Contact Us
×

Category: Analytics

How Data Visualization Helps Data Analysts Make Effective Decisions Faster

Posted on by Imarticus Learning

In most organizations, the challenge is not the lack of data, but the excess of data. Companies have such a large quantity of data that it is difficult for them to organize and use it as a tool to prioritize activities. A large amount of data is often spread across different departments, including marketing, and sales.

Proper data visualization through algorithms can help a company reason through chaos. It can filter the data and digest it in a way that detects the right prospect at the right time. It also provides the rich context needed to greatly improve the efficiency of a company or a segment of it, like for example the sales team. 

Data Analysis for Effective Decision Making

Data visualization helps data analysts give an explanation and rationale as to why a recommendation is made, as well as contextual data from relevant applications, such as customer relationship management, which allows an individual to act on the recommendation more intelligently and effectively. 

Intelligent data analysis throughout proper data visualization allows the breakdown of information based on specific criteria. When a proper data analysis is carried out, not only is time saved but also, companies are able to support their decisions on facts and can be confident that they are making the most effective strategy.

Role of data analysts

Data analysts are in charge of examining a set of data with the objective of deriving conclusions in order to make decisions or simply to expand knowledge on a specific topic for which they contain information. Almost any industry or company can profit from proper data analysis if they have the required means of data visualization.

Data analytics is a key tool that helps recommend products, services, actions, or decisions that address a specific demand. Data analysts also extract value from data thanks to visualization tools. Today many universities worldwide offer data analytics courses and data visualization courses that help people become experts in delivering commercial strategies for many industries.

Why Imarticus for data analysis online course?

Becoming a data analyst will open many doors to an enormous number of possibilities in many industries. It will give you professional analyst skills, new ways to make key data-driven decisions on aspects like how, when, or where to deploy resources, or how to engage prospects and leads more effectively.

You will become of value to the industry, as you will be key in eliminating their reliance on guessing outcomes and relying on gut instincts to make critical decisions. You can subscribe to a data analytics course offered by us at Imarticus and become a well-profiled professional in this new and demanded field!  

best data analytics certification courses in IndiaAt Imarticus we offer a Program in Data Analytics and Machine Learning that has been designed for fresh graduates and early career professionals that are seeking to pursue a career in Data visualization and Analytics.

This industry-designed curriculum is offered in partnership with many industry leaders, which will be key in providing you with real-life case studies, via our data analytics courses, that will train you for the real world during your formative years. Once you have received your data analytics certification, we will guide you through interview opportunities. Come and join our Imarticus team today! 

Conclusion

The value of data visualization for data analysts is increasingly obvious when compared to the past when there were no systematic methods to determine the next action on any given customer or even of a company. Thanks to professionals with data analytics certification, today companies are able to gauge the true impact of the data, by tracking metrics against previous practices and environment, in order to make effective decisions faster.

Transforming healthcare with operations research in Supply Chain management

Posted on by Imarticus Learning

The Supply Chain Management course is a graduate-level program that explores the systems and processes needed for managing product flow from manufacturer to customer. It is an essential component of any company’s operation as it helps them identify, monitor, and manage their inventory across all stages of production.

It also identifies potential areas for improvement in order to optimize operations and generate more revenue. Supply Chain Management has many different applications including healthcare, which we will explore in this blog post.

What is Operations Research in Supply Chain Management?

Operation research is a method for improving decision-making and planning through the development of models that describe complex problems and help to identify the most effective solution. Supply chain management is a sub-field of operations research that deals with managing inventory, information, people, time, space or money as factors in complex supply chains involving many organizations collaborating together. Also, Supply chain management and operations research make a powerful pair, especially when it comes to healthcare.

How Operation research in Supply Chain Management is transforming Healthcare?

Healthcare is one of the most complex industries with many moving parts that need to be managed together seamlessly. SCM offers an approach for improving quality while reducing costs by identifying areas for improvement such as:

  • Supply chain visibility: It is challenging to identify the root causes of supply problems and manage inventory effectively when there is a lack of visibility.
  • Supply chain management helps healthcare providers make better-informed decisions by having real-time, accurate information about their product flow and distribution.
  • Reducing costs: Supply Chain Management can help reduce healthcare spending through improved planning for equipment maintenance, more efficient use of resources and more accurate forecasting.
  • Improving patient care: Supply Chain Management’s central theme of collaboration can help healthcare providers reduce wasteImproved productivity and process efficiency. by identifying areas for improvement in how they work together, share technology, or even practice medicine.
  • Increasing transparency: Supply chain management will help to improve both the quality and delivery of care. Supply chain visibility creates a shared understanding across healthcare stakeholders, such as patients, doctors, payers, or manufacturers.
  • Reduces errors: Supply Chain Management can increase the number of people who receive critical treatments quickly by reducing errors in inventory management and product distribution which reduces wait times.
  • Supply chain optimization for ancillary services like lab tests, diagnostics, pharmaceuticals etc.

Explore Supply Chain management courses with Imarticus Learning.

Imarticus Learning offers Supply Chain Management courses to help you discover the power of Supply Chain Management. Supply chain management is best suited for Supply Chain professionals who want to move up the Supply Chain career ladder.

Imarticus Learning’s Supply Chain certification prepares you for a comprehensive understanding of Supply chain management concepts so that they can be applied in your work environment with ease. Imarticus also brings you instructor-led training from experienced industry experts and certified practitioners, helping you develop Supply Chain Management skills that are in demand.

Course USP’s:

  • Takes your career goals to the next level.
  • Best suited for Supply Chain professionals.
  • Learn from industry experts and take your skills to the next level with Supply chain management online courses developed by expert Supply Chain practitioners & trainers, certified consultants, and leading academics of top universities in India and abroad.

How Data Science is Making Personalization of Customers Feasible?

Posted on by Imarticus Learning

How Data Science is Making Personalization of Customers Feasible?

Data science opens the door to an enormous number of possibilities in customer experience management. It plays an increasingly important role in all areas of the customer relationship management lifecycle, but countless companies have yet to make this advanced technology part of their marketing tools.

One of the main reasons is the lack of full visibility of what can help them engage better with customers and the inability to quantify potential improvements. Nowadays, with the amount of information available to both consumers and businesses, the key to success is knowing how to offer personalized offers that appeal to each consumer. 

Data Science for the Hypersonalization of Customers

To better understand how data science can make sales and marketing actions more effective, it helps to think about one of the main responsibilities of these groups: acquiring new customers. To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help a company increase sales by effectively identifying those who represent real business opportunities. 

Intelligent data analysis allows the segmentation of leads based on their specific criteria, such as needs, purchasing power, geographic location, and other exclusionary criteria. In this way, it is possible to optimize prospecting efforts, allowing companies to increase their closing rates and, ultimately, business profitability. 

Role of Data Science

Data science extracts value from data through the combination of multiple fields, such as statistics, artificial intelligence, and data analytics. Data science involves the preparation of data for analysis, including steps such as data gathering, scrubbing, presentation, and manipulation. Data scientists can pursuit analytical operations and are able to review results to reveal patterns and enable businesses from different fields to gain informed insights.

To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help you increase sales by effectively identifying those who represent real business opportunities. Today, more and more people are opting for a Data Scientist Career, as it is in increasing demand in many industries.

Why Imarticus for data science online course?

Not only is data science being key for market forecasting and finding good investment opportunities but also for smart marketing. As competition in the market increases, it is becoming more and more necessary to shape the business according to the demands of end-users. Data science makes it possible to offer products/services that address the needs of each user. 

Here at Imarticus, we offer an industry-designed curriculum on DSP Data Science Prodegree. In partnership with many industry leaders, we will introduce you to real business projects and case studies, throughout high-quality tech-enabled education. With one of our courses at Imarticus, not only will you learn data science, but also, we will provide you full placement upon completion of the program.

Conclusion

Data science opens a door to an enormous number of possibilities in customer experience management. It gives sales and marketing professionals a new way to make key data-driven decisions on how to deploy resources and engage prospects and leads more effectively, eliminating the reliance on guessing answers and relying on gut instincts in making critical decisions. You can subscribe to a data analytics course in India offered by Imarticus and become a well-profiled professional in this field! 

Here’s how you can improve customer service for mid-market and enterprise businesses with artificial intelligence and machine learning

Posted on by Imarticus Learning

Customers are the reason for businesses to drive! Whether it’s a small, medium, or an established business, it holds equal importance for all. A customer would like to take services from a company that provides easy access to the platform, understand their needs, is quickly responsive, and resolve the queries optimally.

Technology has a way of making life easier. This is especially true for businesses, which can save time and money by utilizing machine learning and artificial intelligence to analyze customer data to provide better service.

In this blog, we’ll understand a few important ways businesses use AI and Machine Learning for improved customer service. So, let’s get started:

How AI and Machine Learning Contributes to Enhance Customer Service

AI and Machine learning is used in the following areas to enrich customer experience.

  • Customer service interaction
  • Enhancing returns
  • Troubleshooting problems
  • Uses of Website
  • Messaging
  • Customized offerings

Following are the ways used to enhance customers’ experience using AI and machine learning.

1. Chatbot – For Faster and Efficient Assistance: 

The chatbot is one of the most used AI applications by business enterprises. It understands human communication in an accurate context and provides relevant answers to the questions. With the chatbot, you can avail the following benefits:

  • Help businesses to gain insights about user’s requirement
  • Reduces customer’s wait time and get them where they want to be quicker
  • Available for 24 by 7 and provide basic customer support

2. Eliminate Language Barrier to Improve Customer Engagement 

With AI, an enterprise can overcome barriers to doing business with an audience that speaks different languages. Language analysis tool enables office associates to extract main information from the customer feedback and, based on that, adapt their communication.

Language analysis is an important asset to improve the call center experience. With it, the executive can detect if the customer they’re talking to is happy or unhappy and adjust their tone accordingly.

3. Machine Learning Algorithm – Better Understanding of Customer’s Need 

Machine learning offers businesses to get to know more about their customers. Relying on a machine learning solution helps businesses organize daily support requests, answer common inquiries, completely understand a customer’s requirement, and provide a faster solution.

4. Predictive Analytics

Analytics refers to the effort to analyze the data, and it’s crucial for marketing a product. Predictive analytics, with the help of AI tool, analyses past data and predict future outcomes.

 Take Your Career to Next Level with Imarticus Learning 

Imarticus Learning offers the certification in Artificial Intelligence course that the industry’s best leaders have designed to provide a quality learning experience.

The artificial intelligence course will take 9 months to give you a holistic learning experience. Choosing this course will unlock the lucrative creative opportunities in the coveted field of AI.

Our Artificial Intelligence and Machine Learning Course USPs:

  • Master the skills of Machine Learning and Artificial Intelligence through the most relevant curriculum designed by E&ICT Academy, IIT Guwahati, and leaders from the industry.
  • An opportunity to get educated about what new-age AI & ML engineers do by solving real-time problems in their job. Engage in a world-class education program while mastering practical application.

Here’s what you need to know about Digital Twin in Supply Chain Management

Posted on by Imarticus Learning

Digital Twin is a new concept in supply chain management that has gained popularity over the past few years. Digital Twin technology creates a virtual model of a physical product by digitizing its design, manufacture, and distribution process. This allows businesses to collect data about their products and use it for predictive analytics to improve efficiency, reduce costs, and cut down on waste. Read this blog post to learn more about Digital Twin basics before you start Designing a Supply chain network!

What is a digital twin, and how does it work?

A digital twin is a virtual replica of an existing physical entity, such as a machine or product. It can be used to predict changes that will occur in the future and also help with decision-making processes by providing insights into how those changes will impact performance metrics like supply chain cost per unit shipped, customer satisfaction ratings, etc.

The digital twin works by taking measurements from the physical entity and generating a virtual model. The data is collected via sensors installed on or around the object being monitored to record changes over time. These sensors collect information like temperature fluctuations, system efficiency levels, amounts of wear-and-tear damage done throughout its lifespan, etc. This can be used to produce insights into how that change will affect performance metrics later on down the line. This process enables better decision making about product design and life cycle management for companies looking at long term results instead of short term gains.

Benefits of using a digital twin in supply chain management include:

– Improved decision making by using data from the digital twin to inform decisions about product design, life cycle management and service level agreements.

– Reduced time required for new product launch as a result of faster prototyping with the use of virtual models that can be tested without having actually to build physical prototypes first. This means more efficient workflows are freeing up resources for other projects instead of spending time building components only to find out they don’t meet specifications. In addition, working off a virtual model reduces waste since prototype iterations are not needed before finalizing designs/products, which would have been scrapped otherwise due to being non-compliant or unusable in some way.

– Increased reliability via early fault detection resulting from integrated sensors on products connected through IoT devices, which in turn improves serviceability by allowing problems to be detected sooner.

– Reduced risk of disruption to operations caused by accident or system failure because the digital twin can provide real-time data about how changes will impact performance metrics like availability and reliability before they happen.

Learn Digital Twin with Imarticus Learning

Aspirants can quickly achieve the opportunity to work at most global digital twin courses with placement assistance. Students can get full career help to begin their digital twin courses with 8000+ placements accomplished with 60 per cent compensation increases.

Some course USP:

  • The digital twin courses with placement assurance aid the students to learn job-relevant skills that prepare them for an exciting career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

The Changing Face of the Retail Industry with the Emergence of Data Analytics

Posted on by Imarticus Learning

The introduction of new technologies like data analytics has revolutionized the way we think about retail. Even the figure of the retail professional is changing and evolving. Companies are in a phase of change and are looking for new professionals who understand the difficulties, issues, and challenges of the sector.

Read on if you want to know how data analytics drives the retail business, and to find out more about the roles of data science and retail banking in this industry.

Data Analytics in the Retail Industry 

Today, companies operating in the retail sector leverage the power of data analytics more than anything to ensure business continuity and growth. Retail employees have traditionally had relatively little training in their area of work. This trend is changing and must change if retailers are to improve the shopping experience and be able to adapt to new customer demands.

In today’s world, customers are becoming more and more dependent on e-commerce and no longer depend on going to a store to get information and rely on what the salesperson tells them; rather, customers rely today on store personnel to get information or resolve doubts that they themselves have not been able to find or resolve online. This requires greater professionalization of employees to meet the customer’s demands at the point of sale.

Role of Data Analysts

Data analysis is the science of examining a set of data for the purpose of drawing conclusions about the information in order to make decisions or simply to expand knowledge on various topics, it is an indispensable tool for market forecasting and identifying good investment opportunities.

Many industries, like investment banks and retailers, are already using data analytics. With increasing competition in these markets, businesses are being shaped according to the demands of end-users. Data analytics is a key tool in helping them offer products and/or services that address these demands. 

Data Analytics for the Retail Industry

The retail sector is therefore increasingly demanding professionals with data analytics certification and marketing expertise, as analytical and creative skills are positively valued to find solutions in a changing environment. 

Many aspects of this type of company, from distribution to warehouse logistics, are changing and continue to change drastically in the coming years. Stores are and will be an important factor in a retailer’s sales, as the physical point of sale allows interaction with the customer that is impossible for now in online commerce.

Online sales are going to coexist with physical stores and therefore, new professionals with expertise in the omnichannel world who can relate to both worlds are required. Therefore, having trained staff capable of analyzing data, identifying weaknesses and strengths, and implementing the necessary changes in time will be indispensable for the retail industry to survive the technological revolution. 

Individuals with business analytics skills are being highly valued in these industries. At Imarticus, you can access data analytics courses online to learn how data analytics affects the retail industry. 

Why Imarticus for Data Analytics Online Course?

At Imarticus we offer a PGA Program in Data Analytics and Machine Learning design specifically for fresh graduates and early career professionals that want to pursue a career in Data Science and Analytics. We offer this industry-designed curriculum in partnership with many industry leaders.

During your formative years, we will provide you with real-life case studies via its data analytics courses that will train you for the real world. On completion of the data analytics program, our Imarticus team will guarantee you interview opportunities. Enroll today and begin our data analytics program!

With rampant use of artificial intelligence and machine learning, how are financial institutions dealiing with problems related to data bias and transparency?

Posted on by Imarticus Learning

The public and private sectors are increasingly turning to machine learning (ML) algorithms and artificial intelligence (AI) systems to automate every decision-making process, and financial institutions are no exception.

In addition to widespread use in the capital markets, artificial intelligence and machine learning are used in financial services to make insurance decisions, monitor user behavior, recruitments, fraud detection, credit referencing, and underwriting loans.

However, while AI and ML have brought innumerable benefits to financial institutions, they also have their share of woes in the form of data biases and transparency issues. The question is, how are financial institutions dealing with these problems?

Bias and Transparency in the AI Context

AI systems are powered by algorithms that “train” by reviewing massive datasets to ultimately identify patterns and make decisions based on the observations. Hence, these systems are no better than the fed data, resulting in unconscious data biases.

On the contrary, transparency in the context of AI refers to the ability to explain AI-based decisions. Given the increasingly complex findings and algorithms, ensuring transparency to different stakeholders is vital in the financial sector, both from compliance and business value perspectives.

Biases can occur in many ways. For example, bias due to incomplete data occurs when the AI system has been trained on data that is not representative of the population.

Likewise, the dataset could be biased towards previous decision-making processes, the programmer may introduce their own bias into codes, or business policies pertaining to AI decisions could be biased themselves. The bias of any form eventually leads to unfairness and inequities in financial services.

Dealing With AI Bias and Transparency

Although the use of AI and ML give rise to data bias and transparency issues, they have become indispensable for the functioning of financial services. So, the only course of action left to financial institutions is to adopt ways to get around the problems. Some of them are listed below:

  • Financial institutions and firms can have appropriate controls and monitoring tools to ensure that new data entering the pool is reliable and of high quality. 
  • In addition, some organizations have developed tools to determine if a potential AI solution is biased. 
  • When building AI systems, it is wise to gather a team with domain expertise, model development skills, data engineering capabilities, and commercial expertise. 
  • Organizations can undertake impact assessments of the AI solutions to ensure they are transparent and explainable, as well as determine how the AI-based decision-making process will impact customers. 
  • When engaging with AI technologies, financial services can apply safeguards to ensure that business outcomes are achieved, and customers’ interests are protected. 
  • Another way to minimize data biases is to be open on the user data, match and align data with the target segment, and set up review cycles with legal and statistical experts. 
  • Tracking mechanisms that allow one to track the decision-making mechanism of algorithms can be put in place to eliminate bias and ensure transparency as much as possible. 
  • Lastly, it is pertinent for institutions to document their approach to handling bias and review it after every stage of development and use of the algorithm.

What to Look for in an Artificial Intelligence Course?

If you want to learn AI and ML, there are several online courses you can choose from. An AI and ML certification course that makes you future-ready will have a robust curriculum covering critical concepts related to data science, machine learning, NLP, deep learning, and computer vision.

In addition, the program should offer in-depth experiential learning through hands-on involvement with real-world projects.

Tools Data Scientists Use to Make Precise Predictions

Posted on by Imarticus Learning

It is no secret that the accuracy of predictions in the business world can make or break a company. Data scientists create these accurate predictions to help businesses understand what will happen and prepare for it. It’s not easy, but data science has many tools that can make this process easier. In this blog post, we’ll explore some of those tools and how they work!

Tools data scientists use to make precise predictions:

Predictive analytics algorithms help data scientists predict future events and behaviors by using existing data. These tools build mathematical models that capture the connection between demographics, location, time of day, etc., and measurements such as the number of web visits or revenue.

One type of algorithm is a decision tree, a set of rules used to classify things. For example, if the weather is sunny and warm, there’s an 80 percent chance it will be hot outside. Still, if the weather is rainy or cool, there’s only a 30 percent chance it will be hot outside. A data scientist can use this information to determine an appropriate temperature for an office during a particular weather pattern.

Another type of algorithm is a random forest based on the same idea as decision trees but performs better in some cases. Random forests use when data scientists want to make accurate predictions with many different variables. The randomized process behind the tool helps ensure that each prediction is different from the last one.

Artificial neural networks (ANNs) are machine learning algorithms inspired by the neurons in our brains. They let computers complete tasks like recognizing images, handwriting recognition, and other forms of pattern recognition that machines can use to make predictions.

Support vector machines (SVMs) are another machine learning algorithm. These designs are for computer vision, which is the science of how computers can detect, receive, and process images. In a support vector machine model, there’s one variable being predicted from many different inputs. The goal of SVMs is to find a hyperplane that best separates the input data into two distinct sets.

Decision trees, random forests, ANNs, and SVMs are examples of algorithms that can make accurate predictions. These tools work well with large datasets; however, they require careful preparation and data feeding (known as “feature engineering”).

Explore and learn Data Science with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. Learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization using Tableau. This PG program is for industry professionals to help students master real-world Data Science applications from the ground up. Therefore construct strong models to provide meaningful business insights and forecasts.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Wearable scanners boosting productivity in supply chain operations is the future

Posted on by Imarticus Learning

Did you know that warehouse wearable technology has created a paradigm shift in how supply chain operations function? So, before you set sail on a supply chain management career, here’s a snapshot of how wearables in the warehouse are the future.

Wearables and accompanying apps have been in use for quite some time in warehouse operations. These are devices that are connected to the internet and worn on the head, face, wrist, or other body parts for hands-free communication. They are pretty much like activity and fitness trackers, but in the case of warehouse wearables, the technology helps workers perform supply chain tasks without having any additional mobile device in tow. 

Why Wearable Technology In the Supply Chain Is the Future

Wearable tools in supply chain operations include devices for hands-free scan verification, picking, and sorting. These devices have been improving warehouse productivity for decades and are here to stay for the foreseeable future. 

But what exactly are the benefits they confer on supply chain operations? Let’s find out.

Increased Productivity

Using wearable devices and freeing up employees’ hands dramatically enhances the speed of packing, picking, and shipping operations. Moreover, there is a significant increase in productivity when pickers can scan barcodes, receive instructions, and update inventory information without using a conventional handheld scanner or pen and paper.

Improved Accuracy

Wearable devices like scanners can reduce errors significantly since they eliminate the need for manual data entry. Furthermore, voice-directed wearable devices use voice commands over a headset to instruct employees about picking locations, SKU information, etc. These, along with hands-free barcode scanners and other automatic identification technology improve accuracy and increase productivity.

Identification of Inefficiencies

Awareness of the inefficiencies in the supply chain operations is as important as making the process more efficient. Identifying the obstacles that slow down processes make it easier to eliminate the hurdles. Wearable technology logs valuable information that can be helpful to identify inefficiencies and devise solutions. 

Improved Safety and Ergonomic Benefits

Most often, warehouse employees perform repetitive tasks that can cause injury and fatigue. Hence, warehouse wearables are designed to minimize the number of workplace injuries. For instance, these devices can monitor employees’ location, physical activity, and health, and if any unsafe activity or posture is detected, they provide real-time feedback to the wearer.

Enhanced Speed

Last but not least, the increased awareness that hands-free devices offer allows employees to complete their jobs more quickly. Using mobile computers while doing supply chain tasks is inconvenient, takes up time, and slows down the workflow. But wearables ensure there are minimum disruptions in the workflow, which, in turn, increases the speed of output. Thus, employees can cut down on the extra time wasted. 

Wearable solutions have made life in the warehouse much easier. Moreover, the evolution of machine learning and artificial intelligence has generated further value in the warehouse through robotics, computer vision, natural language processing, and similar sub-technologies. Overall, wearable solutions have transformed supply chain operations by boosting employee productivity and improving efficiency.

What To Look For In A Supply Chain Management Course?

Looking for a supply chain management online course? Well, there are several online courses that cater to the needs of professionals aspiring to make a career in the field of supply chain management. But before you settle for an SCM course, ensure that it has a robust curriculum incorporating key programming languages and tools like Python and Excel and hands-on learning with real-world projects.

How are Business Risks Predicted using Logistic Regression?

Posted on by Imarticus Learning

Logistic regression is a mathematical technique that estimates the probability of an event occurring. Using historical data to create a predictive model, you can use regression to predict business, investment, operational, and strategic risks. By understanding how these risks get indicated, you can better assess your company’s vulnerabilities and protect them from future losses.

This blog post will provide examples of how you might use regression in your workplace and explain what this technique does in more detail.

Why is Logistic Regression critical?

It is a statistical technique that tries to understand how the probability of an event occurring changes when one or more variables get altered. The method builds predictive models using data about previous incidents to use for proactively predicting future events. For instance, you could use regression to guess which customers are most likely to stop using your products and services.

Logistic regression can use to predict business risks in many ways, including:

  • Identifying the likelihood of a bad debt written off.
  • Assessing the probability that an IT system will cause downtime.
  • Estimating the risk that a new product or service will flop.

For example, suppose you are assessing the risk that a customer will default on their repayments. In that case, your model might include variables such as the loan amount and the borrower’s age. If you are trying to assess IT downtime risk, some variables might be how old a system is and its many users.

  • Assessing internal risk levels by quantifying how much staff turnover there has been over the past year. By using information about the average time, it takes for employees to complete their tasks.

For example, suppose you are trying to determine which product is most profitable. If you are trying to assess how quickly tasks are completed, some variables might be how long a study takes to complete and how many times it has met before.

  • You can use it to quantify the risk that you will not receive payment for goods or services supplied.
  • Assessing the likelihood of a customer is likely to leave your company’s favor based on variables. Such as their tenure, monthly spending, and how many requests they have made for support.
  • Predicting the probability of a new product being successful.
  • It determines the likelihood of a new employee bringing in a valuable new business.

Explore and learn with Imarticus Learning

This PG program is for industry professionals to help students master real-world applications from the ground up. Therefore students can construct strong models to provide meaningful business insights and forecasts.

This program is for recent graduates and early-career professionals who want to further their careers in Analytics, the most in-demand job skill. With this program’s job assurance guarantee, students may take a significant step forward in their careers.

Some course USP:

  • Risk management courses aid the students in learning job-relevant skills that prepare them for an exciting financial market career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.