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Moving beyond handcrafted architectures in supervised learning

Posted on by Imarticus Learning

Moving beyond handcrafted architectures in supervised learning

There was a time when handcrafted architectures were the norm in supervised learning. But those days are behind us now, and we are moving towards more automated methods. This blog will discuss why handcrafting is no longer the best approach for supervised learning tasks.

What is a handcrafted architecture in supervised learning, and why is it used?

A handcrafted architecture is a predefined structure used to guide the learning process in supervised learning. It simplifies the learning process and improves generalization. While handcrafted architectures are effective, they can also be limiting.

Recent advances have led to the development of more flexible and powerful architectures that can be learned automatically from data. These architectures are often more effective than handcrafted ones, as they can learn to exploit the regularities in data that are most relevant for the task at hand.

Some of the reasons why handcrafting are no longer the best approach for the supervised learning task.

  • One reason is that handcrafted features are often low-level and do not capture high-level abstractions necessary for many tasks. 
  • Another reason is that the handcrafted feature space is often limited and does not allow for the use of more powerful learning models such as deep neural networks. 
  • Finally, handcrafted architectures are often designed for a specific task and do not generalize well to other tasks. 

Thus, it is clear that handcrafted features and architectures are no longer the best approaches for supervised learning tasks.

How does the move to artificial intelligence impact supervised learning?

There has been a shift away from traditionally handcrafted architectures in supervised learning towards more automated machine learning approaches in recent years. It is the increasing availability of data and computing power that has allowed for the development of more complex models.

There are numerous benefits to using machine learning for supervised learning tasks. Machine learning models can automatically learn features from data, improving performance. In addition, machine learning models are often more robust to changes in data than handcrafted architectures.

There has been a significant shift in building supervised learning models in the past decade. We’ve moved from primarily using handcrafted architectures to a more data-driven approach. It is mainly due to the success of deep learning in various tasks such as image classification, object detection, and natural language processing.

However, deep learning is not the only machine learning approach to achieving state-of-the-art results. Many other methods, such as support vector machines, decision trees, and random forests, can also be very effective. AIML is a course that will teach you about these other methods and how to apply them to different tasks.

Discover AI and Machine Learning courses by Imarticus Learning

The E & ICT Academy, IIT Guwahati, and the most significant industry experts come together in this Artificial Intelligence and Machine Learning certificate. This artificial intelligence course will assist students in preparing for data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits for Learners:

  • Prepare for a fascinating career in data science by completing 25 real-world projects that give you industry expertise.
  •  Impress employers and display your skills with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.
  •  This machine learning and artificial intelligence course will assist students in seizing lucrative job possibilities in the hot fields of artificial intelligence and machine learning.

Guide To Computer Vision: Why Does It Matter?

Posted on by Imarticus Learning

Guide To Computer Vision: Why Does It Matter?

The use of computer vision in the actual world is expanding, challenging, and redefining its meaning. Computer vision is becoming an increasingly prevalent word in conversation across the globe and industries: computer vision systems, software, computer vision hardware, computer vision development, computer vision pipelines, and computer vision technologies. This blog post will teach you about computer vision in detail. 

What is Computer Vision?

Computer vision is a field that utilizes images to analyze and interpret objects, scenes, and motions. The difference between computer and machine vision is that the latter does not rely on visual information alone but instead processes signals from other sensors such as infrared or ultrasonic sensors.  

You can use computer vision in a variety of applications, including:

  • Robotics: A robot equipped with computer vision technology will interpret its environment better than before, allowing it to avoid obstacles while navigating around objects.
  • Autonomous Vehicles: Advanced driver assistance systems (ADAS) use computer vision algorithms to identify road signs and alert drivers if they need to take action based on what they see (e.g., a stop sign). ADAS can also detect pedestrians or cyclists who may cross their path while driving at high speeds.
  • Security Systems: Modern cameras can detect intruders by recognizing faces or body shapes through infrared cameras; even though these systems aren’t perfect yet—they’re still pretty helpful in preventing crimes from happening nearby businesses.

How Does It Work?

Computer vision is a subsection of machine learning and artificial intelligence. The distinction between these three terms can be confusing at first, but they’re not that complicated once you understand each one. Let’s start by looking at how computer vision differs from machine learning and deep learning.

Machine learning is how computers learn to perform tasks based on data provided by humans or other sources. Deep learning refers to the use of artificial neural networks (ANN) to solve problems in visual recognition, speech recognition, natural language processing, and reinforcement learning—and it’s responsible for making recent breakthroughs in AI possible!

Why Does Computer Vision Matter?

Computer vision is the future, and it will change how we interact with machines. It will change the way we interact with each other. 

Computer vision is important because it’s an essential component of autonomous vehicles, which will be critical for reducing traffic-related deaths on roads. Deep learning is a subset of artificial intelligence, a subfield of computer science.

The future looks bright for computer vision. It’s a fast-growing field with significant potential to change the world, and it will have a considerable impact on our lives and society.

Explore AI and Machine Learning courses with Imarticus Learning.

This deep learning Artificial Intelligence credential brings together the E & ICT Academy, IIT Guwahati, and the most prominent industry specialists. This AI course will help students prepare for careers as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners:

  • Students participate in 25 real-world projects to get industry experience and prepare for a fascinating future in data science.
  • Impress employers and demonstrate your talents with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed certificate.
  • This IIT artificial intelligence course will help students land lucrative jobs in artificial intelligence and machine learning disciplines.

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

It’s time to improve your procurement skills with a supply chain analytics certification

Posted on by Imarticus Learning

It’s time to improve your procurement skills with a supply chain analytics certification

The supply chain is a field that comprises a massive amount of data to be handled. In today’s world, leveraging big data in the supply chain is next to impossible without the help of data analytics. Analytical methods use data analytics to collect the data and process it to improve operational efficiency and effectiveness. Taking up a supply chain analytics course plays a vital role in providing decision-making skills to the management at operational, tactical, and strategic levels.

Analytics cover sourcing resources, manufacturing the products, distributing, and ensuring logistics. Therefore, supply chain management careers and posts such as supply and operations planner, data scientist, and demand planner are in high demand. If you have a basic understanding of operations management and are interested in supply chain management, this course is for you.

Why choose us for this Supply Chain Analytics course?

At Imarticus, the Professional Certification in Supply Chain Management and Analytics is a supply chain analytics course exclusively designed by subject experts who have meticulously drafted the curriculum to meet the rising demands of the industry.

The curriculum has been approved by the most prestigious IIT Roorkee, which arms the candidate with the potential to pursue a supply chain management career. We ensure that you get the right mentor to guide you in your education. The course also offers you practical exposure with seminars, workshops, and industrial visits to help you gain experience.

What does the course exactly offer?

This supply chain management course imparts the critical skills needed to attain mastery in the field. Extra credits provided in the course provide flexibility to increase the weightage of the certificate. The course at Imarticus provides the students with supplemental readings, practical problems, and opportunities to create an interactive and collaborative learning environment.

Taking up this supply chain analytics course provides the candidate with immense knowledge of the latest data analytics approaches to support an organization in making data-based decisions. The course is for people who would like to integrate their supply chain management knowledge with the current data analytics system. 

What makes this Supply Chain Analytics course such an in-demand course?

A supply chain analytics course throws light on the ways to collect, evaluate, and improve the quality of supplier-to-consumer metrics and KPIs and support strategic organizational objectives and decision-making.

Supply chain analytics also plays a role in conducting the functions mentioned below, which makes it one of the most in-demand courses in the market:

  • Enhance risk management by detecting current known risks and forecasting future risks
  • Improve planning accuracy by examining data to identify factors that cause a rise or fall in demand
  • Refine order management by collecting data sources to predict demand, assess inventory levels, and detect fulfilment issues
  • Streamline and organize procurement by allocating budgets and analysing expenditures across several departments
  • Increase working capital by making robust models – this helps determine the inventory levels needed to accomplish service goals with minimum capital investment

Students can build data-driven projects in various domains, including competitive analysis, demand forecasting, distribution, and logistics.  

After the supply chain analytics course, the student will receive a certificate validating their expertise in the subjects covered in the curriculum, enhancing their resume and attracting prospective employers’ attention. The supply chain would require data analytics since customer demand increased with each passing day. To meet the diverse demand of the customers and function effectively, it is essential to increase the production and supply chain with a focus on analytics. 

What would you be able to achieve from the Supply Chain Analytics course?

  • Understand and apply business analytics tools to optimize the company’s performance 
  • Gain practical experience to conduct efficient procurement functions
  • Pick the right supply chain strategy to create a competitive advantage in the market
  • Become well-versed with descriptive analytics, predictive analysis, and prescriptive analysis

This course has a lot more to offer, including computer languages such as Python and Tora. Our alumni currently work in the supply chain and logistics departments of Amazon, FedEx, SAP, Nestle, Johnson & Johnson, etc. 

Learn more about the Professional Certification in Supply Chain Management and Analytics from IIT Roorkee 

Unsupervised V/S Supervised Learning: The Ultimate Tech Battle 

Posted on by Imarticus Learning

Unsupervised V/S Supervised Learning: The Ultimate Tech Battle

To understand machine learning, it is crucial to understand the type of data and how to utilize it to the best of our efforts to solve real-world problems. This is where AI ML courses provide the proper guidance needed to get started. Within artificial intelligence and machine learning, supervised and unsupervised learning are the two basic approaches to handling data; these patterns can help you predict future behaviour or outcomes. 

Supervised Learning

Supervised learning is a machine learning technique that allows computers to learn from examples. In supervised learning, the computer is provided with a set of training data (a list of inputs and corresponding outputs) to learn how to map input data into output data. The most common application of supervised learning is for classification—the computer learns to recognize patterns in the data and make predictions based on those patterns.

The most common supervised learning applications are prediction, classification, and regression. Classification is used when you want to assign an object or a piece of information into one or more categories. In contrast, regression is used when you want to predict the values of some variable(s) based on other variables.

Supervised learning has many applications in the real world, including

–Prediction: Predicting what will happen next.

–Classification: Identifying categories and subcategories of items.

-Regression: Finding trends based on historical data. 

Unsupervised Learning

Unsupervised machine learning is a type of AI that enables a computer to learn and make predictions without being given any specific examples of correct answers or any input data that has been marked as correct.

In this type of learning, the algorithm is provided with unlabeled data and must be able to figure out how to group it into meaningful groups. One example would be grouping similar images into categories like “cats” or “not cats.” Another example would be identifying objects within an image, like a dog or a cat.

Unsupervised learning is a type of machine learning that involves analyzing data without any prior knowledge about the structure or patterns in it. In unsupervised learning, an algorithm will attempt to find patterns in the data and use them to make predictions.

Unsupervised learning is used in natural language processing, computer vision, and other fields. Unsupervised learning can be used for many different purposes, but there are some common applications including: 

  • Discovering hidden variables
  • Identifying relationships between variables
  • Identifying anomalies in data, predicting values
  • Finding clusters or groups within a data set.Here’s what both of them have to offer against the other-
Unsupervised Supervised
Unsupervised learning, also known as self-organized learning or unguided learning, means that you do not give any specific training examples to your model. You just provide it with a set of data and let it learn from it. This type of learning is usually used for clustering and dimensionality reduction. It is also called inductive inference because we use training data to learn how to generalize from our observations about some phenomenon in order to make good predictions about future events involving that same phenomenon.
The algorithm is given a set of data and must identify patterns within it. For example, if you have a list of songs and their genres, then the goal would be to determine the genre of each song without any other information. Here, an algorithm is given a dataset along with metadata—information about how it should be interpreted—and then learns to categorize new examples according to what it has learned from its training data. For example, if you have training data consisting of images labeled as “cat” or “dog,” then the goal would be for your algorithm to learn what category each image belongs in so that when presented with new images, it can correctly identify them as well.
This type of machine learning has many applications in fields such as medicine, where it can be used to identify cancerous cells in medical images; finance, where it can be used to predict stock prices; and retail, where it can help identify products likely to be purchased by customers. Supervised learning can be used to build predictive models that can be used in many different business applications, such as fraud detection and customer churn prediction. It can also be used for modeling time series data, recommendation systems, and classification problems.

Unsupervised learning is ideal for finding hidden patterns in your data. These patterns can help you predict future behavior or outcomes. You can use unsupervised learning to find clusters of similar customers, for example, and then use those clusters to predict what products they’ll buy next. Or you can use them to find correlations between different variables—like age and income—and then use those correlations as a starting point for further research into specific groups of people who share those characteristics.

Supervised learning is also useful because it helps you build classifiers—a fancy word for “systems that classify things.” You might want to create a classifier that identifies whether or not someone has cancer-based on their medical records (which would be an example of supervised learning) or one that identifies if someone is behaving differently based on their social media activity.

To learn more about the prediction techniques and the integrated course offered by Imarticus learning, get in touch through our website or our offline learning centers near you. This course is led by a prestigious IIT Guwahati college. Get you the best coaching needed to boost your career growth; get in touch with our experts today!

How NLP Becomes Suitable For The Mass-Market

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How NLP Becomes Suitable For The Mass-Market

Modernization has changed our lives significantly, leading to changing needs and demands. It impacts businesses as they need to update and upgrade their products and services based on market trends. It is challenging to keep track of changing consumer demands and continue launching suitable products and services. Thus, businesses keep looking for technologies, tools, methodologies, and strategies to enhance customer and market understanding and grow their business successfully.

The industrial revolution has brought remarkable transformations in our everyday life and work. Emerging technologies are enhancing our capabilities for efficient and effective business performance. These technologies work on the data collected from various sources to extract meaningful, hidden, helpful information from structured and unstructured data. Natural Language Processing, Artificial Intelligence, and Machine Learning are some of the trending technologies in the digital world. 

Natural Language Processing (NLP) is computer-aided human language processing, including written and spoken language. It is used to either understand the language or generate the language. Artificial Intelligence and Machine Learning are used to analyze and extract meaning from the vast amount of collected data. Businesses collect data for various aspects of the company, like processing, operation, product design, technology, and marketing. NLP is suitable for companies targeting mass-market if it has:

  • Access to vast amounts of market data
  • Experts to develop cutting-edge technologies
  • Computing capacity to process the collected data

How is NLP Suitable for the Mass Market?

NLP is used to translate, generate, and understand language and emotions used in various formats like speech, text, audio, and video. Marketing has been one of the most important criteria for a successful business. Hence the suitability of NLP for marketing can be understood by its applications for business marketing, as discussed below:

  • Sequence classification: It assigns the text sequence to one of multiple previously defined classes of information. For example, information may provide emotions like joy, anger, sadness, sorrow, etc. The marketing team can determine the emotions behind feedback, review, and discussions about the product/ services on social media. Based on this information, the team can create strategies and plans to improve its sales and make the product satisfy consumer needs and demands.
  • Question-Answer models: It gives a content-correct answer to questions based on information available from the text corpora. The marketing team can use it to automate responses on chatbots or over customer service calls.
  • Text generation: It matches the word based on given text to predict another word or lengthy text accurately. It enables text generation for multiple languages based on the target group and their linguistic dialect.
  • Phrase recognition assigns one or more words in a sentence to a class, also called Named Entity Recognition (NER). 

Future of NLP Technology

NLP is the most researched domain in the artificial intelligence field. It will have advancements with more profound understanding and developments. In addition, there will be general-purpose and specialized models for language processing helpful for a wide range of businesses. The field will evolve rapidly, and thus demand for experts, professionals, and NLP technology career aspirants will increase drastically.

The latest developments in NLP technology are fast and impressive, and universities worldwide include this technology in syllabi. In addition, various training and education institutes such as Imarticus Learning Pvt. Ltd. are providing certification programs in this field to generate next-generation professionals.

Artificial Intelligence and Machine Learning Programs

If you are looking for machine learning certification courses, Imarticus Learning Pvt. Ltd. has partnered with leading institutions and corporations to help enthusiasts learn AI. Choose from among nine certification courses in Artificial Intelligence and Machine Learning, among others.

All of these courses are designed with the help of the E&ICT Academy, industry leaders, and IIT Guwahati to train aspiring machine learning certification program learners. It will help you to become Data Scientist, Machine Learning Engineer, Data Analyst, Machine Learning Architect, Data Science/ Machine Learning Consultant, and AI Engineer.    

This course develops fundamental skills and a practical understanding of diverse industries’ theoretical backgrounds and real-world projects. You can learn job-relevant skills with 25 in-class real-world projects under the guidance of world-class academic professors. The course includes:

  • Live instructor-led sessions,
  • Online discussions with mentors & peers,
  • In-class projects & exercises,
  • Assignments & assessments,
  • Capstone projects,
  • Hackathons, and
  • Career support.

It helps you unlock your career path with lucrative Artificial Intelligence and Machine Learning opportunities. Therefore, if you would like to get details, contact Imarticus Learning Pvt. Ltd. through chat support or visit training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Learn to tackle supply chain issues with tools like digital inventory management with an IIT supply chain management course

Posted on by Imarticus Learning

Learn to tackle supply chain issues with tools like digital inventory management with an IIT supply chain management course

It’s essential to understand supply chain management in the business world. This is especially true if you want to start or expand your business. Even if you already have a grasp of the concept, it’s crucial to stay up-to-date on the latest tools and technologies that can help improve your supply chain. That’s where an IIT supply chain management course comes in! In 2022, you will be able to take an online course that will teach you everything you need to know about managing your company’s supply chain.

What is digital inventory management, and how does it work?

Digital inventory management is a system that helps businesses keep track of their inventory using computerized methods. This can include barcodes, RFID tags, and other tracking technologies. By keeping track of inventory digitally, businesses can save time and money while reducing errors.

There are many benefits to using digital inventory management, including:

  • Reduced time spent on inventory management
  • Increased accuracy of inventory data
  • Easier to track and manage inventory levels
  • Reduced costs associated with inventory management

If you’re interested in learning more about digital inventory management, consider taking an IIT supply chain management course. This course can help you understand the skills you need to manage inventory using digital tools effectively. You can help your business run smoothly and efficiently with the proper training. 

How do we tackle common supply chain issues with the help of digital inventory management?

Supply chain management can be a complex and daunting task for many businesses. There are many moving parts involved, and if any one of them goes wrong, it can have a domino effect that disrupts the entire system.

One of the most critical aspects of supply chain management is inventory management. This ensures that enough raw materials and finished products are available to meet customer demand.

Digital inventory management is a tool that can help businesses keep track of their inventory levels in real-time. You can then use this information to make decisions about production, purchasing, and other aspects of the supply chain.

IIT’s Supply Chain Management course will teach you how to use digital inventory management to tackle common supply chain issues. The course is for business professionals who want to learn more about this vital aspect of the business.

If you wish to learn how to use digital inventory management to improve your business, sign up for IIT’s Supply Chain Management course today!

Discover Professional certification in supply chain management with Imarticus Learning

The online supply chain management course provides cutting-edge information to those interested in taking the operations or supply chain analytics course. Students who enroll in this online supply chain management program will learn about Demand Planner, Data Scientist job titles.

Course Benefits For Learners:

  • We immerse students in relevant technologies and projects, such as six real-world projects that will give them a solid understanding of the sector to prepare them for a successful future as certified Supply Chain analysts. 
  • Students who finish a supply chain management online course can prepare for highly sought-after professions such as Demand Planner or Data Scientist, which are in high demand among businesses today! 
  • Students will learn to become data-centric and improve Supply Chain decision-making by utilizing the Python programming language.

How a supply chain management course with analytics can transform your career in 2022

Posted on by Imarticus Learning

How a supply chain management course with analytics can transform your career in 2022

Supply chain analytics is an integral part of supply chain management and is completely data-driven. Therefore, taking up supply chain management in analytics is a great way to build your career in this field. It will help you gain insights and values from the huge datasets related to numerous supply chain management methods such as procuring, processing, and distributing goods and services. 

With the rising demand for commodities in recent years, there is a rising demand for supply chain analysts due to the production of huge datasets and managing big data. It is primarily involved in the collection of relevant data compiled from numerous applications, infrastructure, third-party sources, and future technologies.

This field aims to improve the quality of decision-making, planning, and strategizing in supply chain management. It helps in synchronizing, executing, and enhancing real-time visibility of the methods and their impact on the customers. A course in supply chain analytics gives you the required skillsets for understanding and analyzing the increase and decrease in visibility in a supply chain network that directly influences decision-making in businesses.

How is it Related to Supply Chain Management?

Supply chain analytics plays a very important role in the field of supply chain management. Professionals in this field are required to collect, compile and analyze huge datasets in a company and influence taking data-driven decisions, based on the compilation of relevant data. Supply chains produce large amounts of data daily. Supply chain analytics is required to make sense of all the data and generate insights and decipher patterns. 

Relevant Courses for Pursuing a Career in Supply Chain Analytics

Needless to say, in our fast-paced era, supply chains will cease to function correctly without supply chain analysis. Here are some of the best courses you can pursue:-

  1. Supply Chain Analytics Certification: For individuals who are eager to learn about how data analytics works in the field of supply chain management, this course is a perfect choice. It will exclusively teach you about supply chain analytics and decipher and fix problems in numerous supply chain sectors with a data-driven approach. This course will help you develop the required data analytics skills to use tools for enhancing supply chain performance.
  2. Big Data Analysis and Machine Learning: A certification in big data analysis and machine learning will open numerous avenues. One of them happens to be supply chain analytics which you can easily opt for. In this course, you will learn how to use the in-demand data science tools and techniques that are required in the job sector. You will be taught valuable skills in data analytics, Python, SQL, and data visualization with Tableau and machine learning.
  3. Predictive Analytics for Demand Forecasting: Data is considered a raw material that requires predictive analysis for deciphering it, compiling it, and effectively using it for decision making. This certification will give you the basic skills required to understand the important concepts of Predictive Analysis which include monitoring and managing big data.You will also learn about the importance of Tableau in providing specific views of minor events or linking information to the recent trends and forecasts for ensuring proper allocation of resources and improving effectiveness.

Conclusion

Data analytics has become an integral element in supply chain management in recent years. In recent years including 2022, it has indeed become a great career prospect for students of data science. A professional supply chain management course or a supply chain analytics course from Imarticus is the right start for your supply chain career.

Does data analysis using SQL for software libraries?

Posted on by Imarticus Learning

Introduction

If you love tackling complex algorithms, solving tricky mathematical problems, or are simply attracted to your computer or similar electronic/digital devices by nature of being a geek, then the life of a data scientist is what you should be running after.

One of the most sought-after jobs in the 21st century, becoming a data scientist requires some basic knowledge of core concepts of big data training. These concepts are discussed briefly in the article below.

What is Data?

Fundamentally, data refer to bits and pieces of information that are stored and formatted in a manner that is consistent with the rest of the data for a particular purpose. Data can be found to exist in any format, whether it be on paper, in figures or as bytes in an electronic device, or even as nuggets of information in a person’s brain. However, in the era of the 4th industrial revolution, any mention of data generally denotes the kind that can be transmitted electronically.

Since data has become the lingua franca in the IT industry, it is imperative that we also explore the many dimensions of data through its many phrases old and new:

  • Structured data

Data existing in a fixed field such as spreadsheets and datasets are referred to as structured data

  • Unstructured data

Information or data that is not arranged in a column-row format

  • Raw data

Information yet to be analyzed and formatted

  • Metadata

A data set that has been summarized

  • Big data

A huge corpus of both structured as well as unstructured data that cannot be processed in the traditional manner of software technologies and database

  • Big data analytics

The method of organizing, collecting, and synthesizing data to explore useful information

  • Data mining

Data applications are used to figure out patterns that cannot be seen with the naked eye from a group of data in order to predict the behavior of something in the future.

How is SQL Relevant for Data Science?

SQL or Structured Query Language is a type of programming language used in standard databases for data management systems. It was conceived in the 1970s by IBM and has now become a standard process recognized by International Organization for Standardization (ISO) and American National Standards Institute (ANSI).

This programming language is employed for better management of relational databases and performance of operations on the data contained within them.

When we speak of SQL for database management, we speak of language’s capability to engage with more than one database at the same time. SQL is highly popular for data science since it is highly flexible and offers a great accessible learning curve with respect to complex depth. This enables users to create advanced data boards and tools for analyzing data.

To create and engage with databases efficiently, SQL has been utilized in a variety of tools, each with its niche market and focus. This includes big names such as Microsoft Access, PostgreSQL, and MySQL.  

Nowadays, big data is a huge concept, and learning how to use SQL for big data analysis can be a great skill that will open up big opportunities for you worldwide.

Benefits of a Big Data Training

To understand how a big data analytics certification course with a SQL specialization is helpful, let us look at the points below:

  1.     Big data training as part of business analytics courses will be a significant skill for top organizations
  2.     Data specialists are in high demand, and a big data analytics certification with SQL specialization will be a wise career choice for you for a guaranteed upward trajectory in terms of progress.
  3.     The role of a data specialist is a high-paying job
  4.     You can work across industries such as data quality, higher education, sales, financial institutions, marketing, business intelligence, and more!
  5.     Your big data training and SQL skills will put you at the forefront of important decision-making groups in your organization.

Conclusion

In the information era, if you want to carve a place for yourself in the digital world big data training with SQL specialization will be highly beneficial for you.

Supply chain management certification: The key to empowering supply chain professionals

Posted on by Imarticus Learning

Supply chain management in commerce is the process of managing the flow of goods and services, between businesses and locations. A supply chain career involves the storage and movement of raw materials, inventory, and also finished products. It also involves managing order fulfillment from the source of origin to the source of consumption. Read on to know more…

Why is a supply chain management certification required?

With the passage of time, technological innovations, growing population, and ever-increasing demand for commodities, the delivery of goods and services in our global economy have become challenging and increasingly complex. Large businesses and robust organizations are in constant need of supply chain managers for managing the deliveries and other operations related to the supply chain. Therefore, studying a course and pursuing a career in supply chain management is quite a lucrative option. You will learn how to execute the choicest practices in procurement, purchasing, logistics, inventory management, distribution, and compliance.

Notably, supply chain managers and professionals face numerous challenges in business operations such as disruptions, risks, and an increase in market competition. Once you take up a course, you will learn how to effectively minimize the damage incurred because of supply chain disruptions, plan out risk management methods and strategize cost reduction policies. A certification course in supply chain management will give you the skills you need to become a valuable asset to your company in contributing to the effective working of the supply chain.

Benefits of taking up a supply chain management course

Here are some reasons a course in supply chain management can be beneficial to you:

  • Career boost: If you are already an analyst manager or a part of handling logistics, taking up a good course and getting certified as a supply chain professional is bound to give your career opportunities the boost it requires.
  • More knowledge about business operations: Pursuing a career in supply chain management will give you the skill set required for managing a company’s supply chain with ease. Not only will it give you the credentials to get a promotion in your organization, but also the knowledge about dealing with the complexities of the supply chain and how things work for the smooth process of delivering commodities between suppliers and consumers. You will easily be able to troubleshoot any problem and mitigate any risk during manufacturing or distributing goods and services.

 

  • More job opportunities and prospects: An SCM certification will be like a feather on your cap that will give you an edge over different candidates who have no formal training in this field.
  • Help you stay updated: Supply chain management courses will help you keep abreast with the latest developments and ongoing trends in this sector.

What you will learn in a supply chain management course?

 

  • Designing value in SCM: You will learn how to design value in the global supply chain with decision-making tools and make decisions for forecasting and planning through quantitative methods.
  • Enterprise resource planning: You will learn about ERP for the integrated approach, the core structure of ERP-related systems, and different ERP techniques and tools for SCM.
  • Supply chain operations: You will learn about various SCM operations like concepts, conceptual framework, latest trends, technologies, operational measures, operational activities, demand planning, and forecasting. 

Conclusion

A course in SCM will teach you the most important concepts used in strategy, inventory control, and the like. A supply chain analytics course or a professional supply chain management course from Imarticus Learning will take your supply chain career to greater heights. 

Explore strategic operations management and supply chain analytics with a supply chain management certification

Posted on by Imarticus Learning

Supply Chain Management is the process of managing supply and demand, constructing a sustainable architecture, taking advantage of international logistics, generating net value, and evaluating performance throughout the world. It is the management of the complete manufacturing process of a product or service, from raw materials to the end product delivered to the customer.  

Among the many benefits of using supply chain planning and analytics software, is the ability to better anticipate and respond to the client’s needs. Predictive analytics may be used to help companies anticipate consumer demand and as a result, make inventory modifications that save money while also expediting delivery.

End-to-end supply chain analytics necessitates the acquisition of raw materials, production, distribution, and services that are required after the sales. An organization’s supply chain cannot function properly without the seamless integration of the supply chain management platforms. The goal of this kind of integration is to provide data on products at every level of the supply chain with supply chain visibility.

Operations Research

Operations Research is a branch of applied mathematics concerned with quantitative decision-making issues involving the allocation and management of scarce resources. These issues emerge in a variety of contexts, including industrial businesses, investment firms, health care organizations, and governance. An operations research analyst creates and applies computational and scientific models to assist in the resolution of decision-making difficulties.

As with engineers, they formulate and solve problems. Their job includes developing a quantitative representation of the system and analyzing and forecasting the repercussions of alternative modes of operation. Mathematics optimization algorithms, probabilistic and statistical methodologies, research, and simulation software may all be used in the investigation.

Operations Management

Product and service production and distribution processes are meticulously managed by Operations Management professionals. In today’s business world, the efficiency and efficacy of procedures are of paramount importance. Because of this, operations management frequently involves a great deal of internal process monitoring and analysis.

Operations Management professionals get the processed data from the Operations Research analysts and then choose to make a decision based on the data provided to them. They’re in charge of everything from product conception and delivery to inventory control and logistics to production planning and execution.

About the Supply Chain Management and Analytics course from IIT Roorkee

Indian Institute of Technology Roorkee is among the most important engineering institutions in India. Since its inauguration, the institution has played a critical role in the provision of technical expertise and knowledge to the nation, in support of research. The institute is one of the world’s leading technical institutes, and it has had a significant impact on all aspects of technological advancement. 

The DoMS & E-Learning Centre, IIT Roorkee, has devised a comprehensive curriculum along with Imarticus to teach you the fundamentals of supply chain management and analytics. Python’s potential will be used in this course to help you become more data-centric and enhance Supply Chain decision-making. Along with learning the analytics part of the course, you will be also trained in Sales and operations planning.

Conclusion:

It is due to the Operations and Supply Chain Management professionals that are upholding the supply and demand of products in the consumer market. Learn the process of designing supply chain networks yourself and become a successful supply chain professional with the help of the Professional Certification in Supply Chain Management & Analytics course from IIT Roorkee. As a part of this course, you will be able to visit the Roorkee Campus for 3 days and learn there, you will also be working on a Capstone Project with experts in the industry.