribution feeders, and power generation and control componentry.

Till Now 310 million smart meters have been installed globally. That number will more than triple by 2022, reaching nearly 1.1 billion according to Navigant Research. While representing only a fraction of the sensors on the grid infrastructure, the smart meter installation numbers provide a good indication of the penetration and rate of growth of the smart grid. These developments are occurring worldwide.

Collectively, these devices generate massive amounts of information. With recent developments in information technology, including elastic cloud computing and the sciences of big data, machine learning, and emerging social human-computer interaction models, we are able to realize the economic, social, and environmental value of the smart grid by aggregating the sum of these data to correlate and scientifically analyze all of the information generated by the smart grid infrastructure in real time.

By holistically correlating and analyzing all of the dynamics and interactions associated with the end-to-end power infrastructure—including current and predicted demand, consumption, electrical vehicle load, distributed generation capacity, technical and non-technical losses, weather, and generation capacity— across the entire value chain, we can realize dramatic advances in energy efficiency.

Smart grid analytics enables us to provide real-time pricing signals to energy consumers, manage sophisticated energy efficiency and demand response programs, conserve energy use, reduce the fuel necessary to power the grid, reconfigure the power network around points of failure, recover instantly from power interruptions, accurately predict load and distributed generation capacity, rapidly recover from damage inflicted by weather events and system failures, and reduce adverse environmental impact.

The advent of smart grid analytics represents a major advance in the development of energy efficiency technology. Many leading utilities including Enel, GDF Suez, Exelon and PG&E work with us to drive innovation by applying the science of smart grid analytics to the benefit of their communities, consumers, and stakeholders.