Augmented reality technology has evolved into one of the most effective distance learning solutions. It enables tutors to both educate and engage their students in the learning process. It is partly due to the fact that the modern generation became acquainted with technology at a young age. What do you know about the applications of augmented reality in distance education?
Technology is already ingrained in the lives of today’s students. They have been using it since they were children. During the lockdown, most educational institutions switched to distance learning, believing that it was the best way to educate and stay safe during the pandemic.
Nonetheless, because they had no prior experience, this move proved to be difficult. It also caused many problems, such as efficient education and keeping students engaged at all times. Augmented reality has evolved into a helpful tool, allowing for the resolution of the majority of problems.
Augmented reality (AR) is a modern technology that allows a person to connect the physical and virtual worlds and interact with them in real time. It allows virtual objects to be integrated into the physical world and the educational process as well.
There are numerous benefits to using augmented reality in distance learning:
It allows students to share content regardless of their location.
It gives students quick access to high-quality, fun, and interactive experiences.
It increased students’ motivation, attention, and confidence.
It does not always necessitate the installation of a specific app or a monthly subscription, etc.
It enables students to take virtual field trips, transporting them to a physical, real-world location thousands of kilometres away.
AR has the potential to alter how students interact with visual graphic experiences. It is capable of integrating computer-generated graphics into the real-world environment displayed on the screen.
Getting Practical Skills In Addition To Theory
It seems that distance learning appears to be all about theory, with no room for practice. Nonetheless, AR provides the ability to model a specific situation using a graphic representation of the environment. For example, students learn the topic regarding the lives of savannah tribes. They can travel far into the past and feel as if they are living in those days, thanks to augmented reality.
The learning process of pilots is another good example of AR use. Using it, students learn to apply their theoretical knowledge into practice. It helps to save their lives in the event of a mistake and practice a specific situation more than once.
Presentation Of Complex Phenomena and Subjects
There are concepts and phenomena in the educational program that are difficult to imagine without visual presentation. For example, cell structure of plants, molecule structure, interaction of chemical components at the molecular level, cell decay, etc. AR enables students to comprehend not only the structure of specific elements, but also to investigate how forest plants and animals interact to create a sustainable environment.
Virtual Trips And Language Advancement
A student can, for example, use special AR glasses (such as Epson Moverio or Everysight Raptor) and a downloaded app to immerse himself in Paris and learn French. Furthermore, after an exciting trip, he can take a test to assess his progress in learning a language. For example, the educational company Unimersiv created the House of Languages program. After installing it on a smartphone, a student can study foreign languages, visit virtual museums, airports, and cafes, among other things.
Taking a virtual trip is especially beneficial when a student is required to write an essay or report on a specific topic. Given that many museums and libraries had locked their doors during the pandemic, it proved to be a difficult task.
Using the AR technology, a student can get a complete understanding of the subject under study, which eases handling any type of writing. If you are still having trouble coming up with ideas, you can check out the top writers list to get writing help.
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A growing number of colleges and universities are experimenting with AR in these areas and using it to support curriculum delivery. AR engages students’ senses and enhances learning by immersing them in a world rich in both information and experience: it provides a learning experience that students are unlikely to forget.
Do what you do best and partner for the rest! This fits well with how Telcos are approaching the digitalization wave. Telecom leaders are aware of what value partners can bring in, as CSPs look to expand their value chain and revenue streams by exploring cross-industry business opportunities. In fact, the partnership strategy is not new to Telcos, but it indeed is becoming prominent with the evolving business models as 5G, IoT becomes mainstream. The partner ecosystem enables CSPs to accelerate innovation, increase agility and lower the operating cost by offsetting pressure from traditional services.
Many CSPs have seized partnerships with other industry verticals to capitalize on the 5G promise. Deutsche Telecom (DT) announced a 5G partnership to support the smart industries and accelerate digitalization in the industry. Reliance Jio, the Indian operator, has also transformed itself into a digital service provider by offering an array of services under the JIO brand (see figure 1).
Telia created Division X, a separate business to focus on emerging businesses such as IoT, 5G, and AI by creating a digital ecosystem-enabled platform to monetize joint offerings with partners.
We have seen both horizontal as well as vertical expansion by operators to add more value to the telco value chain. The diversification of services through collaboration and co-creation with the B2B2X business model is the new norm.
So how well are Telcos able to adopt the partner ecosystem strategy? We might have seen some progress, but how can they accelerate this to match the evolving customer needs. A telco needs to define its role in the evolving value chain and ensure a successful transition into the role of an enabler or provider of new-age services.
What Telcos can bring to the table?
Playing to their strength while adopting the digital transition.
CSPs need to constantly keep innovating the service offerings to entice the digitally savvy enterprise and end consumers. They need to start thinking like Google, Amazon, or any webscale organization to embrace an end-to-end digital transformation. The traditional way is not sustainable and demands a reshuffle of the strategic focus and priorities set in the past. Telcos need to move away from connectivity providers’ perception and start leveraging their core competencies such as solid customer base, insights into customer needs, network assets as a platform to digital disruptors, and more.
The way forward strategy: Partner Ecosystem development
Telcos have been working with interconnect partners, roaming, MVNOs, other value-added service providers. But these partnerships are low involvement, with limited monitoring and contract management requirements. The new partnerships are becoming more complex and dynamic with the diversification of services and partners from across industry verticals.
One of the reports by a leading analyst organization revealed that CSPs are already cut out of strategic engagements and solution building with enterprise partners. CSPs are playing a secondary supplier role in 40% of enterprise 5G deals are signed. To capitalize on these opportunities, CSPs need to strengthen their position by creating a robust digital partner ecosystem that can deliver value with their offerings.
Where the telcos will see the most opportunities in near time:
Research shows that key industry verticals such as industrial automation, healthcare, connected cars, intelligent homes represent a $1 trillion opportunity by 2023. Telcos will support a wide variety of use cases with network slicing, edge computing and AI. However, a clear monetization strategy will have to be in place for these new revenue streams.
Healthcare: As there is a radical shift towards digital health services, it has created an opportunity for Telcos to offer telemedicine solutions for remote health monitoring and health management for people with chronic diseases. The low latency and ultra-reliable connectivity will provide accurate feeling and tactile interaction in remote surgical procedures.
Mobility: As the 5G networks roll out across cities and bring together existing wireless networks, they can provide real-time, end-to-end visibility into the transportation systems. Increased fleet visibility will also translate into better safety and reliability for travelers.
Gaming: 63% percent of gamers play with other fellow gamers. In fact, massively multiplayer online games make up the most popular gaming genre globally, but most gamers, especially multiplayer gamers, must deal with lag. By utilizing 5G end-to-end network slicing, operators can create a low latency-focused slice to offer an enhanced gaming experience, while a separate high bandwidth slice can be created for video streamers within the same mobile network.
Smart Industries: 5G will help create a more agile, fully connected, and automated end-to-end manufacturing experience from design to distribution. Supported by the unprecedented levels of AI/ML and automation, the smart industries will make faster decisions and quickly adjust to changes in near real-time.
While the opportunities are immense, Telcos alone cannot deliver the success that 5G promises. CSPs need to establish successful partnerships with digital incumbents and innovative startups on both the technology and service front to deliver the 5G promise to its enterprise and end consumers. One certain thing for success is, CSPs need to bring cooperation and collaboration with partners at the center of value creation.
Subex is hosting a live webinar on “Innovating and Accelerating Growth in the 5G world” that will take a deep dive into the new generation of the partner ecosystem and how it can help CSPs deliver the 5G promises.
Non-Fungible Tokens (NFTs) are taking the art world by storm.
A large number of serious problems outweigh any positives.
Two infographics to explain the process and issues.
The above image shows how an object of value, like an artwork, music file or GIF, can be “minted” and sold via a non-fungible token (NFT). An NFT is much like a certificate of authenticity. But instead of a physical certificate, you own a token: a unique piece of data on a blockchain. NFTs work as public ledgers, recording each transaction associated with the sale of an artwork. When you purchase an NFT, you’re essentially purchasing a tamper-proof digital receipt.
An NFT is not:
An artwork—digital or otherwise. The buyer doesn’t actually possess the original item at all. It is permanently stored elsewhere. A secure method is to attach the work permanently to an Ethereum blockchain—containing the work, the unique identifier, and an ownership record. However, it is possible to store the art on a server separate from the NFT.
A right to copy, disseminate, or display the artwork [1]. The creator of the artwork usually keeps these rights. The buyer’s only right is that of ownership of an “original copy.” For example, the artwork in the image above is a portrait of Diana Ross I created in 2013. I sold the physical artwork and kept a digital copy. I could sell ownership of the digital image with an NFT (but I’m not going to, because of the environmental concerns outlined below).
An exclusive digital version of the artwork. Digital artist Beeple made history earlier this year when Christie’s auction house sold an an NFT consisting of 5,000 of his illustrations for over $69 million. However, he posted each element of the art on Instagram; Anyone can download a free copy, albeit without that prized COA.
NFTs do have a couple of positives: They provide a solution to tracking digital artwork and verifying ownership. In addition, the technology is enabling artists to make up for lost income due to the pandemic lockdowns. However, the marketplace is suffering from a deluge of criticism for a variety of issues.
The Bad…and The Ugly
At the top of the list: Environmental issues: There is hot debate about how much energy is used specifically with NFTs. But we do know that its close companion, cryptocurrency, uses more energy than the whole of Denmark (or Argentina). A sale of just six NFTs is estimated to use ten times the energy that an average American uses in a month [2].
Many other issues are plaguing the inchoate technology:
Fraud: Forgery is a pervasive problem for physical art collectors, and it has infiltrated the digital market as well. The lack of legislative control adds another layer of risk.
Ownership issues: Paying several thousand dollars for a virtual “token” falls into the realm of legal quagmire. From a legal perspective, it isn’t clear who owns what.
Prohibitive costs for artists: Artists can lose money due to “gas” and other fees associated with selling on Ethereum, even with minimum prices in the hundreds of dollars range. Cryptocurrency fees can be so unpredictable and difficult to comprehend that some artists are losing before they even post a work for sale. One artist reported on Reddit that “Fees out the behind” for money transfers caused him to lose $45 before he could even list his artwork [3].
The bubble is about to burst. The NFT marketplace has also been dismissed by many market professionals as a “collector” bubble. Remember the Beanie Babies craze? Decades ago, these small cloth toys traded for thousands of dollars. Most are now worthless. James Surowiecki, a columnist for The Slate and The New Yorker, states that investing in collectibles is “far more lucrative when you get on it early”– and that time has passed. “There’s the very real possibility that the whole thing will crash,” he says [4].
Tech Issues: NFTs are contributing to a global silicon chip shortage [5]. In addition, some buyers of NFTs aren’t aware of where their art is digitally stored. If it’s on a private server that crashes, the token will become worthless.
It’s doubtful that so many resources should be used for something that adds dubious value to the human experience. Until the serious problems with NFTs are fixed, visit a local art gallery and support your local artist.
Digital Twins are increasing in usage but are often used in multiple contexts and in a simplified manner. Most references to the Digital Twin actually refer to a Digital shadow i.e. maintaining a digital copy of a physical object that is updated periodically. In a more complete sense, the Digital Twin concept relates to simulation and interaction of complex, multiple physical objects in a digital environment (typically for Engineering and Construction)
I am interested in the idea of Digital Twin because my teaching at the #universityofoxford applies more to AI in engineering (as opposed to say financial services).
Also, Digital Twins relate to the idea of Physics based modelling in Engineering. A wind tunnel is an example of Physics based model. Hence, one could think of a corresponding digital entity to the physical model which simulates the behavior of the model in a digital sense.
For this reason, digital twins are one of the best conceptual mechanisms for incorporating artificial intelligence into large-scale, dynamic engineering problems – especially considering existing ideas of physics-based modelling in engineering.
Digital twin technology is already used in various industrial sectors such as aerospace, infrastructure and automotive.
A paper I recently read talks about how Digital twins can be implemented through surrogate modeling.
The paper uses a discrete damped dynamic system to explore the concept of a digital twin.
The paper uses Gaussian process (GP) emulator within the digital twin technology is explored. GP has the inherent capability of addressing noisy and sparse data.
GP is a probabilistic machine learning technique that attempts to infer a distribution and then use that distribution to predict unknown points.
GP has two distinct advantages over other surrogate models:
GP is a probabilistic surrogate model, it is resistant to overfitting.
GP can measure the uncertainty which can then be used in the decision-making process
Additional notes from the paper
GP not only model and also the example (spring) is a relatively simple one for explanation
As IoT proliferates, digital twins would get more complex based on increasing data being reflected in the virtual world from the physical world
Digital twins / surrogate modelling approach suits dynamically evolving systems
Typically, the digital twin starts from an ‘initial model’ which is often a physics-based model.
Over time, as more and more components can be modelled virtually, digital twins of larger (composite) objects would become the norm ex aircraft, automobiles etc
“If you make decisions based upon averages, at best, you’ll get average results”
During the 1950s[1], United States Air Force pilots were having trouble controlling their planes. The problem turned out to be the cockpit, or more specifically, the fact that the cockpit had just one design: one designed for the 1920’s average pilot. The Air Force concluded that they simply needed to update their measurement of the average pilot, adjust the cockpit accordingly, and the pilot handling troubles would go away.
With the help of Lieutenant Gilbert Daniels, the Air Force measured more than 4,000 pilots across 10 size dimensions. The air force had assumed that the vast majority of pilots would fall within average across the 10 dimensions. In reality, none – none – fell within average across the 10 dimensions; that is, out of 4,000 pilots, zero of them were “average” (see Figure 1).
Figure1: The Danger of Making Decisions Based Upon Averages
The Air Force’s “aha” moment?
If the cockpit was designed for the average pilot, it was actually designed for no pilot
Todd Rose came up with the Jaggedness Principle of individuality. The Jaggedness Principle asserts that measuring a collection of traits across a sufficiently large number of individuals, roughly half of individuals will be above average, and roughly half will be below average for any particular trait. And that across all the traits, few (if anyone) will actually be “average” (notice the “jagged” line for each individual in Figure 2).
Since no one is “average”, why do organizations continue to make decisions based upon averages? We have spent much of our university education and professional career being taught to make decisions based upon averages – average churn rate, average click rate, average market basket size, average mortality rates, average COVID19 infection and death rates. And maybe when your data analytics tool of choice was a spreadsheet, then the best we could do was using averages to make overly generalized policy and operational decisions. But the world is changing… and changing rapidly!
Unfortunately, averages don’t provide the level of granularity necessary to make precision decisions that drive the optimization of the organization’s key business and operational use cases. “On average” is not how successful companies will survive in a world of continuous transformation.
Fortunately, Big Data, Data Science, Analytic Profiles, and Nanoeconomics provide the foundation for changing the organization’s decision-making frame. It’s time for organizations – and management teams – to “Cross the Analytics Chasm” from making overly-generalized operational and policy decisions based upon averages, to making granular, precision decisions using big data and data science (see Figure 3).
Some critical concepts for crossing the Analytics Chasm include:
Nanoeconomics. Nanoeconomics is the important concept guiding organizations across the Analytics Chasm. Nanoeconomics is economic theory of individual entity (asset) predicted propensities, whether the entity (asset) be human (doctor, nurse, technician, operator, teacher) or device (wind turbine, automobile, chiller, compressor). See Figure 4.
Figure4: Nanoeconomics is economic theory of individual entity predicted propensities
Nanoeconomics is based upon identifying and codifying individual asset (human or device) predictable propensities, tendencies, patterns, and relationships. And from those predicted propensities, organizations can make informed, precision decisions that seek to optimize the organization’s key business and operational use cases.
Analytic Profiles. Those predictive propensities are captured in Analytic Profiles (or asset models) that facilitates the application of those customer, product, and operational propensities against the organization’s key business and operational use cases (see Figure 5).
“If you make decisions based upon averages, at best, you’ll get average results”
Crossing the Analytics Chasm requires a mind shift in how organization’s make decisions. Making decisions based “on average” is not how successful companies will survive in the age of digital economic transformation. Organizations need to embrace the power of nanoeconomics – the economics of individual entity (human or device asset) predicted propensities.
Organizations can couple the concept of nanoeconomics with Analytic Profiles to leap across the Analytics Chasm in transitioning from decisions based on averages, to decisions based upon predicted propensities. And as a result, these organizations can become more effective at leveraging data and analytics to power their business and operational models (see Figure 6).
The valuable data and analytic concepts mastered to cross the Analytics Chasm – nanoeconomics and analytic profiles – positions the organization to exploit the economic potential of data and analytics and transverse the Big Data Business Mode Maturity Index to re-invest business and operational processes, dis-intermediate customer relationships, and transform industry value creation processes.
But ya gotta start by getting over that darn Analytics Chasm…
[1] Story taken from the Harvard Graduate School of Education article “Beyond Average”
You have a product that has taken off. Your daily active users metric has been growing exponentially. The number of events per day you’re logging is now in the 100’s of millions.
As a result you now find yourself with terabytes of data or if you have become really successful hundreds of terabytes.
You begin to wonder if you could use all of this data to improve your business. Maybe you can use the data to create a more personalized experience for the users of your product. Or maybe you can use the data to discover demand for new products.
You request that your data team come up with way to leverage this data to do just these types of things.
The data team that you have hired recommends that you develop a data pipeline. An end-point of that pipeline being the data warehouse.
You may get something like this:
Data Pipeline and Data Warehouse
But after months of work, and many dollars spent building the data warehouse, the data scientists that you hired can’t come up with the insights.
How could all of that data, all of those IT consulting hours, and those cloud computing resources be marshalled to not produce the insights?
The problem likely lies in one of the important components of your pipeline: the data warehouse
Here are some of the painful things you can experience in the data warehouse:
Poor Quality Data
Data that is Hard to Understand
Inaccurate / Untested Data
A Slow Data Warehouse
A Poorly Designed Data Warehouse
A Data Warehouse that Costs Too Much
A Data Warehouse that Does Not Factor in Privacy Requirements
Poor Quality Data
You data may be streaming in from multiple sources. When an analyst runs a JOIN on this data, it could result in a table that is inconsistent. Inconsistent data can manifest itself as missing columns that are required to properly identify each data item. Or the data may contain duplicates that take extra space and prevent from performing the aggregations necessary to achieve insights without extra work (meaning extra analyst time cleaning the data via interpolation, and extra compute hours deduplicating the data).
Data that is Hard to Understand
You have PhD’s on your analyst team. Why are they scratching their heads and shrugging their shoulders after looking at your data? It could be that the tables in the data warehouse are an enigma.
A lot of times, the data warehouse is built by a different team than the analysts. Both groups are trying to manage data but are not necessarily playing for the same data team.
Oftentimes the tables are created in a way that makes it easy to create the table and but not easy to be processed downstream. The table is created without taking the downstream requirements into consideration! Noone thought to begin the data warehouse design with the end goal in mind of quickly enabling insight generation.
Inaccurate / Untested Data
Data items can be wrong. Data items may reflect something that is not possible. The data may reflect something going on in society that you do not want to serve as a basis for downstream analysis. The data must be accurate otherwise, it will lead your analysis to wrong or detrimental insights. Untested data is worse than not having any data.
A Slow Data Warehouse
A data warehouse can be of no use because it takes too long to query, or goes down often. If users are not trained on how to write efficient queries or if the warehouse is not developed to automatically scale with the growth of the data, and if there are no protections in place to prevent abuse of the compute resources of the warehouse your insights will never materialize.
Poorly Designed Data Warehouse
Business leaders who launch a data warehouse without first considering the business needs and translating these into actionable tasks will likely get a data warehouse that does not meet their business needs.
Not understanding these business needs upfront leads to miscommunication amongst the analysts, which leads to confused insights.
A Data Warehouse that Costs Too Much
One possible cause of a costly warehouse is not matching the right warehouse implementation option to your needs. Not every organization needs to create a from-scratch, on-premise, data warehouse. Doing this takes a lot of time, a lot of the right human resources, and equipment. This can yield a project that is late, over budget, and expensive to maintain or upgrade. As a result over time your warehouse becomes less useful as other priorities consume the organization’s resources.
A Data Warehouse that Does Not Factor in Privacy Requirements
Even if your product is a game, or something purely consumer oriented, and even if you spell out clearly in the terms of service that whatever data the user shares is yours, you still can’t ignore how the data warehouse will protect your user’s identifiable information.
Not taking this into consideration can result in people in the company being able to look up specific users for non-business purposes. It can result in people in the company misusing personally identifiable information, which can hurt your users, and negatively impact daily active user growth. It can result in personally identifiable information inadvertently leaking somewhere downstream.
How to Deal?
There is no magic bullet to addressing these many issues. While some of these issues are technical in nature (and just require the right no-how), others are organizational–meaning you can’t just download a free-ware tool to solve them.
But briefly, some of these issues can be addressed by:
Have a well organized product development process. Using agile
Realize that there is no one-size fits all data warehouse. You will have to some warehouses that are configured to be high-speed data stores to capture data streaming in from your product. These are data warehouses that are configured to prioritize transactional activity. Other data warehouses will be configured to be always-on, highly-available, scalable, and reliable data stores whose purpose is to hold your 100s of terabytes of data in a queryable form to enable the data analysts.
Nowadays, businesses have to rely on data in unprecedented ways. In fact, businesses hailing from various disciplines use massive amounts of data on a daily basis. They gather data from several sources, offline and online. However, it is also important to compile and process that data and analyze it using apt software solutions. That is why Data visualization applications are used by so many companies. From technology giants to leading MNCs, plenty of companies are relying on BI and data visualization solutions like Microsoft Power BI. For effective Power BI implementation, hiring a veteran development agency is recommended.
Understanding the Importance of Data visualization
Before you invest in a specialized data visualization tool like Power BI or buy it, it is necessary that you know the significance of data visualization. Your business may obtain data from myriads of sources. Analysis of that data helps in understanding customer preferences, areas of improvement and market trends, etc. This, in turn, helps the businesses take key decisions and make strategic moves. To understand the analyzed data properly, presenting it in a comprehensible visual manner is necessary. That is where data visualization steps in.
Microsoft Power BI as a data visualization tool
Microsoft Power BI is a BI solution that has robust and embedded data visualization capabilities. The data compiled and analyzed by the tool is visually represented using several elements. These include graphs, videos, charts, images, etc. These visual elements help the users and viewers understand the data well. It is possible to use many filters or parameters to represent data in specific ways. Dashboards and reports are also key features present in the application. Of course, you will gain from the services of a veteran Power BI developer for utilizing these elements.
The elements in Power BI used for data visualization
As it is, Power BI comes with a wide range of data visualization elements. These include:
Charts of varying types
Area Charts– It is also referred to as a layered area chart. It is used to indicate a change in one or several quantities over time. Area charts should be used when the user wants to display and see any variable’s trend over time. For example, it can be deployed to get a glimpse at the workforce productivity in various quarters. You may also use it to analyze the sales and expenditure of the company quarter-wise.
Line chart– A line chart is one of the widely used visual elements in Power BI. It is useful when you want to visually represent trends over time. The data points are joined by a straight line horizontally. For example, it can be used to represent the sales figure of a company in a financial year.
Bar Charts– These charts are used to represent categorical data through horizontal bars. This is used a lot on Power BI as they are easy to comprehend. This chart can be used to represent the growth rate of various departments in a company, per quarter, for example.
Combo chart– A Combo chart blends a column chart and line chart. They can be useful when you want to compare several measures with varying value ranges. They can be used to illustrate the association between two measures in a single visualization.
Doughnut charts– A doughnut chart is much like a pie chart, and it is used when it is necessary to display the relationship of a section to a whole. However, users need to remember that doughnut chart values should add up to 100%. Using too many categories in a doughnut chart makes it hard to read.
Funnel charts– Funnel charts are used when it is necessary to illustrate sequential connected stages in any process. It is used widely to show sales processes. Each funnel stage denotes a certain percentage of the total amount. A funnel chart resembles a funnel, with the first stage being the biggest in size.
Pie charts– Pie chart is somewhat like a donut chart, and the combination of all segments must add up to 100%. The data is segregated into slices, and it is useful for representing the similar category of data.
Gauge charts– A gauge chart may remind you of the speedometer used in regular car dashboards. In it, a needle is used for data reading.
There are some other types of charts available in Power BI, like the waterfall chart. However, these are typically used by the Power Bi Experts.
Maps
In Power BI, you can make use of maps to represent sales data. This is accessible through the globe icon in the tool’s visualization pane. You have to pick the required categories.
There are three types of maps, namely Flow maps, point maps, and regional maps.
R and Python for data visualization
Microsoft has made it possible to use R and Python to enhance the data visualization prowess of Power BI. This can be immensely helpful for the end-users who want their reports to be as information-rich and visually enticing as possible.
R is a language that is used extensively for graphics and statistical computing. For that, it is necessary to have R studio and necessary packages and libraries in place. R provides a robust platform for data visualization and analysis. In fact, with it, you can visualize data prior to the analysis.
Python is another programming language that can be used with Power BI. It is necessary to set up Python with the necessary libraries and packages in the system. Python, in fact, has been used for years for data visualization needs. However, it lacks robust chart generating options, which can be achieved by integrating it with Power BI.
It is hard to locate another BI and data visualization tool that is enriched with so many visual elements like Power BI. After you equip the dashboard with various visual elements and feel happy with the visual representation of data, you can publish it. Based on the version of Power BI you have, it is possible to share reports that can be seen only by other Power BI users and those who do not use the platform.
Data visualization tips for Power BI users
As it is, Power BI is laden with so many visual elements that using them in the right way can be tedious for some users. This may be tougher for those who are new to the platform. Listed here are some effective tips for extracting the most out of data visualization features in Power BI.
Before using any visual element such as a type of chart or map, think of the purpose and type of data to be represented.
Do not clutter the dashboard using too many visual elements at a time. Also, customize the charts with an apt color and label for making these easy to comprehend.
You can also add visual elements in Power BI that you may have used in the MS Office suite. These include shapes, text boxes, and images. After adding, you can resize these elements as well.
Summing up
As you can see, Power BI is a powerful data visualization tool, and you can use many of its embedded visual elements to showcase your data effectively. However, it is also necessary that you pick the visual elements cautiously and evade overdoing things. You may also seek services of the Power BI development services for creating killer Power BI reports.
Legal Tech refers to the technology used in the legal sector. It has significantly transformed how attorneys and other legal professionals perform their duties. Moreover, it has brought a lot of opportunities for law offices (solo legal practices, law firms, and corporate/government legal departments) by digitally transforming legal operations, helping them meet client demands efficiently and timely.
More interestingly, the scope and adoption of technology are not limited to top legal organizations. Small-size law firms and even legal startups have also invested in technology, taking advantage of the opportunities it offers.
4 Ways Technology is Transforming the Legal Sector:
Advanced technologies simplify lawyers’ work and improve legal services’ quality, all while reducing operational costs. Here’s how.
Bridging Communication Gaps between Lawyers & Clients
Lawyers can now collaborate more effectively with their teams and establish a flexible, more secure medium to communicate with clients by utilizing unified communication tools. This can result in enhanced productivity and client satisfaction.
The Era of Automated eDiscovery
Searching for documents and highlighting or tagging relevant evidence pieces are parts of case preparation that consume a lot of time. Nowadays, most of the paperwork is digital; here, eDiscovery automation software (powered with advanced analytics) can automatically find and tag keywords and key phrases and eliminate irrelevant documents, helping attorneys speed up the entire process.
Case Management Becoming Easier
Many software on the market enable attorneys to manage different case management functions using one platform. For instance, schedule preparation, contact-list organization, document management, billing data entry, etc., are now easier to manage. Besides, any cloud-based case management software allows attorneys to store all the case-relevant data (helpful information) in a centralized location and access it anytime from anywhere. This is even more beneficial for those working remotely.
The Rise Attorneys’ Online Communities
By coming together in large numbers, attorneys create community groups, most often to help people who don’t have access to professional legal advice and counseling. Another motive is to include law students and solo attorneys in community groups and discuss several legal profession-related topics, such as issues, trends, news, etc.
Social media sites and apps, especially Twitter and LinkedIn, are becoming more popular as a forum platform for attorneys to connect with other legal professionals and establish a strong network in the industry.
Challenges Brought by Technology for the Legal World
Undoubtedly, technology is making an attorney’s job and life more manageable. However, it also brings along various challenges; let’s discuss a few.
The Sudden Knock on The Door
A few years back, the sudden occurrence of the technology revolution has left many attorneys (mostly veterans) baffled in their profession. Typical legal processes such as research, documentation, case preparation, etc., used to be managed manually. However, with digitization, many legal tasks are handled by automation-powered software and tools, arising several challenges for attorneys.
Lack of Technical Knowledge & Expertise
Many legal professionals are still unable to understand which technologies are best for their practice and how to make the best use of them to obtain favorable results. Due to this, many law firms often choose legal process management servicesprovided by external firms having skilled legal professionals with all required technical knowledge and capabilities.
Issues on Organizational Level
Due to the rapid and intense emergence of technology in legal, lawyers and law firms need a massive operational overhaul, transforming several processes. From lead generation to revenue recognition, everything needs to be changed now. Consequently, law firms now have to deal with challenging situations such as determining the usage scope of legal tech, developing new business models, establishing policies, etc.
Financial Restraints
The cost of technology adoption and maintenance put a question on many law firms’ budgets, forcing them to think twice before making any tech investment. Many legal organizations overlook the advantages of technology for their practice because of financial problems.
Nowadays, individuals or businesses in legal need would prefer choosing a tech-driven legal services firm. Besides attracting more potential clients, here are some other benefits of the continuously growing legal tech.
Benefits of Legal Tech
Reduction in Manual Effort
Legal tech, for example, data processing, document management, eDiscovery software, etc., automatically manages these processes, allowing lawyers to free up a significant amount of time. As a result, they can utilize this time to communicate with clients and prepare case files for the effective representation of clients in the courtroom.
Research Work becomes Easy
Legal research tools assist lawyers in becoming updated about different rules and regulations. Such software can also identify relevant documents on the internet, shortlist them, and even highlight key phrases that can be helpful for an attorney to make their argument stronger.
Better Management of Resources
Various applications for title management and calendaring allow attorneys to efficiently manage work related to titles and get valuable insights regarding all tasks scheduled for a particular workday. This enables senior attorneys to utilize resources (paralegals or other clerical staff members) more effectively, bringing better results.
Minimized Risk of Mistakes/Errors
Tech-driven data entry and management solutions restrict access to sensitive and confidential information a law firm holds, for instance, clients’ case details. Besides, integrating such tools with analytics can help make better use of the available data.
Enhanced Transparency
With the help of reliable law practice management software, law firms can better control their processes and eliminate workflow issues. These software record real-time information (for example, how much time a paralegal has to spend on a particular client’s case) in a centralized, secure location. This data can then be used for billing and analyze staff performance, productivity, and much more.
Excellent Customer Experience
Using AI-powered legal software, attorneys can send personalized emails to clients. Also, by collecting client data, such software allow law firms understand their clients’ need better, helping them meet their demands well in time. Thus, legal tech can help law firms enhance clients’ experience by providing highly customized legal services.
Conclusion
Solo attorneys, law firms (of all sizes), and corporate/government legal departments must become aware of the technology they can use to improve legal operations. Since many legal processes are shifting toward digital platforms, they need to catch up with legal tech trends and adopt all easily applicable tools. It is time not to be afraid of being replaced by that might be developed in the near future; instead, it is time to gain knowledge, improve technical skills, and utilize technology for the betterment of legal functions, processes, and the overall growth of legal business at large.
Data Science is a broader concept and multidisciplinary.
Data science is a general process and method that analyze and manipulate data.
Data science enables to find the insight and appropriate information from given data.
Data Science creating an opportunity to use data for making key decisions in different business domains and technology.
Data science provides a vast and robust way of visualization techniques to under the data insights.
Machine Learning
Machine learning fits within data science.
Machine learning uses various techniques and algorithms.
Machine learning is a highly iterative process.
Machine Learning algorithms are trained over instances.
Machine Models are learned from past experiences and also analyze the historical data.
Machine Model able to identify patterns in order to make predictions about the future of the given data.
“The main difference between the two is that data science as a broader term not only focuses on algorithms and statistics but also takes care of the entire data processing methodology”
Let’s see quickly the Machine Learning Process – Overview and jump into Train and Test.
Understand the scenario
Certainly, you can assume how the students are getting trained before their board exams by the great teachers in School/College.
At School/College level we use to undergo many more Unit-test/Term exams/Revision exams/Surprise tests and etc., Here we have been trained on various combinations of questions, mix and match patterns.
Hope you all come across these situations many times in your studies. No exceptional data set that we’re going to use in Data Science. All because we need to build a very strong model before we go into deploy the model in a production environment.
Similarly, in the Data Science domain, the Model has been trained by the sample data and makes them predicts the values with the available data set after data wrangling, cleansing, and EDA process, before deploying into the production environment, before the model meets the real-time/streaming data.
This process is always helping us to understand the insight of the data and what/which model we could use for our data set to address the business problems.
Here we must take care of the data set and it should match with real-time/streaming data feed (To align with all combinations), while the model performing in a production environment. So, the choice of data set (data preparation) is really key before the T&T process. Otherwise, the model situation becomes pathetic… as below in the picture. There might be huge effort loss, impact on the project cost and end up with unhappy customer service.
Here you should ask me the below questions.
Why do you split data into Training and Test Sets?
What is a good train test split?
How do you split data into training and testing?
What are training and testing accuracy?
How do you split data into train and test in Python?
What are X_train and Y_train X_test and Y_test?
Is the train test split random?
What is the difference between the training set and the test set?
Let me answer one-by-one here for your benefit to understand better way!
How do you split data into training and testing?
80/20 is a good starting point, giving a balance between comprehensiveness and utility, though this can be adjusted upwards or downwards based upon your model performance and volume of the data.
Training data is the data set on which, you train the model.
Train data from which the model has learned the experiences.
Training sets are used to fit and tune your models.
Test data is the data that is used to check if the model has learned well enough from the experiences it got in the train data set.
Test sets are “unseen” data to evaluate your models.
Architecture view of Test & Train process
CODE to split give dataset
# split our data into training and testing data X_train,X_test,y_train,y_test = train_test_split(X_scaled,y,test_size=.25,random_state=0)
What are training and testing accuracy?
Training accuracy is usually the accuracy we get if we apply the model to the training data
Testing accuracy is the accuracy of the testing data.
It is useful to compare these to identify how Training and Test set doing during the Machine Learning process.
Code
model = LinearRegression() # initialize the LinearRegression model model.fit(X_train,y_train) # we fit the model with the training data
linear_pred = model.predict(X_test) # make prediction with the fitted model
# score the model on the train set print(‘Train score: {}\n’.format(model.score(X_train,y_train))) # score the model on the test set print(‘Test score: {}\n’.format(model.score(X_test,y_test))) # calculate the overall accuracy of the model print(‘Overall model accuracy: {}\n’.format(r2_score(y_test,linear_pred))) # compute the mean squared error of the model
X_train — This includes your all independent variables, (Will share detailed notes on independent and dependent variables) these will be used to train the model.
X_test — This is the remaining portion of the independent variables from the data which will not be used in the training set. Mainly used to make predictions to test the accuracy of the model.
y_train — This is your dependent variable that needs to be predicted by the model, this includes category labels against your independent variables X.
y_test — This is the remaining portion of the dependent variable. these labels will be used to test the accuracy between actual and predicted categories.
NOTE: We need to specify our dependent and Independent variables, before training/fitting the model. Identifying those variables is a big challenge and it should come out from the business problem statement, what we are going to address.
Is the train test split random?
The importance of the random split has been explained in the below picture clearly in a simple way! You could understand from pictorial representation!
In simple text, the model could understand what all data combination are is exists in the give data set.
The random_state parameter is used for initializing the internal random number generator, which will decide the splitting of data into train and test.
Let say! random_state=40, then you will always get the same output the first time you make the split. This would be very useful if you want reproducible results to finalize the model. from the below picture you could understand better why we prefer “RAMDOM Sampling”
Thanks for your time in reading this article! Hope! You all got an idea of Train and Test Split in the ML process.
Will get back to you with a nice topic shortly! Until then bye! See you all soon – Shanthababu
Email Marketing can be challenging. I learnt this lesson from my experience in the digital marketing sphere and being a support representative at an email software company.Why?There are a number of reasons. They come in different forms and from various places and refer tosegmenting an audience, finding contacts, designing a perfect subject line, to name a few.
Such activities require from marketers tons of creativity, consistency and research. Yeap, email marketing is stillone of the most efficient marketing channels due to ROI. This fact only fuels the competition in the industry, leading to seeking new solutions.
Notably,email campaign software has become the go-to optionfor many brands and businesses. Automation interferes in many spheres and enterprises, while digital marketing is not an exception. Email campaign software makes a difference there.
However, how many email platforms are there? A lot. I have been working in digital marketing for some time and understand why one can find very confusing the amount of software available before marketing teams.
That’s whyI have designed a list of the top email marketing software that can add to your small business, startup, or long-term campaign. This post will be helpful for those who have doubts about which email marketing to use or have just started a journey into the marketing world.
Top Email Marketing Services
Before all, the automation tools I am listing in this post are different and answer to similar needs of a marketer. Some of them are all-in-one solutions; others aim to facilitate a specific issue. Interestingly, you can combine one tool with another.
How to choose the best marketing software? Pick the one that will help your business needs or goal.The right email marketing tools are about answering the challenges.What are some that marketers consider crucial? Scheduling, organization,personalization, segmentingand data collection. Each of them is equally important for the open and click rates within lead generation.
At the same time, many of you have struggled with email templates; there are tools for it as well. Among other things, the platforms help to track results and report on valuable data. All in all, it is what a reliable marketing tool is to be expected of.
Let’s look at the options that can help you with the email marketing objectives.
1. Constant Contact
Constant Contractis at the beginning of the list as it has a specific focus on email marketing and has been long enough in business. Despite the idea that I had used it only for a while, many colleagues of mine refer to it as an excellent solution for small business. Why is it good?
First of all,it puts simplicity and accessibility in email campaign designation. For instance, the particular platform offers the management of emails, sending schedule, and content. It refers to template and newsletter creation, together with the insertion of CTA buttons. Importantly, it has integrations with Shopify underlining its usability for small businesses.
Also, it offers email list management and segmenting for better targeting. In the end, it is used by many small companies to generate leads. However, what I heard is that their users wish they paid less for the simplicity the particular platform offers.
2. GetProspect
Have you ever struggled with your email list enrichment? I bet you are.GetProspectemail findermay be a solution with its simple interface, easy-to-use functions, and extracting possibilities. I have worked at this company for some time and must say it does a pretty good job in what they offer. What exactly is it, and what value does it provide to your business?
Well, small businesses usually struggle with getting contacts of their target audience. If you are a b2b service, they may be business owners, CMOs or CEOs of firms. If you are a marketer or SEO specialist, they may be influencers or bloggers. Lastly, if you already have an extensive database, you may need to verify it.GetProspecthas these functions. With it,you can extract the emails from Linkedin or any corporate website.
It’s not the only email finder on the market. Still, it can be integrated into other CRMs byZappierand has a very minimalistic design. Thus, you can extract your groups of contacts, transfer them to the greater platform and produce the campaign you want.
Many of its users say that that simplicity and straightforward solution to email enrichment captivate them.
3. Mailchimp
You probably have heard of this marketing tool. It is one of the leaders for a reason. If I haven’t mentioned this in my post, it would be a mistake. Why is it good? There is a free package, providing valuable functions, while paid options are to bring even more.
I usedMailchimpfor its easy-to-use tracking and email building. Particularly, it has thedrag-and-drop feature, which can help a lot if you are new to email design.
Simultaneously,Mailchimpcan be handy in segmenting audiences. I had to use it on my first marketing assignments and was very glad it had a drag-and-drop function. Making discount coupons and give away campaigns required much less time, thanks to a large collection of templates.
However, looking back,I can say it has basic analytics and segmentation, while for the advanced ones, the user should pay. Notably, a friend of mine had some issues with the support department and their responses. Bad luck, possibly.
Lastly, integration capabilities with other platforms can significantly add to the user’s experience, though. It will be a great choice if you are supposed to level your email creation before entering a larger market and nurturing more leads.
4. Hubspot
HubSpotis another popular solution that many businesses use.The pros of this email marketing software lie in its universal nature. The particular software offers an all-in-one automation solution for many marketing platforms. However, it as well as a separate email marketing tool that is free.
Similar toMailchimp, it providesassistance in preparing visual materials and producing the body of emails. Some of my colleagues did like the interface and the follow-up sequences upon purchasing via websites. However, as it is a free tool, though, by a recognized company, it has some limitations, while the full version can be costly for small firms.
I would be using it if I have plans of enlarging my business, where email won’t be the crucial part of my marketing activity but add to the social media strategy. At the same time, it would be great if you are trying and experimenting with email marketing or considering unifying all of your channels under one CRM system. Then,Hubspotwill be the perfect solution.
5. Sendinblue
Sendinbluehas made it to this list due to its surprising features, considering the time we live in. Who sends SMS messages today when we have messengers? However,the particular tool does!It as well facilitates email campaigns management, having automation and personalization possibilities.In short, it is excellent for transactional messages sending. I had my team use it for one event project, and it did great.
Simultaneously, the template options are not as advanced as the top marketing email services above provide. Thus, choosing this option would be suitable for those who have their template game on an adequate level. That is one downturn among some other ones.
They refer to a limited free package and multiple logins only under advanced packages.
Still, it is affordable and should be a good choice if it suits your goal and strategy.
6. Sender
In regard to this email marketing software,you may want to use it if you pursue your deliverability improvement.The algorithms behindSenderfocus on tracking delivery rates. At the same time, there is a facilitator for template creation. One can add different visuals that will for sure optimize the engagement rates of the campaign. The service pays attention to details making your email marketing campaign bright.
Still, I heard that they had some lags within their segmentation feature, which the company is likely to have taken care of. Why? Their customer support is friendly and lends a helping hand irrespective of the issue’s complexity, despite thatthe pricing is relatively low.
7. Drip
You may think that this tool can be helpful in drip campaigns. This mailing campaign softwarehas a powerful segmenting focus and synchronizes with many website constructors.
Such a combination makesDripuseful for many entrepreneurs or small business owners that conduct their business online. In addition, they have a bunch of personalization features. That’s why many consider it ideal for firms with small operations in specific niches.
One of the cons is thatit can be a bit pricey. Yet, it offers some educational materials for users. Again, thedata analytics, targeting features, and personalizationwithin this email automation service can become a game-changer for an owner of a small firm.
8. Convertkit
Convertkitis another email marketing tool that is handy in email campaign designation. AsDriporMailchimp,it is excellent for segmenting the audience. However, compared to them, this service offers it throughtagging. Some colleagues of mine have said that it is easier to have different groups and target them by tags at your display, especially if there is only one product of yours.
On the other hand,the particular instrument can be challenging to use at first.You may need some time to comprehend all the functions. This happened to me, and I decided to go for another solution. Still, if you want to enhance your lead generation funnel, this can work.
9. Aweber
Aweberis atraditional and straightforward mailing campaign softwarethat was designed solely for email marketing. It has bothadvanced and drag-and-drop featuresfor template creation. Besides, as it is a long time on the market, it has an extensive knowledge base and support.
Moreover, it has all the standard features referring to personalization, follow-up automation, listing and segmentation. Notably, what is the most important thing is its simplicity.
I believe I have started my email marketing journey with this tool, andfor me, as a newbie in marketing, it was pretty easy to use.That’s why it can be a universal tool for tiny companies who just start selling their product and have not developed large lists yet.
10. Omnisend
Omnisendcan be a great choice if you are developing your business on several channels. Although it has a basic set of features, it has SMS automation features and can work with numerous platforms.
You can have different campaigns, while theOmnisendreporting system will show from where you got the revenue. It is essential for prioritizing the campaigns and offers for the customer groups.
Except for simplicity in management, automation and the beautiful design of templates, it can offer affordable packages.Suppose a person needs something for a small business related to visually pleasing products, like jewellery or craft. In that case, they are likely to benefit from the templates of this email campaign software.
Lastly, if you want something that would better align with other strategies or website designing, another option can be a better solution for you.
Bottom Line
There are many email marketing software, andpicking the right one depends on your goal and your business.You may need an email marketing tool solely for email campaigns or contact research. The best is the one that is the most efficient. I have made this list due to what I experienced and heard from my colleagues.
When choosing the best tool,look at what challenges you have or how a tool can give you an advantage. If the issue refers to contacts extracting, then,Getprospectis a solution. If you have multiple products and many platforms or channels,MailChimporHubspotcan be a pick.
If you need some help with templates, picking an email automation service focusing on their designation would increase your engagement rates. Lastly, if you lack segmenting,DripandConvertkithave efficient mechanisms and reporting to work with contacts’ data.
