On-demand Mobile Apps: Must-have Features and Trends

The success of Uber has changed the approach to business operations in the tertiary sector and inspired hundreds of startups. A smartphone has become a platform for receiving services in a new hassle-free way for users, and the distance between them and the service providers has decreased. The latter also got the possibility to build a robust online presence and connect to the target audience directly. Technology companies are another essential component in this process as they develop customized solutions to cater to consumer needs. Healthcare, education, retail, transportation, beauty, etc. – the list of industries taking advantage of the economy uberization is vast. Even the smallest companies or individuals-freelancers can instantly access the consumer base through third-party apps – deliver home-made food, rent out accommodation, offer childcare, cleaning, consulting services, to name just a few. Besides, clients have a tool to shape the market by assessing the quality of products and services through a rating system.

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All the Skills Require For A Data Scientist

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Markov Decision Processes

The Markov Decision Process (MDP) provides a mathematical framework for solving the RL problem. Almost all RL problems can be modeled as an MDP. MDPs are widely used for solving various optimization problems. In this section, we will understand what an MDP is and how it is used in RL.

To understand an MDP, first, we need to learn about the Markov property and Markov chain.

The Markov property and Markov chain

The Markov property states that the future depends only on the present and not on the past. The Markov chain, also known as the Markov process, consists of a sequence of states that strictly obey the Markov property; that is, the Markov chain is the probabilistic model that solely depends on the current state to predict the next state and not the previous states, that is, the future is conditionally independent of the past.

For example, if we want to predict the weather and we know that the current state is cloudy, we can predict that the next state could be rainy. We concluded that the next state is likely to be rainy only by considering the current state (cloudy) and not the previous states, which might have been sunny, windy, and so on.

However, the Markov property does not hold for all processes. For instance, throwing a dice (the next state) has no dependency on the previous number that showed up on the dice (the current state).

Moving from one state to another is called a transition, and its probability is called a transition probability. We denote the transition probability by  . It indicates the probability of moving from the state s to the next state . Say we have three states (cloudy, rainy, and windy) in our Markov chain. Then we can represent the probability of transitioning from one state to another using a table called a Markov table, as shown in Table 1.1:

Table 1: An example of a Markov table

From Table 1, we can observe that:

• From the state cloudy, we transition to the state rainy with 70% probability and to the state windy with 30% probability.
• From the state rainy, we transition to the same state rainy with 80% probability and to the state cloudy with 20% probability.
• From the state windy, we transition to the state rainy with 100% probability.

We can also represent this transition information of the Markov chain in the form of a state diagram, as shown in Figure 1:

Figure 1: A state diagram of a Markov chain

We can also formulate the transition probabilities into a matrix called the transition matrix, as shown in Figure 2:

Figure 2: A transition matrix

Thus, to conclude, we can say that the Markov chain or Markov process consists of a set of states along with their transition probabilities.

The Markov Reward Process

The Markov Reward Process (MRP) is an extension of the Markov chain with the reward function. That is, we learned that the Markov chain consists of states and a transition probability. The MRP consists of states, a transition probability, and also a reward function.

A reward function tells us the reward we obtain in each state. For instance, based on our previous weather example, the reward function tells us the reward we obtain in the state cloudy, the reward we obtain in the state windy, and so on. The reward function is usually denoted by R(s).

Thus, the MRP consists of states s, a transition probability  , and a reward function R(s).

The Markov Decision Process

The Markov Decision Process (MDP) is an extension of the MRP with actions. That is, we learned that the MRP consists of states, a transition probability, and a reward function. The MDP consists of states, a transition probability, a reward function, and also actions. We learned that the Markov property states that the next state is dependent only on the current state and is not based on the previous state. Is the Markov property applicable to the RL setting? Yes! In the RL environment, the agent makes decisions only based on the current state and not based on the past states. So, we can model an RL environment as an MDP.

Let’s understand this with an example. Given any environment, we can formulate the environment using an MDP. For instance, let’s consider the same grid world environment we learned earlier. Figure 3 shows the grid world environment, and the goal of the agent is to reach state I from state A without visiting the shaded states:

Figure 3: Grid world environment

An agent makes a decision (action) in the environment only based on the current state the agent is in and not based on the past state. So, we can formulate our environment as an MDP. We learned that the MDP consists of states, actions, transition probabilities, and a reward function. Now, let’s learn how this relates to our RL environment:

States – A set of states present in the environment. Thus, in the grid world environment, we have states A to I.

Actions – A set of actions that our agent can perform in each state. An agent performs an action and moves from one state to another. Thus, in the grid world environment, the set of actions is up, down, left, and right.

Transition probability – The transition probability is denoted by . It implies the probability of moving from a state  to the next state  while performing an action . If you observe, in the MRP, the transition probability is just  that is, the probability of going from state  to state  and it doesn’t include actions. But in MDP we include the actions, thus the transition probability is denoted by .

For example, in our grid world environment, say, the transition probability of moving from state A to state B while performing an action right is 100% then it can be expressed as: . We can also view this in the state diagram as shown below:

Figure 4: Transition probability of moving right from A to B

Suppose our agent is in state C and the transition probability of moving from state C to state F while performing the action down is 90%, then it can be expressed as P(F|C, down) = 0.9. We can also view this in the state diagram, as shown in Figure 5:

Figure 5: Transition probability of moving down from C to F

Reward function – The reward function is denoted by . It implies the reward our agent obtains while transitioning from a state  to the state  while performing an action .

Say the reward we obtain while transitioning from state A to state B while performing the action right is -1, then it can be expressed as R(A, right, B) = -1. We can also view this in the state diagram, as shown in Figure 6:

Figure 6: Reward of moving right from A to B

Suppose our agent is in state C and say the reward we obtain while transitioning from state C to state F while performing the action down is +1, then it can be expressed as R(C, down, F) = +1. We can also view this in the state diagram, as shown in Figure 7:

Figure 7: Reward of moving down from C to F

Thus, an RL environment can be represented as an MDP with states, actions, transition probability, and the reward function. Learn more in Deep Reinforcement Learning with Python, Second Edition by Sudharsan Ravichandiran.

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How Agile Methodology Assists the Development of Smart and Working Software Products?

As with any project, an effective and result-driven is a must, the same goes with software development projects. There’s no secret that software development is one of the trickiest and complex tasks that ever existed and to do them right, one needs to be following certain methodologies and processes that make the project go smooth and results in a functional project.

Agile is one of such methodology that is widely adopted by software development companies across the globe. It was formally launched back in 2001 when 17 different IT experts joined forces together to set up a software development method that eliminates all the factors contributing to slowing down a project’s development phase. They came up with four points in their famous Agile Manifesto that were driven to make the software development process fast and result-oriented. The 4 points are:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

Today, every organization seems to have been practicing agile methods in rendering software development services knowingly or unknowingly. No matter if you are new to the software development world or you have learned software development a decade after implementing old development methodologies, one thing is for sure that you are somewhere influenced by the agile software development methodologies.

Before proceeding further, it is important to discuss what the most important roles are in agile methodology. Agile methodologies are all about visioning such plans and models that are made to facilitate users so agile methodology hierarchy starts with keeping users in mind. Let’s get some important roles listed down here.

Important Roles in Agile Process:

1. Consumers:

As earlier said in agile methodology, strategies regarding the design and development of software are made to address users’ ultimate needs. In the modern-day, this is labeled as estimating user personas of how users approach a specific software application.

There are hundreds and thousands of software applications available for users, keeping your business software product competitive in this tech-dominated spectrum is key. And here’s agile methodology comes into play. It leverages the whole development process through the workflows that facilitate the development process in a way that brings productivity.

2. The Head of the Product:

A software product is someone’s idea that was once cooking in the brains. That person who envisions a software product is known as the head of the product. This person knows how things will dish out in the process of getting the desired product developed.

These are the persons who act as vocals for end users. They do establish their connections with both the users and the developers to convey users’ needs from a specific software product to developers. In short, they are the brains behind any software product idea. They present their vision of software in a series of demonstrations and hence the execution goes underway for both the long and short time. Priority is given to the segments that are most important and require greater effort.

Apart from coming up with a vision of a product, these people are responsible to interact with the design and the development teams and supervise the project managers to eliminate any discrepancy in the process. This is by far the most amazing benefit of agile methodology that it gets the whole team on the board and tasks are then assigned to them. When a product head interacts with the development team, he or she makes sure they come up with the stories helping developers to understand the user persona. These stories are short and taken from the real-life experiences of users.

These user stories are prioritized by the product owner, reviewed by the team to ensure they have a shared understanding of what is being asked of them.

3. The Team Responsible for Software Development:

These are the people that are directly responsible to come up with a product that is envisioned by the product head. In an agile environment, the members of the software development work on specified and sometimes non-specified tasks.

Because the goal in front of them is very simple; to deliver a functional product incorporating all the features that are supposed to have in the ideation phase. They use each other’s core specialties where it is necessary. In agile environments, the development teams collaborate in a way that they use each other’s skills in a unidirectional way to get the job done.

In some cases, the developer team is not limited to just software developers. They can bring in QA analysts, project managers, designers, and other business analysts to broaden the scope of the development process.

Why Agile Methodology Works Better Than Other Methodologies?

When you pile up factors such as agile development, quick adoption, flexibility, using collaborative development tools, and the right teams, the desired results can be obtained quickly and productively. The agile methodology is all about adaption and flexibility that adds to the whole development process and refines it.

Agile architecture is better for many challenges because its concepts, frameworks, and procedures are based on the working conditions of today. Agile structures and development processes that give priority to the delivery of working applications and encourage input to enhance application and processes are best suited to the smarter and faster environment of today’s operation.

The Wrap:

Finally, the agile procedures are most suited and widely accepted among the software development teams across the globe. Many developers have their say on agile development as a method that has brought more productivity into their work despite putting unnecessary burdens on them.

If you are an entrepreneur and are looking to get your software product developed in the latest fashion, agile methodology is the way forward for you. Moreover, SoftCircles is one of the top software development companies in New York that strictly follows an agile methodology to bring the best out of a software development project.

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3D imaging Market is Expected to Generate a revenue $ 55.77 Billion by 2027, Despite the Covid-19 Outbreak

The pandemic of COVID-19 has created a negative impact on the global 3D imaging market. The sustainability of the global market is mainly attributed to the advent of novel technological advancements in entertainment, healthcare, consumer electronics, and industrial automation. In addition, the emergence of 4D technology is projected to offer growth opportunities for the global 3D imaging market. Though the industries across the majority of the economies are completely shut-down, several market players are preferring for effective strategies to curb the impact of COVID-19. For instance, in May 2020, Tesco has introduced a 3D imaging system in Ireland to manage customer number and queuing. The technology was first deployed in Tesco’s 60 largest Superstore and Extra outlets to ensure an accurate steady flow of customers throughout the day. These key factors are projected to create lucrative opportunities in the pandemic situation. In addition, due to the crisis of the coronavirus disease, businesses are more concerned with customer optimism, and loyalty. Therefore, the majority of enterprises can go for the adoption of 3D imaging for finding ways to help clients through this severe situation; and this will significantly impact the global market, after the pandemic situation. Our reports include the following:

• Technological Impact
• Social Impact
• Investment Opportunity Analysis
• Pre- & Post-COVID Market Scenario
• Infrastructure Analysis
• Supply Side & Demand Side Impact

According to the latest publication of Research Dive, the global 3D imaging market is set to register a revenue of $55.77 billion by 2027, during the forecast timeframe.

Smartphones Segment shall have the fastest growth During the analysis Period

The segmentation of the market has been done on the basis of product type, image sensor, application, end-use industry, and region. The report offers valuable information on drivers, restraints, vital segments, lucrative opportunities, and global leaders of the market.

Factors Affecting the Growth

As per our analyst estimations, the versatility of 3D imaging in a broad range of industries such as entertainment, healthcare, security & surveillance fuelling the growth of the global 3D imaging market. However, the low product penetration in low and middle-income countries is projected to restrain the growth of the global 3D imaging industry, during the projected period.

Smartphones Segment shall have the fastest growth During the analysis Period

Based on the product type, the global market for 3D imaging is categorized into 3D Cameras, Sonography, Smart Phones, and Others. The smartphones segment is expected to rise at noteworthy CAGR, during the analysis period. The growth of the smartphones segment is because it is the rising popularity of capturing 3D objects and image processing via smartphones.

The Complementary Metal-Oxide Semiconductors will be the Most Lucrative

Based on the image sensor, the global market is fragmented into a charge-coupled device(CCD) and Complementary metal-oxide semiconductors. The complementary metal-oxide semiconductors (CMOS) will generate a remarkable revenue in 2027.

The Layout & Animation will have Rapid Market Growth, during the Forecast Period

Depending on the application, the global 3D imaging market is broadly categorized into 3D modeling, 3D scanning, layout and animation, 3D rendering, image reconstruction. The market size for the layout and animation is expected to rise at a remarkable CAGR by 2027. The huge investment in the R&D of this segment may expected to boost the growth of the market, over the forecast period.

Healthcare sector shall have a Major Market Share in the Forecast Period

on the basis of end-use industry, the global market for 3D imaging is broadly categorized into entertainment, healthcare, architecture & engineering, industrial applications, security & surveillance, and Others. The healthcare segment will held significant market share and is projected to register a remarkable revenue, in the forecast period. The increase in the geriatric population is one the major reason for the rise in the adoption of 3D imaging during the forecast period. 

Geographical Analysis and Major Market Players

Based on the region, the 3D imaging analytics market is segmented into North America, Europe, Asia Pacific, and LAMEA. The Asia-Pacific 3D imaging analytics market will register a significant revenue, over the forecast timeframe. The enormously rising government investments in 3D imaging solutions along with the increasing number of startups, mainly in China India, and South Korea, are expected to bolster the growth of the Asia-Pacific 3D imaging market in the global market.

 The leading players of the global 3D imaging market are

• GENERAL ELECTRIC COMPANY,
• Autodesk Inc.,
• STMicroelectronics,
• Panasonic,
• Lockheed Martin,
• Koninklijke Philips N.V.,
• Trimble Inc.,
• FARO Technologies, Inc.,

About Us:
Research Dive is a market research firm based in Pune, India. Maintaining the integrity and authenticity of the services, the firm provides the services that are solely based on its exclusive data model, compelled by the 360-degree research methodology, which guarantees comprehensive and accurate analysis. With unprecedented access to several paid data resources, team of expert researchers, and strict work ethic, the firm offers insights that are extremely precise and reliable. Scrutinizing relevant news releases, government publications, decades of trade data, and technical & white papers, Research dive deliver the required services to its clients well within the required timeframe. Its expertise is focused on examining niche markets, targeting its major driving factors, and spotting threatening hindrances. Complementarily, it also has a seamless collaboration with the major industry aficionado that further offers its research an edge. https://marketinsightinformation.blogspot.com/

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Homework Assignment: Create a COVID19 At-Risk Score

Figure 1: The Art of Thinking Like a Data Scientist

I love teaching because the onus is on me to clearly and concisely communicate my concepts to my students.  As I told my students, if I am describing a concept and you don’t understand it, then that’s on me and not you.  But I do expect them to be like Tom Hanks in the movie “Big” and raise their hands and (politely) say “I don’t get it.”  If they don’t do that, then I’ll never learn how to improve my ability to convey important data, analytics, transformational and team empowerment concepts.

And that’s exactly what happened as I walked my Menlo College undergrad class through a “Thinking Like a Data Scientist” workshop.  One of steps in the methodology calls out the importance of creating “analytic scores” that managers and front-line employees can use to make informed operational and policy decisions (see Figure 1).

While folks who have credit scores understand the basic concept of how scores are used to augment decision making, if you’re an undergrad student, credit scores may not be something that has popped up on your radar yet. So, in order to make the “Creating Analytic Scores” step of the methodology come to life, I decided that we would do a group exercise on creating a “COVID At-Risk Death” score – a score that measures your relative likelihood of dying if you catch COVID19.

Analytic Scores are a dynamic rating or grade normalized to aid in performance tracking and decision-making.  Scores predict the likelihood of certain actions or outcomes and are typically normalized on a 0 to 100 scale (where 0 = bad outcome and 100 = good outcome).

Note:  Analytic Scores are NOT probabilities.  A score of 90 does not mean that there is a 90% probability of that outcome.  Analytic Scores measure the relative likelihood of an outcome versus a population. Consequently, a score of 90 simply means that one is highly likely to experience that outcome versus others in the population even if the probability of that outcome occurring is, for example, only 2%.

Sample scores can be seen in Figure 2.

Figure 2: Sample Scores by Industry

The true beauty of an Analytic Score is its ability to convert a wide range of variables and metrics, all weighted, valued and correlated differently depending upon what’s being predicted, into a single number that can be used to guide or augment decision-making. FICO (developed by the Fair, Isaac and Company) may be the best example of a score that is used to predict certain behaviors, in this case, the likelihood of an individual borrower to repay a loan or another form of credit. The FICO model ingests a wide range of consumer transactional data to create and update these individualized scores.  Yes, each FICO score is a unique measurement of your predicted ability to repay a loan as compared to the general population (see Figure 3).

Figure 3: Source: “What Is a Credit Score, and What Are the Credit Score Ranges?”

Everyone has an individual credit score. The score isn’t just based upon generalized categories like age, income level, job history, education level or some other descriptive category of data.  Instead, the FICO credit score analyzes a wide variety of transactional data such as payment history, credit utilization, length of credit history, new credit applications, and credit mix across a wide variety of credit vehicles.  From this transactional data, FICO is able to uncover and codify individual behavioral characteristics that are indicative of a person’s ability to repay a loan.

But what makes Analytic Scores particularly powerful is when you integrate the individualized scores into an Analytic Profile.  Analytic Profiles capture and codify the propensities, patterns, trends and relationships (codified by Analytic Scores in many cases) for the organization’s key human and device assets at the individual human or device level (see Figure 4).

Figure 4:  Analytic Profiles: The Key to Data Monetization

Analytic Scores and Analytic Profiles power Nanoeconomics, which is the economic theory of identifying, codifying, and attaining new sources of customer, product, and operational value based upon individual human and device insights (or predictive propensities).  It is Nanoeconomics which enables organizations to transform their economic value curve so that they can “do more with less”, exactly the situation in which the world finds itself in the mass effort to vaccinate people and transform the healthcare industry (see Figure 5).

Figure 5: Transforming the Healthcare Economic Value Curve

My students and I decided to embark on a class exercise to see if we could create a simple score that measured the relative risk of any individual dying from catching COVID19.  We then discussed how we could use this score to prioritize who got COVID19 shots versus the overly-generalized method of prioritization that it being used today. Here is the process that we used in the class. 

Step 1: Identify Variables that Might Predict COVID19 Death.  I asked the class to research three variables that might be indicative or predictive of death if one caught COVID19.  We settled on the following three variables:  Obesity, Age and Gender.  There were others from which to select (e.g., pre-existing conditions, vitamin D exposure, exercise, diet), but I settled on these three because they required different data transformations to be useful.

Step 2:  Find Data Sources and Determine Variable Values.  Our research uncovered data sources that we could use for each of the three variables in Figure 6. 

Figure 6: Step 2: Find Data Sources and Transform into Usable Metrics

To measure Obesity, we used the Body Mass Index (BMI) where we input the individual’s height and weight and then calculate that individuals’ BMI and BMI classification (underweight, normal, overweight and obese).  To measure Age, we used the percentage of deaths by age brackets. And for Gender, we used Male = yes or no.

Step 3:  Calculate “Percentage Increased Risk Adjustment” per Variable.  Next, we adjusted the variables for increased COVID19 risks.  For example, we added a risk adjustment coefficient to BMI categories of overweight (BMI * 2) and Obese (BMI * 6) to reflect that it isn’t a linear increase in risk as one was more overweight.  We did the same thing with age, increasing the risk bias as one got into the older brackets.  Note:  this is an area where we could have used some machine learning algorithms (such as clustering, linear regression, k-nearest neighbor, and Random Forests) to have created more precise Increased Risk adjustments.

Step 4:  Input Relative Variable Importance.  Since not all variables are of equal weight in determining COVID19 At-Risk of Death, we allowed the students to assign relative weights to the different variables.  This provided an excellent opportunity to explain how a neural network could be used to optimize the variable weights, and discussed neural network “learning and adjusting” concepts like Stochastic Gradient Descent and Backpropagation

Step 5:  Normalize Input Variables.  I could have simplified the spreadsheet if I made the students entered percentages that totaled to 100%, but it was just easier to let them enter relative weights (between 1 to 100) and then to have the spreadsheet normalize the input variables to 100%.  Yea, I’m so kind-hearted.

Step 6:  Calculate Population At-risk Scores per Variable.  Next, we calculated a population score (using maximum values) for each of the three variables in order to provide a baseline against which to judge the level of the individual’s at-risk variables.

Step 7:  Calculate Individual At-risk Scores per Variable.  We then calculate the score for each of the three variables for the individual based upon their inputted data:  height, weight, age and gender.

Step 8:  Normalize Individual At-Fisk Scores against Population At-Risk Scores.  Finally, we normalized the individual’s score against the population score to create a single number between 0 to 100 (where a score of 100 is highly at-risk and a score near 0 is very low at-risk).

The process and final spreadsheet is captured in Figure 7.

Figure 7: COVID19 At-Risk of Death Score Process and Spreadsheet

Now that the data and transformations are in the spreadsheet, the students could play with the different transformational variables like increasing the Risk factor for Obesity, the relative weights of the different variables, and even the input variables. And while most of the variables are not adjustable (your height, age and gender are hard to change…but I guess it is possible…), weight was certainly a variable that we could adjust and used this as an opportunity to see its impact on an individual’s at-risk score (yea I know, I need to lose weight…).

Analytic Scores are a powerful yet simple concept for how data science can integrate a wide variety of metrics, transform those metrics into more insightful and predictive metrics, create a weighting method that gives the most relevant weights to the most relevant metrics, and then munges all of those weighted and transformed metrics into a single number that can be used to guide and augment decision making.  Plus, one can iteratively build out the Analytic Score by starting small with some simple metrics and analytics, and then continuously-learn and fine-tune the Analytic Score with new metrics, variables and analytics that might yield better predictors of performance. Very powerful stuff!

One final point about Analytic Scores.  One cannot make critical policy and operational decisions based upon the readings of a single score.  To really leverage Analytic Profiles and Analytic scores to make more informed, granular policy and operational decisions (and activate the power of Nanoeconomics to do more with less), we would want to couple the “COVD19 At-Risk Death Score” with a “COVID19 At-Rick of Contracting Score” that measures the likelihood of someone catching COVID19. Why prioritize someone high based upon the “Death” score if their likelihood of catching COVID19 is low (i.e., live in a remote, sparsely populated location, work from home and are adamant about wearing a high-quality, N95 mask and practicing social distancing when in public).  Heck, one might even want to create a “COVID19 At-Risk of Transmission” to measure someone’s likelihood to transmit the virus.

If you are interested in the resulting spreadsheet, please contact me via LinkedIn and I will send the spreadsheet to you. You brainiacs out there will likely uncover better data sources and better variable transformations that could improve the accuracy of the COVID19 At-Risk spreadsheet.  And if you create a better formula than the one that we created (which won’t be hard), please share the spreadsheet with me so that I can incorporate it into my next class.  Hey, we are learning together!

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Causal AI dictum: A dataset is model-free

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The Role Of IoT and Big Data In Payroll Process For Businesses

Image(source)

“Hey, Siri! How many steps did I walk today?” “Hello, your average step count for the day is 20,000”. Internet of things, popularly termed IoT, has become such an integral part of our lives these days. People and machines these days are secretly linked to one another. We have gotten used to counting on it so much; we never fail to use it at least once every day. Statistics state that 127 devices get connected to the internet every second. Let’s take smartwatch bands, for instance. While some wear it only to look cool, these watches are specially curated to meet health-conscious people’s needs. They allow us to track our blood pressure, heart rate, daily step-count, and calorie count. No matter how much they curse the internet, the idea of living without it has become nearly impossible in this tech-savvy world.

Businesses have realized the awareness of big data and how it will help them nurture their business activities. Organizations use real-time data to predict profits, analyze their commodities and the necessary changes to be made, and improve decision-making. Research confirms that nearly 90% of the overall data stored is generated in the last two years. What was before just a field of IT is now an essential part of every business enterprise.

Let us now understand the meaning of big data and IoT.

What Is Big Data

Big Data can be referred to as the growing number of data, or information, that is being stored on the web, viz, internet. It can store and process such huge chunks of data that it is impossible for manual data processors, i.e., humans, to process it. Companies make use of this feature to store their employee information and other data related to their finances. Organizations use it as a part of their cloud-based payroll solution to make money-related decisions. An example of big data is social networking sites collecting information from comments, pictures, and videos.

What Is IoT

IoT can be defined as all the gadgets around us that are linked with the internet. It is responsible for gathering data and making it available. It works by the sensors that can be added to tools like smartwatches, which will provide fresh and new data without the help of physical labor. IoT is making us digital and smart by mixing technology and humans. Example of this includes refrigerators or air-conditioners connected to our mobile devices through the help of applications.

Benefits Of Using Big Data In Businesses

Big data lends a hand to businesses in several ways. Some of them are-

Helps Retain Talent

Remember how in the old days when manual HR used to struggle to find candidates with the right talent and retain them? Well, now that big data has come to their rescue, it has become a no-brainer for them. Not only will it find, filter, and interview candidates based on the organization’s requirements, but it will also help them hold on to worthy employees. Organizations can collect data from employees regarding their needs, and they can work towards achieving them in order to retain those employees.

Big data also helps companies with finding new talent. Its robust features help organizations become stronger by collecting honest employee reviews, past revenue histories, sales, and profits, and share this information with new hires. This strategy will attract new candidates to the organization and guide the managers in finding them.

Ensures Proper Data Management

Business organizations that are active generate bytes and bytes of information every day. Therefore, it is essential that the sources, i.e., the automated devices they rely on, are safe and ensure regular data storage and management. One of the big data tools includes providing companies a secure space to save and access the information stored. This includes confidential data like payroll information and employee data like attendance regularization, leave and overtime, taxes, and other related terms.

This is a blessing to the management and the employees since it ensures smooth data flow. Management can make quick and fact-based decisions through real-time data, while the workforce can view their salary payslips, therefore increasing their work efficiency.

Finds And Corrects Errors

The days when organizations had to rely on physical processes of handling their monetary issues are long gone. Big data automation provides companies with mistake-free financial statistics. Businesses that rely on manual HR face a problem with it since physical HR doesn’t ensure error-free processing. On the other hand, the operating system makes the department’s work easy and helps companies rely on the information.

It will highlight where the errors are happening, guide employees with correcting them, and ensure such mistakes don’t happen again. This builds the roots of the financial management of the organization strong. It will sharpen the staff’s payroll skills and increase productivity among employees because ensuring zero errors would mean them getting paid fairly.

Aids In Decision-making

The role big data plays in the decision-making of the organization is utterly crucial. It has the capability to dig deeper than any physical labor and produce data that is impossible to process. It provides organizations with necessary information at godspeed. This is what helps companies in making quick, real-time decisions.

Businesses can also make proper use of this feature to prepare for any issues they might face since the data will show them where they are headed and prepare them for the same. It will alert the organizations of any potholes that come in the way they are headed and provide them with alternative solutions.

Enhancing Employees’ Development

It is essential that organizations help their employees enhance their experience within the company. In order to do that, they must develop their job by propelling them into training and development. This is vital for both enterprises, and the workforce since following the same schedule will lead to them losing interest in what they do, leading to less productivity. Therefore, to keep it stagnant, they must take this step.

Big data uses employee information to provide solutions to companies regarding their development from which they can plan out the training procedures for every individual employee based on how they want to evolve.

Benefits Of Using IoT In Businesses

Following is how beneficial IoT is to business organizations-

Collects Real-time Data

Collecting data all the time is essential for the management since it will lead to the development of the organization. They can get hold of the necessary information with the help of collaborative tools like Hangouts, Email, etc. Doing this will help the top management in making crucial decisions and lead to the business’ development.

IoT gadgets like CCTV cameras, PCs linked to the organization’s server host, etc., can aid the HR department with the same. These tracking tools detect the called for data and send it to the managers who are in need of it.

Tracks Employees’ Work

With the latest technology tools, tracking and managing employees’ work has become a cakewalk for organizations. They can track employees and identify the factors that might distract them from working. Since all the work employees do has now shifted virtually and businesses have started going green considering the current environmental situation, work tracking has become much more manageable. Organizations can know how much work each employee does every day with such tools’ help.

For example, by adding a sensory device on a content writer’s keyboard, the company can know exactly how many words he writes every day. This reduces the chances of them stealing from the company. This helps the managers place the right employees in the position(s) that fits them best.

Automates Monetary Process

IoT helps A LOT with processing the organization’s finances. Tracking tools like GPS can lend a hand with managers getting information about the employees’ absent days and their leaves. The location sensor will provide precise data and track the number of hours the employee has worked. This helps the managers in giving salaries to workers based on the same. With tools like a strong calculating processor that ensures zero errors, it lends great help to organizations to process employees’ salaries.

Looks Out For The Workforce

It is obvious that a fit and fine worker will contribute more and give more money to the organization than an ill one. Organizations need to ensure that the health of their employees is retained and they don’t face any health issues or stress at work. They can monitor this by giving them smartwatches and track their overall health. These wearables can track their efficiency and help the businesses point out the issues faced and provide solutions to fight them.

Final Note

Modern tools like big data and IoT have entirely changed the way businesses function these days. Organizations can make use of these instruments to develop the business and help their employees evolve with it.

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DSC Weekly Digest 22 March 2021

One of the first jobs that I had after college (in the midst of a recession) was working as a typesetter for a small company in Florida in the latter 1980s. Having spent almost every waking hour on computers during school, this was hardly the kind of job where you’d expect there to be an issue about job security. Working on the then-current Linotronic hardware, my job was to markup text to be formatted on film using computer codes, which gave me an early insight into work I’d be doing a decade later with XML.

Things went swimmingly for the first six months or so, until a small company called Aldus, out of San Jose, California released a program called Pagemaker for the new Macintosh computer. For many companies, Pagemaker was a game-changer, making it possible to create professional-quality content visually in real-time. For our small typesetting firm, it was the End Times. The company went from having a revenue of $10 million when I started to less than $150,000 when I was finally laid off with the rest of the staff a year later. For me, it provided a window into understanding how quickly technology can completely rewrite the landscape.

The field of data science has changed dramatically since DSC first began in 2012. The niches that opened up after those first few years have largely been filled, and competition for baseline data science jobs has increased even as salaries have dropped. Knowing what a Bayesian is or how to correct for skewed distributions is no longer enough, and in many cases, work that used to require R or Python working in an IDE has now become integrated into mainstream BI tools, available at the click of a button.

That does not mean that there are no data science positions out there, only that many of them are increasingly specialized. In essence, the future of the data scientist is where it’s always been, as a subject matter expert who has the knowledge and experience to use the tools of data science, machine learning, and AI in order to better understand and interpret their own domain. This shouldn’t be surprising, but for all too many people who want to be data scientists first, I’d make the case that they should look upon data science as a toolset, a set of skills that all researchers and analysts should have.

This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.

In media res,
Kurt Cagle
Community Editor,
Data Science Central

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R vs. Python vs. Julia: How easy it is to write efficient code?

julia> 10 ∈ [71,38,10,65,38]
true

julia> 20 ∈ [71,38,10,65,38]
false

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