Data Visualization: Data visualization is the process that defines any effort to assist people to understand the importance of data by placing it in a visual context.  Patterns, trends, and correlations that might be missed in text-based data can be represented and identified with data visualization software. It is a graphical representation of numerical data. This is one of the Hot skills in the market, you will get the highest salary. Types of data visualization Visual Reporting Visual reporting uses charts and graphics to represent business performance, usually defined by metrics and time-series information. The best dashboards and scorecards enable the users to drill down one or more levels to view more detailed information about a metric A dashboard is a visual exception report that signifies the ambiguities in performances using visualization techniques Visual Analysis Visual-analysis allows users to visually explore the data to observe the data and discove

Watson IoT is a set of capabilities that learn from, and infuse intelligence into, the physical world. The Internet of Things-generated data is growing twice as fast as social and computer-generated data, and it is extremely varied, noisy, time-sensitive and often confidential. You can learn quickly IBM watson for IoT quickly. Complexity grows as billions of devices interact in a moving world. This presents a growing challenge that will test the limits of programmable computing.  What is Cognitive IoT Cognitive IoT is not explicitly programmed. It learns from experiences with the environment and interactions with people.  It brings true machine learning to systems and processes so they can understand your goals, then integrate and analyze the relevant data to help you achieve them. References IBM Watson IoT videos 5 Challenges in internet of things Follow us on social media Facebook Twitter

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs. An execution graph describes the possible states of execution and the states between them. Spark also supports a set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. #Spark   Review of Spark Machine Language Library (MLlib): MLlib is Spark's machine learning library, focusing on learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives. Why MLlib? It is built on Apache Spark, which is a fast and general engine for large scale processing. Supposedly, running times or up to 100x faster than Hadoop MapReduce, or 10x faster on disk. Supports writing applications

Challenges in the internet of things you need to know. I have covered in this post all the top challenges involved. The challenges are Security Privacy Standards Legal Development Issues Security While security considerations are not new in the context of information technology, the attributes of many IoT implementations present new and unique security challenges. Addressing these challenges and ensuring security in IoT products and services must be a fundamental priority . Users need to trust that IoT devices and related data services are secure from vulnerabilities, especially as this technology becomes more pervasive and integrated into our daily lives. Poorly secured IoT devices and services can serve as potential entry points for cyber-attack and expose user data to theft by leaving data streams inadequately protected.  The interconnected nature of IoT devices means that every poorly secured device that is connected online potentially affects the security an

Data science is a combination of technical and general skills. As an analyst, you are responsible to provide useful information to the client. Below is a useful list of skills. Top Data Scientist Skills. 1. Paradigms and practices. This involves data scientists acquiring a grounding in core concepts of data science, analytics, and data management.  Data scientists should easily grasp the data science life cycle, know their typical roles and responsibilities in every phase, and be able to work in teams and with business domain experts and stakeholders.  Also, they should learn a standard approach for establishing, managing, and operationalizing data science projects in the business. 2. Algorithms and modeling. Here are the areas with which data scientists must become familiar: linear algebra,  basic statistics,  linear and logistic regression,  data mining,  predictive modeling,  cluster analysis,  association rules,  market-basket analysis,  decision tr

Agile Vs Scrum Agile is a time boxed, iterative approach to software delivery that builds software incrementally from the start of the project, instead of trying to deliver it all at once near the end. It works by breaking projects down into little bits of user functionality called  user stories , prioritizing them, and then continuously delivering them in short two week cycles called  iterations . Agile scales like any other software delivery process. Not that well . Look - scaling is hard. There is no easy way to magically coordinate, communicate, and keep large groups of people all moving in the same direction towards the same cause. It's hard work. The one thing Agile does bring to the conversation, is instead of looking for ways to scale up your project, look for ways to scale things down.

The QlikView is a Windows-based desktop tool that is used by business analysts and developers to create a data model and to layout the graphical user interface (GUI or presentation layer) for QlikView apps. It is within this environment where a developer will use a SQL-like scripting environment augmented by ‘wizards’) to create the linkages (connection strings) to the source data and to transform the data e.g. rename fields, apply expressions) so that it can be analyzed and used within the UI, as well as re-used by other QlikView files. Related: QlikView+Tableau+Jobs (Search and know skills needed) The QlikView Desktop is also the environment where all user interface design and user experience is developed in a drag-and-drop paradigm: everything from graphs and tables containing slices of data to multi-tab architectures to application of color scheme templates and company logos is done here. Qlik View Server (QVS) The QVS is a server-side product that contains the in-memory