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Category: Management

Big Data Engineer Salary: How Much Can You Earn as a Big Data Engineer?

Posted on by Imarticus Learning

Who is a Data Engineer?

As businesses across the globe are enthusiastically adapting the data-driven strategies to optimize their decisions, the demand of highly skilled Data Engineers has increased manifold. A skilled person who is able to convert the raw data into a self-explanatory form to analyze the trends by developing requisite algorithms is a Data Engineer.

The entire task of Data Mining, maintaining and extracting trends from different data sets in an organization is completed by a team of Data Engineers. Ultimately, the Data Engineers provide reliable infrastructure to maintain big data.

Skills required to be a Data Engineer

A Data Engineer must have deep understanding of SQL, Extract Transform Load, Apache Hadoop, in depth knowledge of Python, Java, Scala, Kafka, hive, storm and many more.

Big Data EngineerEnterprises now a days prefer the employees with the experience of working on the cloud platforms like Amazon Web Services etc. Sound knowledge of Data warehousing and Data modelling is also given a lot of preference these days.

The required skills and preferences may affect the salary of an Data Engineer by 10%-15%.

A Data Engineer deals in Big Data, the person should be proficient in the documentation skills and must also be good in his/her verbal and Non-verbal communication skills.

How to Become a Data Engineer?

Applied Mathematicians, Engineers, People holding Bachelor’s degree in Computer Sciences or related IT field find it easier to become a Data Engineer. The aspiring candidates then go for a Big Data certification course to have in depth understanding of required technological skills to be a Data Engineer.

Roles and Responsibilities of a Data Engineer

The generic tasks that a Data Engineer has to perform include:

  • Aggregation and Analysis of given data sets
  • Development of Dashboards and reports
  • Development of tools for business professionals
  • Providing improved techniques to access the Big Data

Three main domains in which a Data Engineer works are: Generalist, Pipeline centric, Database-Centric Generalists are the Data Engineers who processes, manages and analyses the data.

Big Data EngineerPipe-line centric Data Engineers work in coherence with Data Scientists to utilize their collected Data. Database-centric Data Engineers manages the Data-flow and database analytics.

Along with the technical skills, a Data Engineers must have some soft skills as well to communicate their analysis. Some of the key responsibilities are:

  • Acquisition of Data
  • To match their development constantly with the business requirements
  • Consistent improvement in the data reliability, efficiency and Data Quality
  • Development of predictive and prescriptive modelling

The key responsibilities vary from organization to organization.

Data Engineer: Employers and Salaries

Some of the top companies where Data Engineers are highly paid are:

  • com Inc
  • Tata Consultancy Services Limited
  • IBM Private Limited
  • General Electric (GE) Co
  • Hewlett-Packard
  • Facebook

Factors affecting Salaries of Data Engineers 

Experience:

Average Experience as a Data Engineer Average Pay-Scale based only on Experience
Entry level ₹400,000 approx.
1-4 years ₹739,916 based on 317 salaries
5-9 years ₹1,227,921 based on 179 salaries
10-19 years ₹1,525,827 based on 49 salaries

Job Location:

The Data Engineers working in the prime locations like Gurgaon (Haryana) earns 27.3% more average salary, in Hyderabad (Andhra Pradesh) 13.7% more average salary, in Bangalore (Karnataka) 12.5% more average salary than in locations across the nation.

The average salary of a Data Engineer in Mumbai, New Delhi and Chennai are relatively lesser than average salary across the nation.

What Is Distributed Computing Training in Machine Learning?

Posted on by Imarticus Learning

The traditional machine learning approaches rely on using open-source tools for data analysis and prediction making. This approach does not work out well when the data is large. The RAM on the system gets damaged when large files like these are involved. We need to use an approach that not only helps us build the machine learning models successful but also ensures that the system is not burdened or damaged while an operation is being performed. Hence, we need to learn Distributed Computing in Machine Learning.

What is distributed computing?

An approach to improve the system performance, resolve scalability issues and increase the system efficiency by dividing the task being performed on a single machine to different systems is called distributed computing.

Distributed computing has many applications such as the world wide web, global financial systems, machine learning and much more. Here we concentrate basically on the concepts of Machine Learning Training with distributed computing.

Distributed computing training 

The main purpose of this training in machine learning is to help an individual master the skills in machine learning and resource allocation and management. Distributed computing came up as a technique to resolve the scalability associated with machine learning algorithms. It developed on a massive scale in recent years to provide large-scale operations such as big data analysis efficiently.

When we talk about distributed computing, there are two main approaches:

  1. Horizontal fragmentation- It uses an approach to store the selected portions of the available instance at different sites.
  2. Vertical fragmentation- Storing of the selected attributes of the subsets of the instances comprises of vertical fragmentation.

The data involved in machine learning is very massive if a real-time problem is involved. A situation might be encountered where the machine learning model needs to be trained again and again without disrupting the ongoing parallel task. In this situation, distributed computing serves as a boon by resolving the issues.

The training in distributed computing also highlights the importance of applying these techniques in fields such as medical computing where huge amounts of data are uploaded at every instance of the given time and need to be analyzed for relevant purposes.

Distributed machine learning platforms

Training in distributed computing for machine learning also provides information about the platforms that been developed to do so. Some of these platforms are listed below:

  • H2O- Developed by H2O.ai, H2O is an open-source platform for distributed computing in machine learning with in-memory support. It also provides support for traditional machine learning algorithms and includes AutoML functionalities.
  • TensorFlow- Distributed TensorFlow provides different servers each of which is considered to be a cluster and each process is made to run on an executive search engine.
  • DMTK- It stands for distributed ML toolkit and is developed by Microsoft to provide highly efficient techniques for performing a machine learning task.

Apart from the frameworks mentioned above, there are other frameworks such as Apache Spark Mlib and Apache Mount that assists in the machine learning applications as well.

Conclusion

Most of the problems that we encounter today are voluminous and very hard to process for machine learning tasks. Distributed computing left its footprints in the field of machine learning by solving one of the major issues that are big data handling. It has gained a lot of popularity in recent years because of its high degree of scalability, efficiency, and performance. It has not only helped in performing large-scale computations but has also helped in the optimization of the operating systems. To be accurate, it has revolutionized the world of machine learning training and computations.

How Managing Supply Chain Effectively Can Help You Reduce Overhead Costs

Posted on by Imarticus Learning

Effective supply chain management can help a business to grow amidst high competition. From buying raw materials to selling products, businesses can reduce the errors that hamper the ROI (Return on Investment). Effective supply chain management can reduce overhead costs and boost revenue. The day-to-day operational cost of a company is known as the overhead cost. Read on to know how effective supply chain management can help in slashing overhead costs.

 

Understanding supply chain management

 

Supply chain management includes the flow of products/services from the manufacturer to the customers. Every business should know the flow of products/services in the supply chain for better results. With the help of supply chain data, businesses can reduce operational issues. The objective of supply chain management is to slash overhead/inventory costs and reduce operational errors. Many businesses invest in supply chain software to know more about the flow of products and services. With effective supply chain management, you can fulfill the customer requirements and build better relationships with distributors/vendors. At present, businesses are hiring employees that have a certification in supply chain management for slashing operational costs.

 

Slashing energy costs with supply chain management

 

Businesses try to consume limited environmental resources to slash their overhead costs. Warehouses and outlets use lighting and coolers all day long. Businesses spend a large amount on maintaining the lights and other devices that utilize energy. With effective supply chain management, you can uncover the energy costs involved in your day-to-day operations. The lights and coolers can be replaced with other devices that are energy efficient. You can get to know which areas of your supply chain are consuming more energy than others.

 

Reduce the cost of office supplies

 

Office supplies may not seem costly at prima facie but can add a lot to your overhead costs. With effective supply chain management, you can get to know the cost of supplies used in your offices every month. Many businesses go paperless to cut the cost of ink and paper. Paperless documentation is easy and cheaper than traditional paper documentation. Even a small adjustment in your supply chain can save a lot in the long run.

 

Cost tracking with effective supply chain management

 

Operational costs can be slashed when the business knows about all the costs. Effective supply chain management includes documentation of all types of costs. You can study the record of your costs and can compare the records of different months. If any changes in the costs have occurred, you will quickly identify them. Once you have maintained an effective supply chain, you will spend less on retaining it. You will only fix those areas of the supply chain that need improvement.

 

How to learn more about supply chain management?

 

A supply chain management course can help in learning the required skills. You can go for the Professional Certification in Supply Chain Management & Analytics offered by Imarticus Learning. This course is provided by Imarticus in partnership with IIT Roorkee. You can learn job-relevant skills from the IIT faculty via this online course. One-to-one mentoring sessions will be provided by industry experts during the course. This course will also include a 3-day immersion program that allows students to interact with peers in IIT Roorkee. You will work on various real-life projects related to supply chain management in this online course.

 

Conclusion

 

The supply chain management online course offered by Imarticus follows an industry-oriented curriculum for a better learning curve. From career support to supply chain assignments, Imarticus will offer it all via their online course. Start your supply chain online course now!

How Providers Can Use AI to Improve the Payment Integrity Process

Posted on by Imarticus Learning

Nowadays AI is utilized successfully and has proven to be an efficient, cost-effective, and reliable solution to cut down inappropriate payment claims worth a million dollars every year. The anomalies and patterns can be detected in less than a minute which helps to decrease fraud, system abuse, and future wastes.

From the provider’s point of view, they can be educated well to ensure evidence-based and high-quality alternatives. Learn more to know how the AIML program by Imarticus uses AI to improve the payment integrity process.

AI and Payment Integrity

A huge data volume from the providers, facilities, labs, etc. is integrated with AI-based computer power systems. This recognizes patterns in the data in a very effective and automatic way and helps to identify false claims. However, the billing behavior of the providers is difficult to detect as they are usually dealing directly with third-party enterprises for handling billing and coding issues.

This outsourcing may result in missing clarity and inconsistent processes which can ultimately lead to upcoding errors and fraudulent claims.

Thanks to the AI certification course, the identification of errors and fraud is a quick procedure with high precision and accuracy and the errors can be avoided drastically.

artificial intelligence and machine learning coursesInteroperability, APIs, and NLP Efficiency

The real innovation lies in the fact that the medical records of the patients can be directly obtained from the providers of EHRs with firm signed contracts.

This kind of interoperability helps in making the tasks work automatically like pre-authorization of the requests as per the need. This saves the manual working hours and makes the entire system run fluidly.

AI-based natural language processing (NLP) can further accelerate the time-saving process by around 40 percent when used on unfiltered data in the review stages. This helps in the augmentation of the staff efficiency and reduction of the costly human resources like nurses.

Integrating technologies like AI, NLP, robotic processing, and machine learning courses can give the payers the advantage of controlling the expenditure. Furthermore, it gives a helping hand to the providers to better manage the revenue systems to have a more unified and fluid cash flow within the system.

Prepayment cost avoidance model

One of the emerging trends of the industry is a significant shift to a prepayment from a post-payment cost avoidance model. It results in cost reduction related to reprocessing, reworking, and claim recoveries. But, the payers have to be super cautious when adopting this method as it is not yet well demonstrated and proven. Payment integrity based on AI is positioned very uniquely and this prepayment cost reduction model is close to becoming a reality in the industry soon.

Educating the providers

To overcome overutilization and fraud claims another approach that can be employed is their pre-detection by the providers themselves even before the claim submission. During the overpayment or appeal recovery process, the providers can be educated about the non-compliance, errors, overpayment issues, or the reasons for service rejection. This can increase the cooperation from the providers and helps decrease the number of appeals made.

On the same lines, AI-based technologies can analyze the data sets and send responses to the doctors, and list all the factors causing the denial of the claim and also about the unnecessary medical care as mentioned in the health plans.

Conclusion

Finally, analytics and solutions based on AI can ensure to cut down inappropriate claims significantly by identifying the wrong claims and acting upon them. Learn AI and improve the healthcare systems by making proper and efficient use of AI-based algorithms and methods.

How Has Data Science Given Rise to Smart Logistics?

Posted on by Imarticus Learning

How Has Data Science Given Rise to Smart Logistics?

Every day, billions of packages are delivered to customers by the logistics industry. At every supply chain node, a large quantity of data is generated. Customer data and delivery data are collected by the logistics firms every day. Data science plays a crucial role in supply chain management and many other logistics processes.

Businesses are relying on data science to reduce waste, forecast demand cycles, manage delivery routes, and many other processes. Young enthusiasts can learn data science to earn a lucrative job offer in the logistics industry. Read on to know how data science is affecting the logistics industry.

 Autonomous vehicles for logistics 

With the growing population, businesses have to cater to the growing needs of the customers. Also, e-commerce sites are growing in number that has generated more online customers. Delivery teams now have to cover remote areas for delivering the packages to customers. Even the top logistics companies in the world are facing driver shortages. It is why many experts are suggesting the use of autonomous vehicles for delivering packages. It may seem like a far-fetched thought but, autonomous vehicles are already available in the market.

AI and ML algorithms are used for designing better autonomous vehicles. As a data scientist, one should be familiar with AI and ML. If autonomous vehicles disrupt the services of traditional vehicles in the future, data scientists will be in huge demand. You can learn data science now to make your skillset futureproof and earn a lucrative job offer.

Smart warehouses 

For storing different types of products, logistics firms need many warehouses. Some products need to be stored under specific temperatures. For example, meat products need to be stored in cold temperatures. The temperature requirements may differ from one product to another in a warehouse. With the help of data science and ML, smart warehouses can be created. Smart warehouses help you set automatic alarms for any temperature failure. All the products can be stored in ideal conditions with the least manual interruption. It will prevent the product damages that occur in warehouses.

Market forecasting with data science 

Data science can help in analyzing customer data and better supply chain management. With data science, you can forecast market demands and supplies. Many times, warehouses have to bear a loss due to oversupply or undersupply. Data science can help in designing smart algorithms that can predict supply and demand trends. Logistics firms can track their supply following the demands of the customers.

Reverse logistics with data science

 Data science algorithms can identify the geographic locations that are prone to return the products. Based on that, you could target geographic locations accordingly. Fewer customers will return your product and you can save the cost for reverse logistics. You can build a successful data scientist career if you can help businesses to slash operational costs.

How to learn data science for logistics? 

An online data science course in India can help in learning industry practices. Imarticus Learning is a reliable EdTech platform that can help in learning data science for logistics. The PG Program in Data Analytics & ML offered by Imarticus can make you job-ready.

best data science courses in IndiaWith an industry-designed curriculum, you can learn about the use cases of data science in the logistics industry. From logistic regression to programming languages, this course will cover them all.

 Conclusion 

 The course offered by Imarticus will help you in learning via 25 real-life projects related to data science. A data science online course can help in kickstarting a data science career or getting a raise. Start learning data science for logistics now!

How Do Business Analytics & Data Analytics Differ? What Are Their Applications?

Posted on by Imarticus Learning

Both business and data analytics is about processing data and gaining insights from data.

The terms business analytics and data analytics might be used interchangeably in organizations on a small scale. On the other hand, big firms hire both business analysts and data analysts to perform well-defined functions, thus making it very necessary to understand the difference between the two.

With Imarticus’s, you can choose between the best data analytics courses and business analytics courses in India. Read on to have a deep insight into the two important career paths and their respective applications.

best data analytics courses in IndiaBusiness Analytics

Business analytics course focuses on business impact when analyzing data and the actions that should result from the insights. For example, it provides answers to the following questions, “Should a company develop a new product line?” Or, “Should it prioritize one particular project over another?”

Business analytics combines various capabilities, tools, and applications to measure and improve the effectiveness of core business functions such as marketing, customer service, sales, or IT.

Business analysis (also Business Analytics) is the iterative exploration of a company’s data. There is a statistical analysis method used to bring information to light that can help to stimulate innovation and business results. Companies that rely on analysis consider big data to be a valuable asset of the company: data helps to advance business planning and forms the basis for future strategies. Business analysis helps these companies get the maximum value from this gold mine of insights.

Sufficiently large volumes of high-quality data are required for business analysis. Companies that want to achieve the most accurate results possible have to integrate and compare the data from different systems. A decision is then made as to which subsets will be made available to the business.

Data Analytics

A Data analytics career involves combing through massive data sets to identify patterns and trends, create hypotheses, and support business decisions with data-based insights.

For example, data analytics seeks to answer questions such as, “What impact do geographic factors or time of year have on customer preferences?” Or, “What is the likelihood that a customer will defect to a competitor?” In practice, data analytics encompasses many different techniques and approaches. It is also known as data science, data mining, data modeling, and big data analysis.

In data analysis, raw data is collected and examined to conclude it. Every company collects huge amounts of data, such as sales figures, market research, logistics, and transaction data. The real benefit of data analysis is in identifying patterns in a data set that can indicate trends, risks, and opportunities.

Data analysis enables companies to use this knowledge to change their processes to make better decisions. In practice, data analysis can help, for example, to decide about the next product developments, to develop customer loyalty strategies, or to evaluate the effectiveness of new medical treatments.

Applications of Business Analytics and Data Analytics

Since Business Analytics and data analytics are both based on big data tools, they have several applications in various industries across the globe. Some of them are mentioned below:

  1. Digital Advertising
  2. Energy Management
  3. Medical Applications
  4. City planning and mapping
  5. GPS tracking
  6. Transportation
  7. Risk detection and management
  8. Security
  9. Transportation
  10. Traveling
  11. Customer interaction
  12. Internet Browsing
  13. Expenditure Management

Conclusion

Business and Data analytics share the same overarching goal: use technology and data to drive business success. We live in a data-driven world where the amount of information available to businesses is growing exponentially. Both functions in combination can help companies achieve maximum efficiency and gain some useful insights.

Artificial Intelligence skilling has to start from a young age! How? Explore…

Posted on by Imarticus Learning

The chasm between machines and living things is shrinking. Artificial intelligence (AI) is deeply rooted in all aspects of technology, from robots to social networks. India has the potential to skyrocket in the domain of Artificial Intelligence and surpass USA and China, largely owing to:

  • It’s deep-rooted IT &ITeS infrastructure
  • Innovation ( India ranked among the top 50 countries in the Global Innovations Index 2020)
  • Accessibility to large datasets

These have pioneered more than a handful of start-ups and private investments in this sector. For AI to flourish further, there needs to be a nationwide upskilling of the younger generation in Artificial Intelligence Training. The GenZ needs to be acquainted with the theoretical and practical aspects of AI application to increase its scope of innovation and entrepreneurship.

Artificial Intelligence CareerIn the future, the interaction between humans and AI will define in a lot of ways the structure and functioning of a modern-tech society.

Thus it becomes imperative to lay down the basis of friendship for the years to come by exposing the young ones to AI.

While a lot of minds will wander to an Artificial Intelligence Career it is also important that others are no less familiar with the upsides and downsides of such a powerful technology.

Here is how we can ensure the frontiers of the same:

  • Introduce young people to the concepts of AI and machine learning through education curriculum. In India, the Central Board of Secondary Education (CBSE) announced the integration of AI in partnership with IBM for the academic year 2020-21
  • Encourage learning through hands-on projects so that student can make better, informed and critical use of these technologies
  • Enrolling young minds on various Edu-tech platforms specializing in the field of Machine Learning and AI which help them gauge interest and real-life applications of such technologies using intuitive software

Some of these websites include- Scratch, App Inventor, Cognimates etc

  • Experiments with Google is an easy-access, affordable, and user-friendly tool to explore artificial intelligence training at a young age with exciting experiments on AI, VR, AR, Chrome, Voice, Android etc to apply creativity and technological dexterity at the same place. One of these fun-filled learnings includes MixLab that uses voice commands to create music
  • Engage in the practice of cultural inquiry – like what is the goal of You tube’s recommendations or how do my Amazon purchases reflect on my Instagram feed
  • Lastly, before introducing your children to the world of AI and machine learnings, self-education of the same is very crucial

Apart from exploring the possibilities of AI, these junior minds also need to know the limitations of AI to have a balanced approached. That is to say, AI is not the ultimate machine as it is created by humans and will improve along the way by errors made and rectified by humans.

Artificial Intelligence CareerIn recent studies, a scientist is experimenting to teach AI to learn like a kid. They want to inoculate the eager learning attitude and swift skills of young people into the algorithms of machines.

And, AI does not create everything. It is the innovation and vision of responsible human beings that will introduce, implement, and maintain the technological structure in human society.

Complete Guide To Vectors in Linear Algebra With Implementation in Python!

Posted on by Imarticus Learning

Mathematical implementation and mathematical functionality play a key role in understanding the workings of various physical entities. Creating mathematical models and mathematical measurements is essential to give shape to the theories and concepts. It also plays a vital role in writing code and new-age Machine learning algorithms.

Measurement:

Any attribute of an object that can be assigned with a meaningful number to observe, assess or understand the item is called Measurement. This measurement can be broadly divided into two types:

  1. Scalars
  2. Vectors

Scalars:

The measurement of the attribute of the objects doesn’t depend on the direction of the item.

SclarTo illustrate the definition let us consider the length between two points. The span between these two points doesn’t change depending on the direction as the size remains the same.

Vectors:

The measurements of the attribute of the objects depend on the direction of the attribute too.

Vector

This can be understood by taking the example of force. Force needs a path with the numerical to comprehend the measurement

Vectors are denoted by an Arrow (entirely→).

The direction where the vector points are called the vector’s direction.

Types of Vectors:

  1. Zero Vector or Null Vector: A zero vector is the vector consisting Zero-Length and no direction
  2. Unit Vector: A vector that has a magnitude of 1 with a set direction.
  3. Collinear Vector: If the vectors are parallel to each other or on the same line irrespective of their direction.
  4. Coplanar Vector: All vectors that lie in the same plane
  5. Equal Vector: If the vectors have the same magnitude and direction
  6. Position Vector: A point that can be constituted as a constant point regarding other vectors

Implementation of vectors in Python: 

Vectors are a beneficial component not only in computer languages but also in machine learning. Decision-making is one of the most critical aspects of machine learning and vectors, in particular, is used in one such algorithm called Support Vector Machine (SVM). An SVM is used to analyze the given dimensional space for finding optimal hyperplane. The concept of vector/Euclidean distance is used to know the distance between data points and hyperplane.

Python TrainingTo achieve this through machine learning, we use Python as the programming language using libraries such as NumPy, Pandas. Python and the array operations in Python are useful to perform many algorithms such as SVM.

Therefore, having a prior Python Training is valuable and essential to get a grip on how vector functionalities are applied in more advanced topics such as Machine Learning.

Implementation in Python:

Vector Implementation can happen through arrays in Python. All the vector functionality can be done through libraries like NumPy. Using a simple code, we can implement various basic vector functionalities such as

  1. Vector Addition: The addition of two vectors through Python can be seen here:

 

Vector

import numpy as np #pip install numpy

a=np.array([2,1,3])

b=np.array([4,5,3])

print(a+b)

Output: [6,6,6]

  1. Vector Subtraction: Subtraction of two vectors through Python can be seen here:Vector Subtraction

import numpy as np

a=np.array([2,3])

b=np.array([1,-1])

print(a-b)

Output: [1,4]

  1. Scalar Multiplication: Multiplying a scalar to vector is given below:Scalar Multiplication

import numpy as np

a=np.array([3,5])

print(3*a)

Output: [9, 15]

  1. Euclidean Distance calculations: In Euclidean distance calculation the distance is measured between two points and can be done in Python as follows:Euclidean Distance calculations

import NumPy as np #pip install NumPy

a=np.array([4,6])

b=np.array([7,-2])

print(np.linalg.norm(a-b))

Output: 8.0622577

These are some of the implementations of vectors in linear algebra using Python. Python is an essential language to understand advanced topics such as machine learning. Therefore, basic Python Training is the best step to ensure a great career.

How to Create Efficient Supply Chain Management Systems

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How to Create Efficient Supply Chain Management Systems

E-commerce platforms are experiencing immense growth and this has led to the boom in supply chain management (SCM). Therefore, SCM Analytics is essential. It helps to analyze available data and gather metrics that can determine how systems can become more efficient.

Aspiring candidates can enroll in a supply chain management course to work in the field. Imarticus Learning offers professional certification for students who have basic knowledge of programming and operations management.

Top 5 Ways to Ensure an Efficient Supply Chain Management System

The working and proper management of a supply chain ensures the performance of a business and brings in new customers. So, companies need to maximize the efficiency of SCM. Candidates who wish to work as managers can focus on a supply chain management and analytics course that will enable them to learn how to create consistent supply chain management systems.

Following are 5 ways in which the efficiency of SCM systems can be improved.

  1. Get Reliable Suppliers

Introducing the right suppliers is essential to maintaining an efficient supply chain. Suppliers should be dependable and should have a history of good service. If the suppliers consistently provide good products, businesses will thrive and the supply chain will be sustained. Supply chain managers and analysts need to thoroughly research all potential suppliers. It will help to establish while suppliers will ensure product quality and packaging. Having reliable suppliers also means that the business can maintain ethical approaches and provide the best service to customers.

  1. Invest in New-Age Technologies

Managers can use a Warehouse Management System to streamline and maintain the efficiency of supply chains. Software applications should be used to collect relevant data, obtain insights, and document the performance of supply chain systems. Certain businesses can invest in transport management systems. These help to track and ship products without any errors. The entire SCM system can also be shifted to mobile devices for managers to work on the go.

  1. Ensure Constant Supply Chain Improvement

A supply chain that is efficient should remain so. SCM workers need to focus on new ways to tackle and maintain the efficiency of the supply chain. The improvement can be in terms of cost efficiency and benefits, customer service, or delivery to market.

  1. Returns Management System

A returns management system is an important aspect of SCM. This particular system allows companies to process returns faster. It improves the efficiency of the actual supply chain by keeping track of all the damaged goods that need to be processed again or manufactured again.

  1. Employee Development

For a supply chain to remain functional, a business needs dedicated and efficient employees. Every supply chain has a unique set of problems that require quick solutions. Employees should be able to provide those solutions and manage the supply chain in a way that minimizes errors. Therefore, businesses need to invest in proper training and strategizing.

How To Create Efficient Supply Chain Management Systems?

Candidates who wish to become SCM professionals and create an efficient system for businesses should invest in professional certification. Imarticus Learning offers a supply chain management and analytics course that is in collaboration with IIT Roorkee.

Students are taught to make decisions that are data-driven and based on valuable insights. Imarticus Learning provides hands-on training through 6 different projects. Students are also encouraged to participate in live lectures organized by industry experts. The institute provides a holistic education in supply chain management and ensures campus immersion at IIT Roorkee.

A supply chain management course from Imarticus Learning allows students to hone their managerial skills and use advanced software applications to provide error-free solutions. After completing the course, students can become supply planners, demand planners, supply and operations planners, or even data scientists.

How Artificial Intelligence and Machine Learning Is Altering the Marketing Landscape?

Posted on by Imarticus Learning

In this technological era, new technologies are being introduced in the market now and then. Businesses are no more using the traditional marketing methods to generate ROI (Return on Investment). Businesses are using new-age technologies like AI (Artificial Intelligence) and ML (Machine Learning) for better marketing and promotion.

Many young marketers are looking for a certification in AI & ML for securing better job roles in the marketing industry. The marketing landscape is continuously changing, and businesses need a sustainable solution. Read on to know how AI & ML are disrupting the traditional marketing landscape.

Use cases of AI and ML in marketing 

 One can see many real-life use cases of AI and ML in marketing around them. Some of the most popular use cases of AI & ML in marketing are listed below:

  • Businesses now use chatbots for their websites or software applications. Chatbots can interact with customers all day long and can solve basic queries. With a better user experience, businesses can retain their customers.
  • AI & ML can help provide accurate information to customers whenever needed. Inducing automation in marketing processes can reduce the manual load on an organization.
  • AI and ML tools are used by businesses to track dark social sharing up to an extent.
  • Many businesses are planning to use self-driving vehicles for marketing campaigns.

Driving factors for the use of AI & ML in marketing

 At present, businesses need to create personalized marketing content based on the preferences of the customer. Predictive analysis powers of AI/ML tools are helping businesses to create personalized marketing content. The recent COVID pandemic made it difficult for marketers to connect with their customers.

There was no physical interaction with the customers during the pandemic. AI & ML can help in collecting customer data even without stepping into physical markets. The need to decrease human labor in marketing processes has also forced businesses to go with AI and ML tools. With automated marketing processes, businesses can cut costs and boost ROI. In recent times, many businesses have focused on hiring marketers that have a certification in AI & ML for better results.

 How AI & ML are altering the marketing landscape 

Traditional marketing campaigns are launched with minimal insights. With AI & ML, you will have better insights before launching marketing campaigns. Marketing campaigns are becoming target-specific with AI & ML.

An ML/artificial intelligence course can help in learning more about disruptions caused in the marketing landscape. Businesses have to adapt according to the ever-changing marketing landscape to retain their market share. The benefits of AI & ML in marketing are listed below:

  • Marketers are getting better sales leads with AI & ML tools.
  • Marketers can understand the customer behavior and then launch marketing campaigns accordingly. It is better to understand the pain points of customers and then create personalized products/services.
  • Businesses are leveraging the power of customer data with AI/ML tools. The overall marketing cost is also decreased with the use of AI and ML.

How to learn AI & ML in 2021?

Young aspirants can go for the ML/artificial intelligence course offered by Imarticus Learning. The course name is ‘Certification in Artificial Intelligence & Machine Learning’ sponsored by E&ICT  Academy, IIT Guwahati. The certification in machine learning and AI follows an industry-approved curriculum. With numerous real-life industry projects and assignments, Imarticus offers a better learning experience.

Conclusion

Online certification in machine learning and AI can help marketers in securing better job offers. The role of AI & ML in marketing is increasing due to the recent WFH (Work from Home) culture. Start learning AI and ML for productive marketing!

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