Data Science: Visualizing the data at global level

We have always relied on the powers of oracles in order to find out what happens next. That is because we want to make the right decision and do not want to miss anything as the future is always uncertain. It is soothing to know that we can depend on technologies, knowledge, and insights that allow us to take wise decisions and secure our future. Business relies on these entities to make decisions in order to secure its future and thrive. But not every business is able to make sense out of the enormous data it has. Nokia, for instance, had millions of data points collecting data from its customers and funneling it into its business intelligence. Yet, it was not able to predict the rise of smartphones and remained biased towards its traditional business model. The once unchallengeable company is now struggling to gain grounds over its competitors who took the right decision at the right time.

Making sense out of data is as crucial as collecting it. Why companies like Nokia fail to utilize their data is that the two sides involved in the whole decision making process are polar opposites. On one hand are the business people who know what data they need and can define requirements, but do not possess skills to design a data architecture that gives them the data they need. Technology people, those who provide data, don’t understand the business requirements, but can design the data architecture. Thus when these two sets of experts fail to find common ground, business misses insights that are crucial for business intelligence.

Data Science has been a trending word in the industry for a long time. It is the middle path of the business aspect and the technology aspect of decision making. Data science analyses data to provide actionable insights. At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them by incorporating computer science, data modeling, statistics, analytics, and mathematics. With data points such as mobile apps, web apps, websites, point of sales, IoT increasing geometrically, the role and impact of data science can only grow in the future.

Linkedin, in its initial days, was growing fast but its users were not making connections with people already on the site. The traditional analysis was not helping it. Then one executive employed Data Science in order to create more engagement. The process saw unprecedented increase in use connections. Uber, the unicorn start-up, runs detailed predictive analysis of data to check when the demand for cabs is bound to rise and uses surge pricing. It uses similar data science to promote driver loyalty by providing them incentives. In short, Data Science is becoming a crucial discipline and a reliable system for making business decisions across domains.

One of the biggest misconceptions is that you need a sciences or math Ph. D to become a legitimate data scientist. Data Scientists use many technologies such as Hadoop, Spark, and Python. These technologies do not warrant a Ph. D.

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. In simple words, Hadoop is a framework that allows you to store Big Data in a distributed environment so that you can process it parallel.

Apache Spark is an open-source engine built around speed, ease of use, and sophisticated analytics and developed specifically for handling large-scale data processing and analytics. It allows users to access data in across sources, such as Hadoop Distributed File System (HDFS), Amazon S3 etc.  Internet behemoths such as Netflix, Yahoo, and eBay have deployed Spark massively, collectively processing multiple petabytes of data on clusters of thousands of nodes.

Python or Monty Python is a general purpose programming language which has overtaken R as the primary language of Data analytics, Data Science owing to its capabilities such as easier learning curve, wide reach, bigger user base and support groups, flexibility and better app integration.

Mastering these technologies can open the avenues for an aspiring Data Scientist. There aren’t enough Data scientists to cater to the growing needs of the industry.

Interested in learning Data Science and Machine Learning?

Join the Data Science and Machine Learning Workshop in Dubai and learn how to analyze data to gain insights, develop new strategies, and cultivate actionable business intelligence. Click here for more info


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Internet of Things: The next iteration of connectivity!

Internet was born out of the need to build robust, fault-tolerant communication via computer networks.  For past decades, it has become the underlying fabric of digital communication and data exchange. It has given us the ability to transmit data almost instantly and collaborate with peers, business partners, family members no matter what their geographic location is. But how do we take this enormous powerhouse to even greater levels in order to both augment and ease our actions, decisions or processes? The answer was given by CMU in 1982 when it came out with an experimental Coke machine, the first internet-connected appliance, capable of reporting its inventory to the warehouse and the number of bottles to the users. The technology that was used is what we now know as the Internet of Things (IoT).

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, or objects that are provided with unique identifiers, sensors and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. In simpler terms it means everyday objects talking to each other, sharing information and easing human actions and lessening human interventions.

Imagine a manufacturing facility which houses complex machines! The facility owners would naturally want to reduce maintenance cost, increase asset availability, and ultimately improve customer satisfaction. IoT can enable these machines to monitor their data such as temperature, vibration, or rotation speed and issue an alert long before a breakdown happens. This data combined with ERP and enterprise asset management (EAM) systems can enable the facility to change from reactive to predictive maintenance and service, improving capacity utilization.

At a bigger level, such as city, IoT solutions can be deployed to automate otherwise cumbersome, laborious, time-consuming processes or calamitous events.  IoT enabled environmental sensors which measure wind, seismic activity, water levels and tides can provide crucial insights about eventuality of harmful events. IoT solutions can also alert citizens much before such events take place, monitor traffic and control traffic lights to help accelerate evacuation before a storm by prioritizing the outbound direction of key thoroughfares, or mobilize medical systems to cater to any eventuality. During normal conditions, IoT enabled sensors can accomplish plethora of critical tasks such as balancing power distribution, reducing traffic congestion, enhancing surveillance, or monitoring air quality. IoT holds enormous potential and it has started to be implemented at various scales globally, resulting in better productivity and reduced human efforts.

On the flip side, there is a lot of skepticism surrounding IoT solutions. The primary apprehensions are the security of the data, authentication of the device, vulnerability of devices to viruses etc.  This is analogous to the situation Internet was in 20 years ago when it was in nascent stages of its development. There have been lot of initiatives by governments to create robust security measures for IoT devices and programs. Dubai government has already launched Data Wealth initiative and the Dubai IoT Strategy, which will protect the emirate’s digital wealth and pave way for Dubai to developing a robust and advanced IoT ecosystem.

There are numerous implementations and solutions that can radically transform the way we do business, deliver policies, run cities, provide healthcare or interact with loved ones. In the future, IoT will continue to grow and become more advanced, realizing the true potential of the Internet.


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Big Data Hadoop Workshop

Hadoop is no longer a technology for tech enthusiasts and bleeding-edge Internet startups. Research shows that it’s becoming an integral part of the enterprise data strategy as users are gaining new insights into customers and their business.

Hadoop is driven by several rising needs, including the need to handle exploding data volumes, scale existing IT systems in warehousing, archiving, and content management, and to finally get BI value out of non-structured data. And with analytics as the primary path to extract business value from Big Data, Hadoop adoption is rapidly increasing.

The world of Hadoop and “Big Data” can be intimidating – hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this course, you’ll not only understand what those systems are and how they fit together – but you’ll go hands-on and learn how to use them to solve real business problems!

The Big Data Hadoop Workshop is designed to give you in-depth knowledge of the Big Data framework using Hadoop, including HDFS, YARN, and MapReduce. You will learn to use Pig, and Hive to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.

5 Reasons To Attend The Big Data Workshop

  1. Design distributed systems that manage “big data” using Hadoop and related technologies
  2. Analyze data using HBase (NOSQL), and MapReduce program
  3. Use HDFS and MapReduce for storing and analyzing data at scale
  4. Begin your journey in Data Science using Hadoop and other technologies
  5. Get trained for Cloudera Certification for Developers

Topics Covered

  • Introduction to Hadoop Architecture and HDFS
  • Hadoop 2.0, YARN, MRV2
  • Apache Sqoop
  • Hadoop Mapreduce
  • Apache Hive, HiveQL
  • Apache Pig
  • Hbase and NoSql Databases



Earlybird Offer! $999 instead of $1390 (save $391) (The 3-day pass includes course material/ software/ certificate/ breakfast,lunch,refreshments) *Offer valid only on registrations on or before 6 October, 2017