Sonia Sharma, CEO, GoodWorkLabs was honoured with the esteemed ‘Suraksha Chakra’ award from Bangalore City Police on the occasion of International Women’s Day! The star-studded event was hosted to recognise 13 women from Bangalore city, who made an impact on society and broke all the gender stereotypes. Sonia was awarded for her contributions to entrepreneurship, job creation and women empowerment in Karnataka!

 

On the occasion, Sonia Sharma, said: “I am happy to receive this prestigious award from Bangalore City Police. It is a great honour to know that my work has encouraged budding entrepreneurs to excel and contributed towards the betterment of the workforce at large. I am thankful for the consistent effort and hard work of my dedicated, committed, and creative teams. I couldn’t have been where I am now without them. Kudos to the GoodWorkLabs and GoodWorks teams!

“Observing the exponential growth of my two ventures, I aim to expand and create more job opportunities for the young innovators out there. Looking forward to an excellent journey ahead!” she exclaims.

The concept of data visualization plays a very essential role when it comes to assisting the users in question to comprehend the critical ideas quite easily. For instance, identifying the data patterns and trends quickly, and getting the maximum output from the presented data as well.

As a matter of fact, with the increase of data escalation in terms of generating data, finding a way to extract, processing and visualize those data to enhance data interpretation is growing rapidly. In addition to that, if interactive capabilities and data visualization are combined together then it will be easy for the users to dive into the finest details of graphs, charts, maps, dashboard, etc, and helps them to preserve the important data analyses and insights.

On the contrary, with the help of JavaScript programming, the developers can come up with interactive and attractive charts quite easily, directly from the chart libraries. No matter what, be it the requirement for an open source library, a paid-for, or any other types. You can surely find the required one to uplift your visualization skills. There are a number of charting libraries, that includes, Google chart, AnyChart, D3, and Highcharts are the one that worth mentioning. The creating process for all of these is in most of the cases quite similar. Hence, mastering one of then give you the privilege to use the other libraries. Certainly, when you want to add any specific characteristics for any of them.

For the purpose of JavaScript tutorial, let us take the examples from the AnyChart library. This is easy to use and quite flexible as well. It has exhaustive documentation, a wide range of supported chart types verity, and also a code playground, that allows you to test the codes, etc.

Therefore, let’s get into the process of how AnyChart helps in meeting the need for data visualization.

Here we would be addressing the three easy steps that can be used to create a basic bar chart in JavaScript. You can later integrate the same into your website or even in application. These are:
– Data Preparation
– Go to the JavaScript charts library
– Create or enter codes

Now, let’s talk about each one of them in detail.

Data Preparation

In case the data type is unstructured, then you need to prepare the same for the purpose of easy loading into the chart library. According to the format of the library you choose, you need to process the data in the format that the library accepts. However, AnyCharts supports a verity of formats for data including the other ones mentioned above.

For almost all of the charts you create, all you need to do, just put the values for both X and Y axes. Again, for the purpose of making Bar Charts, you will be required to put the values for Y-axis followed by an index number or any item number. This number will be considered as the value for the X-axis.

For instance, let us consider the data in array format, then X will be the item number and data values will be Y axis.

Again, when the data is on JSON format, it will look like this.

Connect the Chart Library

In order to create a connection to the preferred JavaScript chart library, you need to download the relevant package and install the same locally. You can also use the CDN service. In most of the cases, CDN service is the most preferred one. As it will give you the privilege to load data from server Library’s files to the users directly. That in turn as well increase the page load time and enhance the experiences.

Furthermore, AnyChart has a module-based system which allows you to connect to the specific chart type and the features that are essential for your project. It will also help you shrink the code sizes that are running on the application.

Such as, when creating a bar chart using the AnyChart JavaScript, you will be required to add below-mentioned core and basic Cartesian modules.

Writing the Codes

To create a JavaScript Bar Chart, you need to follow the steps mentioned below

To start with let us start with creating a container on the HTML page, it will refer the bar chart.
Then put all the previously processed data on the above-mentioned step.
Let’s create the type of charts that we are going to create from the applicable chart constructor function.
Create a title for the chart and axes
Now at this point create a bar series and put the data
Point the chart to the container the created earlier and start outlining the bar chart
Hence, you can easily understand that creating a bar chart using AnyChart is pretty much straight forward and simple.

Now let’s concentrate on the factors to enhance the looks and feel of the chart, by using some advanced level steps.

Create Advanced JavaScript Bar Charts

From the above discussion, the process of making the bar chart using AnyChart is clear. Thus, dive into other possibilities, when we have to perform a bit complex data visualization job to do. Of course, JavaScript chat creating is not at all complicated for smart web developers.

Lets, go to the advanced method that you can use from the same library.

Create Multi-Series Bar Chart

Besides the single series bar chart, AnyChart JS will allow you to create a multi-series bar chart, that enables to show the multiple sets of data on charts plot. Series referred to the single data set in AnyChat that will also display in chart’s plot. Now with the help of multiple series chart, it is easy to visualize the detailed information with the insights to the audience.

To build a Multi serious bar chart using AnyChart, it is essential to add data adopter module. It will help you load the HTML data in the work environment.

Parse data from the HTML table

Now create the data and specify the data source

Add a legend if you want your audience to understand and read the meaning of the values

Upon running the codes, it will create the multi-series bar chart displaying the composition of the values and understanding of the data as well.

Stacked Bar Chart

It is quite easy, without making any huge changes to the multi-series bar chart you can create a stacked bar chart. There are two ways, value stacking, and percent stacking. You can choose any one by setting the scale of the stack mode method to the “value” and “percent”.

Heres how to set:

This is how to set the mode back to the 100% stacked bar chart

Set Interactivity to Charts

All the charts prepared by using AnyChart are interactive by default. Some of the default chart behaviors are highlighting and points when hovered over, directing to hovered tooltips point, etc. In addition, you can tailor the chart’s interactivity to match the specified requirements.

Final Thoughts

Considering all the point it is clear that, creating the interactive JavaScript (HTML5) charts is quite easy with the appropriate JS library. So, here in this guide, we have just touched the surface. You can also visit the AnyChart’s documents and learn other factors.

For any queries, or specific instructions do let us know in the comment section below.

AR – The Future Tech

 

Augmented reality (AR) is the coordination of digital information with live video and the user’s condition in real time. devices utilized for Augmented Reality are generally those of a computer, camera, processor and screen.

Reasons why Augmented Reality will be a future battleground

• Recent Launch of Apple AR Kit
• Social Media Platforms Incorporating Augmented Reality
• It will change the future of marketing
• Increasing number of users embracing Augmented Reality
• Various mobile Apps Utilizing AR

 

 

The most prominent example is in the healthcare industry. You can find more and more professionals engage with augmented reality to leverage their day to day task. 

• A doctor is able to view a patient’s medical history displayed over the latest medical scan, and even over the patient himself.
• Healthcare practitioners are now able to project medical imagery on to patient’s bodies using head mounted displays. Projecting CT scans though the display can give doctors “X-ray vision” of patients and provide important contextual cues for diagnosing patients.
• Patients are educated through simulation about their medical conditions (Cataract or AMD) using apps like Eye Decide.
• Patients get reminders on taking drugs by wearing Google Glass and having an app installed on the device.
• A nurse views a perfect highlighted image of the patient’s veins so the IV can be inserted in one painless attempt.

Some more facts and figures that prove that AR is the next big thing in tech are:

• The dedicated augmented reality market is expected to reach $659.98 million by the end of 2018
• According to Digi-Capital, AR/VR could hit $150 billion in revenue by 2020, with VR taking around $30 billion and AR $120 billion
• By the end of 2017, the sales of augmented reality smart glasses is expected to be worth $1.2 billion
• According to ISACA, 60% to 70% of consumers see clear benefits in using AR and IoT devices in their daily life at work.
• According to Forrester Research, 14.4 million U.S. enterprise workers expected to utilize smart glasses by 2025.
• According to Gartner, smart glasses will save nearly $1 billion per year in the field-service industry.

 

AR is not limited to a particular sphere as well. It can be utilized across all spheres of the market for branding & marketing purposes. 

 

1.Construction, engineering and architecture – A holographic representation provides an unmatched level of real-world proportion, scale, form, and perspective compared to traditional ways of building models.

2.Product configurator –The AR/MR apps are useful to product designers because they result in faster prototyping and 3D model visualization.

3. Healthcare – With AR headsets, doctors and dentists can show their patients a 3D view of the organ or section of the mouth that they are going to operate on.
4. Education – The main advantage is that 3D images and simulations can be created for students of all age groups. It is ideal for STEM education.
5. Augmented field service – Companies can equip their field technicians with AR headsets and ensure that experienced engineers are present to guide technicians working in remote locations.
6. Engaging advertising – Brands can incorporate AR elements in their advertisements and offer coupons to drive customer footfall into the store.
7. Events – Event organizers and exhibitors are turning to Augmented Reality to increase interactivity at their events which helps in attracting visitors.
8. Product demonstrations – Augmented Reality apps can give your potential customers an accurate view about the product. Furniture stores, home decorators, fashion stores are ideally suited to take advantage of this technology.
9. Interactive Websites – Websites which use Augmented Reality have seen a decrease in the bounce rate by their visitors. The result is that sales conversions, downloads and even total page visits increase.
10. AR-enhanced tours – A tourist walking down a historic place can be given information on his mobile phone which has been overlaid with the real world images.

 

Augmented reality along with virtual reality is changing the world on a daily basis. The applications are unlimited and the possibilities are limited by our imagination only.

Red Is Incredible

 

Redis is an in-memory key value datastore written in ANSI C programming language by Salvatore Sanfilippo.  Redis not only supports string datatype but it also supports list,  set, sorted sets, hashes datatypes, and provides a rich set of operations to work with these types. If you have worked with Memcached, an in-memory object caching system, you will find that it is very similar, but Redis is Memcached++.  Redis not only supports rich datatypes, it also supports data replication and can save data on disk.  The key advantages of Redis are :

 

1. Exceptionally Fast : Redis is very fast and can perform about 110000 SETs per second, about 81000 GETs per second. You can use the redis-benchmark utility for doing the same on your machine.
2. Supports Rich data types : Redis natively supports most of the datatypes that most developers already know like list, set, sorted set, hashes. This makes it very easy to solve a variety of problems because we know which problem can be handled better by which data type.
3. Operations are atomic : All the Redis operations are atomic, which ensures that if two clients concurrently access Redis server will get the updated value.
4. MultiUtility Tool : Redis is a multi utility tool and can be used in a number of usecases like caching, messaging-queues (Redis natively supports Publish/ Subscribe ), any short lived data in your application like web application sessions, web page hit counts, etc.  There are a lot of people using Redis and they can be found at the owner website.

 

 

Here are a few things we suggest thinking about when you are utilising the superpowers of Redis.

• Choose consistent ways to name and prefix your keys.  Manage your namespace.
• Create a “registry” of key prefixes which maps each to your internal documents for those application which “own” them.
• For every class of data you put into your Redis infrastructure: design, implement and test the mechanisms for garbage collection and/or data migration to archival storage.
• Design, implement and test a sharding (consistent hashing) library before you’ve invested much into your application deployment and ensure that you keep a registry of “shards” replicated on each server.

 

Let us explain each of these points in brief.

 

You should assume, from the outset, that your Redis infrastructure will be a common resource used by a number of applications or separate modules.  You can have multiple databases on each server numbered 0 through 31 by default, though you can increase the number of these.  However, it’s best to assume that you’ll need to use key prefixes to avoid collisions among various different application/modules.

 

Consistent key prefixing & Managing your namespace:

Your applications/modules should provide the flexibility to change these key prefixes dynamically.  Be sure that all keys are synthesized from the application/module prefix concatenated with the key that you’re manipulating; make hard-coding of key strings verboten.

 

Registry: Document and Track your namespace

We suggest that you have certain key patterns (prefixes or glob patterns) as “reserved” on your Redis servers.  For example you can have __key_registry__ (similar to the Python reserved method/attribute names) as a hash of key prefixes to URLs into your wiki or Trac or whatever internal documentation site you use.  Thus you can perform housekeeping on your database contents and track down who/what is responsible for every key you find in any database.  Institute a policy that any key which doesn’t match any pattern in your registry can/will be summarily removed by your automated housekeeping.

 

Garbage Collection: 

In a persistent, shared, key/value store, and in the case of Redis, in particular the collection of garbage is probably the single major maintenance issue. 

So you need to consider how you’re going to select the data that needs to be migrated out of Redis perhaps into your SQL/RDBMS or into some other form of archival storage, and how you’re going to track and purge data which is out-of-date or useless. 

The obvious approaches involve the use of the EXPIRE or EXPIREAT features/commands.  This allows Redis to manage the garbage collection for you, either relative to your manipulation of any given key, or in terms of an absolute time specification.  The only trick about Redis expiration is that you must reset it every single time.

 

Sharding: 

Redis doesn’t provide sharding.  You should probably assume that you’ll grow beyond the capacity of a single Redis server. Slaves are for redundancy, not for scaling, though you can offload some read-only operations to slaves if you have some way to manage the data consistency, for example the ZSET of key/timestamp values describe for expiry can also be used for some offline bulk processing operations; also the pub/sub features can be used for the master to provide hints regarding the quiescence of selected keys/data.

 

So you should consider writing your own abstraction layer to provide sharding.  Basically imagine that you have implemented a consistent hashing method and you run every synthesized key through that before you use it.  While you only have a single Redis server then the hash to server mapping always ends up pointing to your only server.  Later if you need to add more servers then you can adjust the mapping so that half or a third of your keys resolve to your other  servers.  Of course you’ll want to implement this so that the failure on a primary server causes your library/service module to automatically retry on the secondary and possibly any tertiary server.   Depending on your application you might even have the tertiary attempts fetch certain types of data from an another data source entirely.

 

Microsoft Azure

 

The tech giant, Microsoft has launched major new machine learning tools. The company has made exciting announcements for developers who want to build new AI models.

Microsoft has launched three new tools at company’s Ignite conference. The new set of tools are designed to help developers working new AI models as well as those who want simply want to use pre-existing models. The company has launched Azure Machine Learning Experimentation service, Azure Machine Learning Workbench, and the Azure Machine Learning Model Management service.

 

 

Azure Machine Learning Experimentation Service

 

The service is designed to help developers train and deploy ML experiments. Microsoft has added the support for popular open source frameworks like PyTorch, Caffe2, CNTK, TensorFlow, and Cahiner. The service is designed to scale from local machines to hundreds of GPUs in the cloud. It also supports Apache Spark on Azure HDInsight clusters.

 

Azure Machine Learning Model Management Service

 

This is a desktop client for Windows and Mac. The tool can act as a control panel for your development lifecycle. Microsoft is projecting the tool as the great way to get started with machine learning. The new model management service uses Docker containers. The developers and data scientists can manage and deploy their models to any Docker container. Microsoft has included its own Kubernetes-based Azure Container Service.

Microsoft will continue to expand its toolbox for developers that want to build machine learning applications. The company is promoting its tools among startups that are working on innovative technologies. Microsoft’s latest offerings in Machine Learning can fuel the innovation across the globe.

 

Applications To Help The Modern Student

A students life is generally spent on the run juggling between petty finances, studies, assignments and grades. Instead of spending time on social media apps, there are a few apps out there which can help students be more productive.

We bring to you 5 such apps for students which can prove beneficial for them.

 

Punchit

Nowadays, comparison becomes the most important part of our life from shopping on E-commerce platform to buying a birthday gift for your love once. Punchit is an amazing platform where you can compare anything in the very easiest way.

 

Awesummly

This is an amazing short news apps which will save you a lot of time and effort. It also helps you to stay in the competitive times. It is great for searching and organizing news stories. You can save news articles and other blogs as well. The best part is that after you save your content, you don’t need an internet connection to see it again. Also whenever you find any interesting news on web you can short it by clicking on the option to short with Awesummly and you don’t have to read the full article. 

 

CamScanner

Have an urgent assignment to be sent to a friend? Scan using CamScanner, create an instant PDF and send it along. The app also comes to the rescue when a few pages from a library book needs to be scanned in an easy manner. 

Evernote

So we all make notes during that lecture. The best thing about Evernote is its cross-platform availability and despite the fact that it is hugely popular, this one never grows old in the list. Along with those notes, you can use images and voice notes as well. 

In Fact

It is one of the best application for students. As students need to be aware of the world and the latest events around the world, this app allows students access to information according to their needs on a daily basis. In Fact brings the best of facts, knowledge and information from across the world.

 

Utilize these apps and stay ahead of the times.

 

 

 

Big Data In Real Life

Big Data is a never ending, never wilting sector of technology that amazes everyone with its capabilities. But still some of us do not completely understand its usefulness.

Let us look at a real life problem to understand the usefulness of Big Data.

McDonald’s serves both cold drinks and hot coffee along with its burgers all day long. But let’s say they realize that the hot coffee doesn’t have enough demand and their raw material which they fill in the coffee machine in the morning is wasted at the end of the day every day.

Now, McDonald’s wants to figure out how they can reduce the losses based on when their customers actually buy hot coffee.

We will assume that the coffee machine of McDonald’s needs to be filled in the morning for coffee to be available throughout the day and you cannot use it as an instant coffee machine. This is for simplicity.

This is a real life example of data analytics. McDonald’s can do two things in this case:

1. Based on the sales figures, they can figure out how much coffee is actually consumed daily. Based on the average consumption of coffee daily, they can then put in the raw materials in that quantity only and reduce the wastage of unused raw materials. This requires you to analyze the data of the sales of hot coffee in the McDonald’s outlet and then figure out how much coffee is sold every day. What are the trends in the sales – are their days or times when the sales go up or down, are there huge spikes or a normal distribution of consumption, etc. Once you have the answers to all these questions, you can tell McDonald’s how much coffee they need to put in the machine every morning.

2. But let’s say the coffee machine uses a lot of electricity and just saving the costs of wastage of raw materials is not enough. In order to make a significant saving, you need to also switch off the coffee machine when the demand is low. Now, based on the time of sale, you need to figure out at what time of the say the sales of hot coffee go up, are these patterns consistent over long periods of time, is there some correlation between the time of the day and the consumption, etc. This will require you to do a statistical analysis of the data based on which McDonald’s will decide what time to run the coffee machines at.

McDonald’s saves thousands of dollars by reducing wastage and optimizing supply in line with the demand. A fairly simple application of Big Data.

Let us cite a few more brief example from our everyday lives. 

1) Every time you log on to Google, Facebook and see ads, they are based on your preferences, browsing history, FB likes/groups, what your friends liked and so on.

– Profiling models and Ad Targeting

2) Every time you try to buy an air ticket online, the prices vary on the basis of the route, demand, expected last-minute demand, how early you book and so on.

– Revenue Management

3) Every time you log on to e-retail sites and look at a product, you’ll start getting recommendations for other products also considered by other visitors.  If you end up buying something, you’ll get recommendations for other products that bundle with it.

E.g. Buy a phone and it will recommend a case or glass protector

– Recommendation Engines

4) If you make ISD calls or STD calls in India, you might get a recommendation for a STD/ISD package.  The idea being to convert what is unguaranteed future income.

Look around and you’ll see the results of Big Data at work!

One more striking example is how the Afghanistan Conflict was better understood by NYU students.

“Drew Conway was a Ph.D. student at New York University who also ran the popular, data-centric Zero Intelligence Agents blog. He analyzed several terabytes worth of Wikileaks data to determine key trends around U.S. and coalition troop activity in Afghanistan. Conway used the R statistics language first to sort the overall flow of information in the five Afghanistan regions, categorized by type of activity: enemy, neutral, ally, and then to identify key patterns from the data. His findings gave credence to a number of popular theories on troop activity there–that there were seasonal spikes in conflict with the Taliban and most coalition activity stemmed from the “Ring Road” that surrounds the capital, Kabul, to name a few.

Through this work, Conway helped the public glean additional insight into the state of affairs for American troops in Afghanistan and the high degree of combat they experienced there.”

Big Data can be applied to any field and can be utilized in many ways. You need to have the vision and the expertise.