Data Analytics

Big Data & Front End Development track in the Microsoft Professional Program

June 8, 2017 Analytics, Azure, Azure Data Factory, Azure Data Lake, Big Data, Big Data Analytics, Big Data Management, Data Analytics, Data Services, Emerging Technologies, Hadoop, HD Insight, IaaS, PaaS, Predictive Analytics, Realtime Analytics, SQL Azure, Stream Analytics, Windowz Azure No comments

Earlier I introduced you the Microsoft Professional Program for Data Science. Right after few days Microsoft announced the BETA availability of two more tracks Big Data and Front End Development.

Big Data Track:

This Microsoft program will help you to learn necessary skills from cloud storage and databases to Hadoop, Spark, and managed data services in Azure. Curriculum of this program involves learning how to build big data solutions for batch and real-time stream processing using Azure managed services and open source systems like Hadoop and Spark.

Are you intend to pursue a Data Analytics career, this is the right program for you to gain necessary insights.

Technology you will apply to gain these skills are: Azure Data Lake, Hadoop, HDInsight, Spark, Azure data factory, Azure Stream Analytics

Below is the course outline :

10 COURSES | 12-30 HOURS PER COURSE | 8 SKILLS
ENROLL NOW here
More details here

Front End Development Track:

This track provides you necessary skills to get started with Advanced Front End development using HTML5, CSS3, JavaScript, AngularJS and Bootstrap. At the end of the curriculum you will become master in Front End Development with all predominant modern web technologies.

So if you are a front end UI developer, this is something you can try out to enhance your skills.

Below is the course outline :

13 COURSES | 15-30 HOURS PER COURSE | 11 SKILLS
ENROLL NOW here
More details here

Track detail

Each course runs for three months and starts at the beginning of a quarter. January—March, April—June, July—September, and October —December. The capstone runs for four weeks at the beginning of each quarter: January, April, July, October. For exact dates for the current course run, please refer to the course detail page on edX.org.

[Microsoft]

Microsoft Professional Program for Data Science

June 3, 2017 Analytics, Azure, Big Data, Big Data Analytics, Certification, Data Analytics, Data Science, Data Scientist, Emerging Technologies, Internet of Things, IoT, KnowledgeBase, Machine Learning(ML), Microsoft No comments

Microsoft has come up with a new program to bring in more skilled people to the field of Data Science by providing them the right training on right set of tools.

Microsoft has put together a curriculum to teach key functional and technical skills, combining highly rated online courses with hands-on labs, concluding in a final capstone project. All these trainings will be delivered by Microsoft either online or through recorded sessions.

The program comprises of 10 COURSES, 16-32 HOURS PER COURSE, 8 SKILLS

The technology skills you will gain through are: T-SQL, Microsoft Excel, PowerBI, Python, R, Azure Machine Learning, HDInsight, Spark.

ENROLL NOW: through this link

Course schedule:
For exact dates for the course, please refer to the course detail page on edX.org.

For more details on this program: https://academy.microsoft.com/en-us/professional-program/data-science/

** This course would provide necessary insight to write Microsoft’s new Certification – Microsoft Certified Solution Associate(MCSA) – Machine Learning.

Happy Learning!!

Introduction to Data Science

June 3, 2017 Analytics, Big Data, Big Data Analytics, Big Data Management, Cloud Computing, Cold Path Analytics, Data Analytics, Data Collection, Data Hubs, Data Science, Data Scientist, Edge Analytics, Emerging Technologies, Hot Path Analytics, Human Computer Interation, Hype vs. reality, Industrial Automation, Internet of Nano Things, Internet of Things, IoT, IoT Devices, Keyword Analysis, KnowledgeBase, Machine Learning(ML), machine-to-machine (M2M), Machines, Predictive Analytics, Predictive Maintenance, Realtime Analytics, Robotics, Sentiment Analytics, Stream Analytics No comments

We all have been hearing the term Data Science and Data Scientist occupation become more popular these days. I thought of sharing some light into this specific area of science, that may seem interesting for rightly skilled readers of my blog.

Data Science is one of the hottest topics on the Computer and Internet nowadays. People/Corporations have gathered data from applications and systems/devices until today and now is the time to analyze them. The world wide adoption of Internet of Things has also added more scope analyzing and operating on the huge data being accumulated from these devices near real-time.

As per the standard Wikipedia definition goes “Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.”.

Data Science requires the following skillset:

Hacking Skills
Mathematics and Statistical Knowledge
Substantive Scientific Expertise

[Image Source: From this article by Berkeley Science Review.]

Data Science Process:

Data Science process involves collecting row data, processing data, cleaning data, data analysis using models/algorithms and visualizes them for presentational approaches. This process is explained through a visual diagram from Wikipedia.

[Data science process flowchart, source wikipedia]

Who are Data Scientist?

Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings.

They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to produce and present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.

Importance of Data Science and Data Scientist:

“This hot new field promises to revolutionize industries from business to government, health care to academia.”

— The New York Times

Data Scientist is the sexiest job in the 21st century as per Harward Business Review.

McKinsey & Company projecting a global excess demand of 1.5 million new data scientists.

What are the skills required for a Data Scientist, let me share you a visualization through a Brain dump.

I thought of sharing an image to take you through the essential skill requirements for a Modern Data Scientist.

So what are you waiting for?, if you are rightly skilled get yourselves an Data Science Course.

Informational Sources:

Azure in Germany–a complete EU cloud computing solution

May 18, 2017 .NET, Analytics, AppFabric, Azure, Azure in Germany, Azure IoT Suite, Cloud Computing, Cloud Services, Cloud Strategy, Cognitive Services, Computing, Data Analytics, Data Governance, Data Hubs, Data Warehouse, Emerging Technologies, Event Hubs, IaaS, Intelligent Edge, Internet of Things, IoT, IoT Central, IoT Hub, Machine Learning(ML), Media Services, Media Services & CDN, Messaging, Microsoft, Mobile Services, PaaS, SaaS, SQL Azure, Storage, Backup & Recovery, Stream Analytics, Virtual Machines, Windowz Azure No comments

With my earlier article Azure in China, it came in to my interest to look for any other country/region specific independent cloud data center requirements. I came across Azure for US Govt(Similar to Amazon Govt Cloud) instance and Azure Germany data center. For this article context I will be covering only Azure in Germany.

What is Azure Germany?

Just like regional regulatory requirements in China, Germany also wanted a completely locally owned/managed Azure Data Center for EU/EFTA/UK requirements. This is also to ensure stricter access control and data access policy measurements. This approach is to enable organizations doing business in EU/EFTA and UK can better harness the power of cloud computing.

All customer data and related applications and hardware reside in Germany
Geo-replication between datacenters in Germany to support business continuity
Highly secured datacenters provide 24×7 monitoring
It meets all Public sector or restricted industry requirements
Follows all Compliance requirements for EU/EFTA and UK.
Lower cost, locally accessible within your business locations in Germany/EU.

“ Azure Germany is an isolated Azure instance in Germany, independent from other public clouds.”

Who controls it?

An independent data trustee controls access to all customer data in the Azure Germany datacenters. T-Systems International GmbH, a subsidiary of Deutsche Telekom and an experienced, well-respected IT provider incorporated in Germany, serves as trustee, protecting disclosure of data to third parties except as the customer directs or as required by German law.

** Even Microsoft does not have access to customer data or the datacenters without approval from and supervision by the German data trustee.

What Compliance?

Azure Germany has an ongoing commitment to maintaining the strictest data protection measures, so organizations can store and manage customer data in compliance with applicable German laws and regulations, as well as key international standards. Additional compliance standards and controls that address the unique role of the German data trustee will be audited over time. Refer to: Microsoft Trust Center compliance.

[Source : Microsoft Azure]

Useful Links:

Introducing Azure IoT Edge

May 13, 2017 .NET, Analytics, Artificial Intelligence(AI), Augmented Reality, Azure, Azure IoT Suite, Cloud Computing, Data Analytics, Edge Analytics, Embedded, Emerging Technologies, Event Hubs, Industrial Automation, Intelligent Cloud, Intelligent Edge, IoT, IoT Edge, IoT Hub, Linux, Mac OSX, Machine Learning(ML), Microsoft, Robotics, Self Driven Cars, Stream Analytics, Windows, Windowz Azure No comments

During Build! 2017 Microsoft has announced the availability of Azure IoT Edge, which would bring in some of the cloud capabilities to edge devices/networks within your Enterprise. This would enable industrial devices to utilize the capabilities of IoT in Azure within their constrained resources .

With this Microsoft now makes it easier for developers to move some of their computing needs to these devices. Edge devices are mostly having small foot print based to high end machines within your company network.

The essential capabilities to be supported by Azure IoT edge include:

Perform Edge Analytics (a cut down version of Azure Stream Analytics)- Instead of doing analytics in cloud developer/implementer can move the basic cloud data processing and analytical capabilities to Edge Device. Run your machine learning algorithms in Edge device and take predictive analytics steps.
Perform Artificial Intelligence processing at edge device itself. Availability of Microsoft Cognitive Service on edge device would bring in whole lot of automation capabilities. Imagine Alexa/Siri working without internet connection, it should be able to provide you reminders etc.
Perform RealTime Decision making locally based on predefined rules.
Reduce bandwidth costs
Connect to other Edge devices and legacy devices within the constrained/corporate network.
Deploy IoT solutions to Edge Device from Cloud and provide updates as needed.
Operate offline without the need of real-time internet connectivity or intermittent connectivity. Doesn’t have to rely on Cloud to provide commands for processing, can do offline data capture and processing of information from other devices connected and take decisions without the need to rely on a connected cloud service.

Azure IoT Edge enables seamless deployment of cloud services such as:

Azure Machine Learning
Azure Stream Analytics
Azure Functions
Artificial Intelligence, including Cognitive Services
Azure IoT Hub communication and device management features

Along with sharing the image represents Azure’s Enterprise Digital Vision, we will discuss about the same in later sessions:

Getting Started & More information:

Azure CosmosDB – a multi-model, multi-API for highly scalable applications

May 12, 2017 Analytics, Azure, Cloud Computing, CosmosDB, Data Analytics, Data Services, Document DB, Emerging Technologies, Microsoft, PaaS, Storage, Backup & Recovery No comments

Azure Cosmos DB is the next generation globally distributed multi model database from Microsoft. Cosmos DB has been built from scratch in mind with efficient global distribution and horizontal scalability aspects as the core.

Azure Cosmos DB guarantees single-digit-millisecond latencies at the 99% availability across the world with Enterprise Level SLAs.
Offers a set of well-defined consistency models to enable high availability.
Multi-Model – offers ability to support key-value, graph and document data in one service
Multi-API – Azure Cosmos DB automatically indexes all data, and allows you to use your favorite API including SQL, JavaScript, Gremlin, MongoDB and Azure Table Storage to access your data.
Elastically scalable and higher throughput.

Data Model	API’s
Key-value Document Column Family Graph	Document DB Mongo DB Table Storage Gremlin Graph Spark

** Azure Cosmos DB is a superset of the existing DocumentDB service, and Microsoft will be transitioning all existing DocumentDB customers to Azure Cosmos DB, free of charge without data loss.

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