How to Become A Supply Chain Professional: A Guide

A supply chain is a system of facilities and transportation channels. It coordinates the purchase of raw materials, their transformation into intermediate and completed goods, and their distribution to the end customers. 

Notably, supply chain management (SCM) is the process of overseeing the flow of products and services – from sourcing raw materials to final products.

As businesses have become more globalised and competitive, the demand for supply chain management professionals has also shot up. 

To become supply chain professionals, aspirants must have specific skills and relevant educational qualifications. This article will address everything you need to know about becoming a supply chain professional. 

What is supply chain management?

At a fundamental level, supply chain management controls the flow of information, materials, and finance for a product or service. It takes care of everything – from purchasing raw materials to delivering the final product to its destination.

The concepts of supply chain and logistics sometimes need clarification. Logistics is a subpart of the supply chain. 

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SCM systems nowadays are digitally created and encompass the handling of materials and software. This happens for all parties involved in creating products and services.

Getting the material, supply chain planning, product lifecycle management, logistics, and order management are all integral steps of a supply chain.

Whether strategic or tactical, material procurement falls under the responsibility of supply chain managers. They keep the inventory under control by keeping track of material availability and when it will be used. Additionally, they plan shipping routes and merge cargo whenever feasible.

Responsibilities of a supply chain manager

A supply chain manager has a wide range of responsibilities. Some of them are listed below:

  • Design the supply chain strategy for the company
  • To spot problems, analyse data from shipping and delivery
  • Keep an eye on logistics to make sure everything goes well
  • Proper upkeep of the records and inventory
  • Find reasonable solutions for supply chain problems
  • Fix any issues that arise (such as delays during delivery or accidents)
  • Oversee and encourage a group of supply chain employees
  • Enhance the effectiveness of the supply chain overall and search for any potential process innovations
  • Build connections with manufacturers, both new and old
  • To achieve sustainability goals, consider the supply chain’s environmental impact

Steps to follow to become a supply chain professional 

There are three ways in which you can become a supply chain professional. They are as follows:

Path 1:

  • Step 1 – Complete your 10+2 level education from any recognised board 
  • Step 2 – Pursue a BBA degree 
  • Step 3 – Pursue an MBA in logistics and supply chain 

Path 2:

  • Step 1 – Complete your 10+2 level education from any recognised board 
  • Step 2 – Pursue a B.Tech or B.E. 
  • Step 3 – Pursue an MBA in logistics 

Path 3: 

  • Step 1 – Complete your 10+2 level education from any recognised board 
  • Step 2 – Pursue a bachelor’s degree in any relevant field 
  • Step 3 – Get certification in supply chain management

Salary of a supply chain professional 

The average salary of a supply chain manager is Rs 13.1 LPA. It ranges between Rs 4.4 LPA and Rs 25 LPA depending on your experience, position, company and workplace. 

Skills necessary for a supply chain professional 

To become a successful supply chain professional, you must work on your skill set and education. Here are some skills and qualities a supply chain professional must have: 

  • Strong communication abilities

These professionals collaborate with several divisions in their organisation and suppliers to get a product manufactured and delivered to customers. Therefore, supply chain specialists must be effective communicators to promote collaboration and cooperation.

  • A strong moral sense

A supply chain professional, like any other business leader, must have a solid moral compass and be able to demonstrate it to peers and business partners. They must be fair to all participants in the supply chain.

  • Dedication to excellence

Customers will not do business with any company that produces inferior quality goods. A supply chain expert must hold his business to the highest standards to ensure success.

  • Time management

Every employee must be able to do tasks quickly and supply chain professionals are no exception. Anyone who works in the supply chain has a full day of commitments. Therefore, they must be skilled at balancing them all.

  • Tech expertise

The supply chain has been dramatically shaped by technology. Professionals in the supply chain must stay up-to-date on new advancements and be prepared to discuss how they could benefit the organisation.

Conclusion 

A professional overseeing the entire supply chain of a company, from raw material sourcing to the final production, is known as a supply chain manager. An essential part of business success, supply chain professionals ensure the continuity of delivery pipelines to meet market demand and enhance sustainability. 

To become a supply chain professional, consider enrolling in the Chief Supply Chain Management course offered by Imarticus in collaboration with IIM Raipur. The 10-month executive certificate programme incorporates live online classes and campus immersion to train professionals for senior supply chain management roles.

How big data Analytics can help in improving Network Security

Every day, the information generated and stored by companies worldwide is increasing in volume and complexity. With this arises a need for tools to protect this vulnerable data from data breaches and exploitation by cybercriminals.

Can big data analytics strengthen network security and save the day? Read on to find out.

What is big data analytics?

Big data refers to highly variable, complex data generated at a high volume and high velocity. The analysis and interpretation of big data are known as big data analytics.

With the boom in digitalisation and cloud computing, hundreds of terabytes of data are generated and utilised daily. Extracting useful information from data of this size is tedious with traditional SQL (Structured Query Language) tools. Here’s where big data analytics comes in. 

Frameworks like Hadoop and databases such as MongoDB, Cassandra, Vertica, and Apache HBase make complex data management quicker and simpler. Searching for specific events within large datasets can be completed by such frameworks in minutes.

Applications of big data analytics

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Big data analytics helps store and maintain data, detect patterns and trends in real-time, predict future events or problems, and nullify abnormal or malicious activities in vast networks.

Some sectors where big data analytics finds wide applications are:

  • Finance and banking
  • Business and e-commerce
  • Public welfare and governance
  • Healthcare 
  • Communication
  • Energy
  • Entertainment and media

Network security and its challenges

Protocols, policies, and practices designed to keep data safe from cyber attacks and security breaches are part of network security. It aims to detect, prevent and nullify misuse, denial of access, unauthorised modification, or data extraction.

Network security uses physical and digital methods to secure confidential and sensitive data. Biometrics, firewalls, VPNs, and sandboxing are various types of network security

As databases expand, monitoring big data with traditional SQL tools becomes unreliable. Downsides of traditional analytics include frequent false positives, cyber attacks, privacy breaches, phishing, malware, and other network security threats.

Other challenges faced by network security are:

  • Breaches at admin or high clearance levels
  • Lack of vigilant monitoring in open network structures
  • Inability to process high amounts of data in real-time
  • Deliberate or accidental breaches by humans or bots
  • Stealing confidential data through hacking or identity theft

How big data analytics can be the solution?

Storage and maintenance of big data

High-volume non-relational data can be collected and stored on NoSQL databases such as Apache HBase, Cassandra, and MongoDB. These databases use platforms like Hadoop to organise variable and unstructured data.

Identification of anomalous patterns in real-time

Employees are responsible for a majority of network security breaches. These leaks may be accidental, but deliberate breaches may be minor and go undetected for long periods.

Big data analytics can monitor real-time changes in regular data flow patterns. Data from sensors monitoring user credentials can detect unauthorised log-ins and reveal botnets or APTs (Advanced Persistent Threats).

Predictive assessment of network integrity

Tools used in big data analytics can perform predictive analysis of the errors that can occur in the system. NoSQL frameworks can reveal the pitfalls in network security, which can then be patched before hackers discover them.

Prompt retrieval of information from networks

A case study by Zion Bancorporation was able to draw a comparison between the processing speed of data by traditional and big data analytics. Traditional SIEM (Security Information and Event Management) tools took 20 minutes to 1 hour to process data collected over a month. It took a Hadoop-based tool only about a minute to do the same. 

This case study shows the ability of NoSQL tools to process data at high speeds that could detect and barricade gaps in network security.

Analysis of datasets to identify past breaches

When breaches in the system occur, companies often fumble to find what exactly caused it. The sheer volume of big data often makes finding a fault in the network a hassle. With big data analytics, a detailed assessment of stored data can be performed within minutes, assuring prompt responses to cyber attacks.

Conclusion

Advancements in big data analytics have the potential to become an indispensable tool in bolstering network security. There is a rising demand for big data analysts to protect and secure sensitive data.

A career in big data analytics and network security is one click away with the advanced certification programme in cybersecurity from IIT Roorkee. The practical approach of this course aims to equip you to deal with cyber attacks and protect vulnerable networks. It is also a comprehensive guide to ethical hacking, social engineering, encryption, and data forensics.

Enrol now to begin an exciting career in cybersecurity today!

What Is the Role of Supply Chain Managers in Sales and Operations Planning?

Sales and operations planning (S&OP) is an inclusive planning procedure aligning a company’s demand, supply and financial planning cohesively. Sales and operations planning is a designed and executed decision-making procedure for the company’s master planning. 

An enterprise’s Supply Chain Managers help produce feasible material in adherence to a steady financial plan and available supply and resources. Since S&OP incorporates a strategic and tactical planning procedure, it provides insights into the shifting demand for resources in different geographies. 

Generally, an S&OP takes a comprehensive planning procedure between 18 and 36 months. In this article, let’s discuss the roles of Supply Chain Managers in sales and operations planning, their benefits and challenges. 

Supply Chain Manager: With Special Emphasis on Supply Chain Management

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Supply chain management primarily deals with the flow of goods and services from processing raw materials to final products. It helps streamline a business’s supply activities and integrate maximum customer value. 

Supply Chain Managers control the purchase of raw materials for production and ensure that the products come at the right time. They also coordinate on storing the raw materials so that no material gets misplaced or damaged in the procedure. 

The goal of a Supply Chain Manager is to enhance and develop the company’s supply chain strategies. He/she strives to improve productivity and efficiency and reduces costs by securing materials of optimal quality and quantity at the lowest price. 

Role of a Supply Chain Manager in Sales and Operations Planning 

A Supply Chain Manager maintains the alignment of daily operations with corporate strategies and closely examines the demand and supply in the department to keep equilibrium. Let’s see what the roles of Supply Chain Managers are in S&OP. 

  • Gathering Data: One of the primary roles of a Supply Chain Manager in an enterprise is to collect forecasts based on the existing predicament with various key variables. The key variables include inventory, recent sales and cash on hand.
  • Planning Demand: Supply Chain Managers help analyse, forecast and influence demands, thus shaping a pattern. As a result, they make an inventory to match the demand for raw materials.
  • Planning Supply: Supply Chain Managers have the role of assessing the production and distribution capacity. They also examine the constraints that might cause an untimely delay.
  • Reconciling Production Plans and Demands: Supply Chain Managers adhere to the demand and production plan alignments and ensure that they meet the company’s financial requirements and objectives. They also make recommendations to the management to ensure the best for their department.
  • Attending Executive Meetings: It is pivotal that a Supply Chain Manager attends executive meetings. He/she generally takes the inputs from the S&OP team, reviews the plan and subsequently makes a final version that would suffice well for the company. 

Benefits of Sales and Operation Planning 

Sales and operations planning comes with many benefits that help integrate the company’s mission and goals through various departments. It does not use a segregated approach but focuses on bringing different pieces of the company together and thus creating an overview. Let’s find out about the benefits of S&OP in the context of Supply Chain Management (SCM).  

  • Resource Optimisation: The S&OP helps in resource optimisation by helping the company know the right amount of materials needed for production. It avoids any circumstances for potential waste and uses the resources to their fullest with maximum efficiency.
  • Collaborative Improvement: S&OP helps in the alignment of the sales department alongside different production goals. It understands the effects of performances and thus improves communications accordingly to enhance efficiency. S&OP does not let production limitations be an issue for seamless collaborations.
  • Profit Maximisation: Once the sales and operations are substantially aligned, the company thus maximises its profits. They help greatly in terms of cost efficiency, lowering storage needs and eliminating complex issues in the supply chain. This would help the management set realistic goals and create a realistic forecast of future budgets. 

Conclusion 

Besides improving forecast accuracy, S&OP is efficient for curtailing inventory costs that boost the working capital. It is also responsible for increasing market revenues and shares by proving its effectiveness to new initiatives and products. 

If you are interested in earning a Certification in Supply Chain Management or SCM, then check out Imarticus Learning’s Professional Certification in Global Supply Chain Management and Analytics. You can make a successful career as a Supply and Operations Planner and learn sales and operations planning and other important aspects of inventory management incorporated in SCM. 

The program is certified by IIT Roorkee. To know more, check out its website right away! 

Everything you should know about k-means clustering

Data is the foundation of the Data Science function. With businesses generating a lot of unstructured data, machine learning frameworks help with handling unlabelled data. The entire domain of Unsupervised Learning under machine learning works on unlabelled data.

As we want to understand how data is organized under machine learning, clustering plays an integral role in this function. Clustering can also handle labelled data apart from handling unlabelled data. As the name suggests, clustering is used for grouping similar data together. One such technique of clustering is known as K-Means Clustering. It is one of the most common forms of clustering.

What is K-Means Clustering?

K-Means Clustering is a type of unsupervised machine learning algorithm used to group similar data points. The goal of the algorithm is to partition a dataset into K clusters, where each cluster contains similar data points. The number of clusters (K) is specified by the user and is one of the inputs to the algorithm.

The algorithm works by first randomly initializing K centroids, which are the centre points of the clusters. The data points are then assigned to the closest centroid based on their Euclidean distance. After all data points have been assigned to a centroid, the centroid positions are recalculated based on the mean position of the data points in the cluster. This process is repeated until the centroid positions no longer change or a maximum number of iterations is reached.

The K-Means Clustering algorithm is sensitive to the initial placement of the centroids, so it’s common to run the algorithm multiple times with different initial centroid positions to ensure that the final clusters are stable.

Use case of K-Means Clustering

K-Means Clustering is a widely used algorithm in many fields, including image and speech recognition, market segmentation, and anomaly detection. It’s also used as a preprocessing step in other machine-learning algorithms.

The key goal of K-Means Clustering is not just to make clusters but to create useful and meaningful clusters. It is critical that the data points in a cluster and closer together and far away from other clusters.

How does K-Means Clustering work?

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The key goal of K-Means Clustering is to find out clusters in the provided dataset. It can either be done by trial and error where the value of K keeps changing till we can create the best clusters. Another method is the elbow technique to find out the value of K. The value from the centroid and the number of centroids are critical for the proper functioning of this method. Accordingly, it uses those points to the corresponding centroid where the distance is the least.

Benefits of K-Means Clustering

There are several benefits of K-Means Clustering. The key advantages of K-Means Clustering are as follows:

Scalability

One of the key advantages of K-Means Clustering is its scalability, it can handle large datasets, and also it’s computationally efficient.

Pattern Identification

It can also be used to identify patterns and structures in the data that are not immediately obvious.

Adaptable

The K-Means Clustering can be adapted to new examples easily. As a result, it is one of the most common methods of handling unlabelled data.

Drawbacks of K-Means Clustering

Along with several advantages, there are some drawbacks of K-Means Clustering. The key drawbacks of K-Means Clustering are as follows:

Size of Clusters

One major drawback of the K-Means Clustering algorithm is that it assumes that the clusters are spherical and equally sized.

Density

K-Means Clustering also assumes that the data points within a cluster are dense, meaning that there are no large gaps between data points. This can lead to poor results if the data does not meet these assumptions.

Specific Knowledge

K-Means Clustering requires us to specify the number of clusters (K) in advance, which can be a challenge when the number of clusters is not known. Also, It’s not guaranteed that the final clusters will be optimal, or even meaningful, for the problem at hand.

It is important to pre-process the data in the case of K-Means Clustering. We need to convert the dataset into numerical values in case data is not already present in the prescribed format before calculations can be performed. We should also understand that applying feature-reduction techniques would improve the speed of the process.

IIT Roorkee Machine Learning Certification Course

The Machine Learning Certification Course from iHUB DivyaSampark at IIT Roorkee and Imarticus Learning can help us learn about K-Means Clustering and other techniques to help us build a data scientist career. The Machine Learning Certification Course from IIT Roorkee covers several modules which help provide an in-depth understanding of various topics in the field of data science and machine learning. The IIT Roorkee Machine Learning Certification Course has been designed by IIT faculty for early and mid-level professionals that provide live training by IIT faculty and campus immersion opportunity at IIT Roorkee.

What are demand forecasting basics and its features

Basics of demand forecasting

Demand forecasting is a term used in business planning to test the future demand for products or services. Depending on this assessment, a company further plans its operations, inventory, and supply chain management. Demand forecasters consider all present or past sales figures, experiences, and knowledge to predict what customers will want in the future.

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It is important to note that generating predictions is only the first step toward demand forecasting success. Success depends upon how the forecasts make informed decisions.

These decisions include product pricing, product direction, hiring, and company expansion. Moreover, the prime focus remains on helping the firm avoid overproduction and underproduction. In a nutshell, demand forecasting helps control all the marketing efforts and aspects of a modern business.

Various techniques or methods help in creating demand forecasts. Some ways include the following:

Collective opinion

Market experiment

Regression analysis

Consumer survey

Time series analysis

Delphi method

Each technique has its features that help in achieving different objectives. Therefore, the technique selection depends on the goals and requirements of the forecast.

Features of demand forecasting

Here are a few key features of the demand forecasting technique. Knowing these features can help choose the suitable method:

Time Horizon

The length of time that a forecast needs bears a great impact on the method of forecasting. When the time horizon increases, the probability of marginal forecasting error decreases.

Level of Detail

The forecasting method depends on the level of detail needed in the data. Also, this data must match the focus of the decision-making unit. For instance, a production or sales planning unit will do demand forecasting at an individual level. In contrast, the corporate planning department will need a high-level total forecast by product categories.

Stability

The method for demand forecasting can vary with stable times and fluctuation. While stable time requires lesser attention, it is difficult to forecast the periods in greater flux as they need lots of attention. In stable conditions, the ongoing patterns are a good option to continue with and put the past patterns into future use.

Data Pattern

Different forecasting method uses different patterns to identify and assume the data. So, it is best to choose a method that fits the need for data and its underlying relationships.

Cost

Improved and accurate forecasting procedure is a costly affair! The expenses also vary from forecasting methods and several costs. So, when a company finalizes its forecasting technique, it should be in sync with the firm’s economy.

Ease of Application

The chosen method must be easy to understand and use. Organizations must be able to interpret the result and use it for maximum benefit.

Other factors that affect Demand Forecast

Apart from these above-mentioned method-led features, several other factors affect the organization’s ability to forecast and predict demand. Let’s explore them here:

Customer factor

Both customers and potential customers affect demand. Of course, happy and loyal customers will stick to the product and generate referrals. But in case the business deals with solving service problems and not with the products, then forecasting becomes tricky!

If the competitors bring a better problem-solving approach, then the past sales data and knowledge become meaningless. Therefore, incorporating the marketing department’s plans into forecasts is a must.

One-offs events

Any single event can skew the historical data. For instance, if an organization’s name features on a TV show or any celebrity endorses it in any event, this one-off event can spike sales for that quarter or year. However, the company cannot consider the same projection in next year’s forecasting.

Future uncertainties

Various subtle changes or happenings can change the entire trajectory of demand. Even if the company has the most accurate predictions, the underlying forces are always at play! From unexpected price regulations or a new competitor to a viral video on social media and a global pandemic, certain uncertainties always loom around businesses!

A Career Option in Supply Chains

With the increasing demand for forecasting among businesses to make critical considerations in supply chain management and other marketing management, there is also a growth in their career options. Several Career Certifications For Supply Chain Professionals are available to help learners gain expertise in this scientific exercise of demand prediction. IIT Roorkee Supply Chain Management Course stands out as the best among them!

Futureproof your career with Imarticus Learning

IIT Roorkee Supply Chain Management Course has a design module that trains candidates with cutting-edge experience handling demand forecasting. This Career Certifications For Supply Chain Professionals equip them with the skills to succeed in high-demand job roles like supply and operations planner, demand planner, supply planner, data scientist, etc.

11 Innovative Ways to Improve Data Visualisation

Organisations gather a huge volume of data each day. This data, in its raw form, is messy and unstructured. It conveys no meaning. One can find patterns and trends only when refined and presented in a certain way. Data visualisation helps in doing so. The process transforms an unrefined and raw dataset into a form that can be understood and provides insightful information. Thus, data visualisation enables data scientists to deliver the story they want to convey using the data.

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As with any form of storytelling, data visualisation requires creativity, design skills and the ability to explain the information most simply and efficiently as possible. Data analysts use several strategies to ensure that their data sends a clear and compelling message. One can learn these strategies as part of data analyst training.

Some of the best ways to improve data visualisation are listed below:

Decide the message you want to convey.

You should clearly know what you want your audience to take away from the visualisation. A dataset means nothing to a viewer until you give it meaning. Once you know what you want your data to stand for, you can design and present it that way.

Clean up and understand your dataset.

A thorough analysis of the dataset is essential before it can be visualised. Clean up your dataset and eliminate elements that do not contribute meaning, particularly when dealing with a large dataset. Only when you reach a conclusion based on the data can you convey it to others.

Choose the best graph type for your data.

Different charts and graphs are used to depict different forms of data. For instance, a line plot is used to show numerical trends over time, a bar graph is used to compare data values within the same category, and a pie chart shows the proportional distribution of data points within a specific category. Depending on your data and what you want to do with it, you can find which graph fits it best.

Keep things simple.

Choose simple charts and structure your data in a way that’s easy to understand. The better you know something, the more simply you can explain it.

Pay extra attention to the text.

You can neither make your visualisation completely text-free nor fill it with redundant text. Your visualisation usually has a title, label and legends. A title needs to be precise and easily understandable. Labels should also be simple and readable. Legends must be thought carefully as they should make understanding graphs easier.

Use colours in the right way.

Colours are an essential component of visualisation. Depending on how you use it, they can either enhance your visualisation or make it confusing. Avoid using too many colours and the same colour for the same data.

Keep your audience in mind.

The type of audience will impact the design of data visualisation. Depending on the audience’s familiarity with the subject, you will decide the level of explanation required and the design elements to be used. For instance, charts for internal stakeholders will look different from those designed for external audiences.

Reduce density.

Don’t try to cram everything on one chart. If there is too much information or too complex, illustrate it using more than one chart. Use whitespaces to help your readers focus and comprehend the information better.

Make use of dynamic visualisation.

Using dynamic visualisation can make your data highly insightful. Interactive visualisations where the viewers can remove some categories of data can help them understand the graph better. Gifs can also be used to depict the same base graph over different periods or conditions to demonstrate changes in the data.

Reduce dimensions.

When several dimensions are shown in the graph depicting different measurements for each data point, it will only confuse the audience. They will find it difficult to find or understand patterns. Thus, use a few dimensions to convey the meaning of your dataset.

Mention the source of the data.

Include the link to the original dataset to make your data visualisation as unbiased and objective as possible. Be honest about the assumptions you made, the data that was omitted and how you reached a particular conclusion.

The skill of data visualisation is essential to learn for data scientists. The data they gather and analyse can be of use to people only if appropriately designed.

At Imarticus Learning, we offer the perfect Data analytics course with placement. Our Postgraduate Program in Data Analytics and Machine Learning focuses on data visualisation with Tableau and PowerBI and other job-relevant skills. All graduates and professionals (up to 5 years of experience) with a technical background are eligible to apply.

Apply now to begin your journey as a data expert.

How does designing the supply chain work

Designing the supply chain is a critical aspect of any business that involves the movement of goods and services from the manufacturer to the end customer. It involves the coordination and management of all the activities and resources required to plan, source, produce, and deliver goods and services to customers.

The value of the global market for supply chain management (SCM) was USD 18.446 billion in 2021. It is forecasted to grow at a CAGR of 10.9% from 2022 to 2030.

The increasing use of technology and new advancements are making the supply chain better and helping the market to grow.

Meanwhile, SCM is a dynamic field. It comes with many challenges for companies to navigate. These challenges include:

Complexity

Volatility

Transparency

Risk management

Sustainability

Cybersecurity

Talent and skill shortages

Cost reduction

Efficiency

In this article, we will take a closer look at the key components of designing an efficient supply chain. It also covers how to become a certified supply chain analyst and future-proof your career.

How to design an efficient supply chain management?

Designing the supply chain involves understanding and optimising the flow of goods and services from the manufacturer to the end user. It encompasses various activities such as:

Planning

This includes forecasting demand, determining production schedules, and setting inventory levels.

Sourcing

This involves procuring raw materials, components, and finished goods from suppliers.

Manufacturing

This includes the production of goods and services.

Logistics

This contains the movement of goods and materials from one location to another. It also concerns itself with the coordination of transportation and storage.

Inventory management

This covers managing the flow of goods and materials in the supply chain. This includes the storage and distribution of inventory.

Distribution

This comprises the physical delivery of goods and materials to customers. As well as the coordination of returns, and reverse logistics.

Customer service

This includes addressing customer complaints, tracking orders, and handling returns.

An effective supply chain design requires balancing these different activities in a timely, cost-effective, and efficient manner.

Overall, a successful SCM design involves ongoing analysis, optimization, and adaptation to changing market conditions. Businesses must be willing to invest in technology, data analytics, and employee training to stay competitive. That’s the only way to meet the demands of their customers.

To delve deeper into how to design an efficient SCM, you can explore a supply chain management course with analytics certified by IIT Roorkee.

Features of supply chain management course with analytics

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Imarticus Learning is offering a supply chain management course with analytics certified by IIT Roorkee. The highlights of the course are as follows:

Curriculum design

This cutting-edge curriculum, designed by the IIT Roorkee, will give you a comprehensive understanding of SCM and analytical skills. These skills include problem-solving, technical, communication, project management, technology, and many more.

Through the use of Python, you will learn how to analyse data. And you will be able to make better supply chain decisions.

Pedagogy

Learn from the best faculties in India through interactive online sessions and discussions.

Led by the IIT faculty, you will gain a solid foundation in the key concepts of SCM. The industry experts will provide practical learning for SCM processes, tools, and techniques.

Campus immersion

Get a chance to participate in a 3-day campus immersion module at IIT Roorkee. You can interact with peers and learn from the IIT faculty in person.

You can also visit the Delhi or Noida campus. Here, you will work on a capstone project under the guidance of industrial mentors.

Experiential learning

Tackle complex, real-world problems in the SCM industry through projects. And create a portfolio of your projects to showcase to future employers.

Combination of SCM and big data

Modern supply chains generate a large amount of data, often referred to as big data. Build strategic global supply chains of the future by combining traditional supply chain techniques with data-centric decision-making.

To enrol for this course, you must have:

Basic understanding of Operations Management and Programming.

Interest in learning SCM.

Why choose Imarticus Learning?

With the increasing trend of e-commerce, there has been a significant increase in the movement of goods. This results in a corresponding rise in the number of job opportunities. Especially, for those skilled in SCM.

The SCM program offered by Imarticus Learning helps you capitalise on this opportunity. It also helps you advance your career in the field. Click here to know more about the course. Or you can get in touch with a representative.

Imarticus Learning is a global premier ed-tech platform. They specialise in technology-driven training. Moreover, they have a decade of experience in the industry. They provide career-enhancing education across several key industries. Some of them are banking and finance, data science and analytics, new technology, and digital marketing.

Power BI: Get started with Python to automate tasks

Showcasing the effectiveness of the data analytics course and machine learning, is the effectiveness of the participants at using Python for automation of tasks

While automation as a strategy is well appreciated, it is necessary for a working professional to learn the necessary skills to undertake implementation. Automation can be achieved in varying degrees by a person depending on his skills. While there are some rudimentary methods to automate tasks, the best way to automate is to go through a learning process through which one acquires effective automation skills. This is where it becomes important to learn python. Those who learn python develop simple coding skills that are very effective at helping themselves automate unproductive tasks when using Power BI. 

Automation with Python: An integral part of the curriculum

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A working executive who seeks to upskill himself and sets himself the ambitious task of acquiring a leadership mindset in data science should go through a well-built curriculum. The inclusion of a Python Certification Course and the automation techniques is just one way of adjudging if a skill development course covers the essentials. 

Learning on the job, without going through a data analytics course, has many disadvantages. Learning on the job limits you to the exposure available in the immediate environment. The knowledge of one’s colleagues on python may not be very wide either. In such a situation, being a self-learner means that there is no one who researches the needs of today and tells you what to learn. Even if you do come to know what should be learned in data analytics and machine learning, you may not get the right tools, and teachers to guide you through the process. 

A structured data analytics course curriculum can accelerate one’s learning in Python, SQL, Data Analytics, Machine Learning, and Data visualization

Advantages of Python

Learn python to empower yourself in the world of technology. It is one of the latest, most effective languages. A Python Certification Course has several advantages that have resulted in it being popular. Here are some reasons:

  • It is designed to be a readable language as it uses English much more than punctuation. 
  • Python is also used widely in web development, data analytics, and machine learning.
  • It is an open-source programming language. 
  • Many programming languages have become obsolete but Python is popular and all the tools that make it easy to use are available easily. 
  • Python communities are big and anytime one comes across any hurdle, and after having done a python certification course one can reach out to these and resolve the problems.
  • Libraries for python ensure that a software team can focus on their core goals. 

Data science and machine learning are becoming an integral part of business

A data analytics course is one of the best ways to upskill. Data science is one of the key pillars of technology-driven businesses and big firms. Data collection, management, assessment, and usage result in massive results for a company. Data science impacts sales, user base, suppliers, hiring, marketing, and overall success of the business.

Python skills form an integral part of technology and learning it well can substantially help in automating many tedious tasks and make you more productive. Thereby, giving you an edge over those who are still learning it. 

The Ultimate Guide To Learning Python Online

The Ultimate Guide To Learning Python Online

Are you looking to make a career as a web developer or a programmer? If yes, it is imperative that you should have a thorough knowledge of programming languages. We have heard about different programming languages and Python is one of them. As per reports from The Economist, it is estimated that Python is going to become the most popular programming language in the world sometime soon. 

best big data analytics course

This means that more websites will use Python for web development. It will become a preferred programming language with developers too. Quite obviously, job openings in Python will also increase in the industry. In such a situation, it becomes inevitable to learn Python. Various kinds of tutorials and training programmes are now available online, with which you can develop a basic idea about Python. 

Understanding What is Python

While going through a Python tutorial, the first thing that you will learn is what is Python in the first place. 

Python is a general-purpose, object-oriented, but a high-purpose programming language. Along with web development, you can use this programming language for other kinds of programming and software development. Mentioned below are some development areas where Python is really helpful:

  • Software development and desktop app
  • Writing system scripts (instructions for telling the computer to “do” something)
  • Mobile app development and back-end web
  • Performing mathematical computations and processing big data

The majority of beginners in the programming and development field now prefer learning Python as their first programming language because of its versatility and simplicity. The Python community is also quite strong and supportive and keeps up with increased popularity. 

Getting Started with Python

If you are a newbie to programming with no knowledge of algorithms or coding, do not worry. You will have no problems learning Python as the programming language comes with a simple and easy-to-use syntax. 

At the very beginning of Python training, we will learn how to install Python and run it on systems. The majority of Mac and Linux systems have Python pre-installed in them. However, it is important to check if the latest version is present or not. 

  • Running Python by using Thonny IDE

This is the easiest way of running Python as Thonny IDE comes with the latest and updated version of the language packaged in it. You, therefore, need not install Python separately. 

  • Running Python without Thonny IDE

You can also install and run Python on your system without using Thonny IDE by following the steps below:

  • Download Python’s latest version
  • Now, run the provided installer file. You have to keep on following the steps as they instruct you to install Python. During the process of installation, check on ‘Add Python to Environment Variables’. With this feature, you will be able to run Python from any segment of the system 
  • You have the option of choosing the path where Python will be installed in the system
  • On completion of the installation process, you can run Python

Running Python

You can run Python in different ways. We will discuss them below:

  • Running Python in Immediate Mode

Once you install Python, in the command line type ‘python’. You will notice that the interpreter will invoke in immediate mode. You can type in Python code directly. You have to press Enter to get output. 

For instance, try typing ‘1+1’. Now press Enter. You will get ‘2’ as output. For exiting the mode, type ‘quit ()’ followed by pressing Enter.

  • Running Python in Integrated Development Environment (IDE)

For writing a Python script file, you can use any text editing software. You have to save it with a ‘.py’ extension. With the use of IDE, coding becomes easier and more convenient. 

IDE is a piece of software that offers useful features to the programmer for application development. The features include syntax checking and highlighting, code hinting, file explorers, etc. 

Mandatory Tools for Python Development

  • Integrated Development Environments (IDEs)

Before you start learning Python, you must choose an IDE and set it up. You will write all your Python code here. You can choose an IDE independently and install it.

  • Test Runner

When you are a beginner, you would want to learn the basics of the language and not be much interested in testing. But, test running is important to ensure that all elements of the program are working properly. In Python, developers use Nose, Unit Test, or Pytest frameworks/libraries for testing. 

  • Debugger

Post-coding, bugs will be there. But, the important thing is to identify them as soon as possible and resolve them. There are many debuggers, which you can use in Python. 

Conclusion

Learning Python online is the most convenient thing. You can also join a Python class at Imarticus Learning to hone your skills in the programming language. The interactive tutorials online also help in learning the language with solved examples and many mock questions. 

How to become an expert in data visualisation

Data visualization is quickly becoming an essential skill for anyone looking to break into the data science industry. With the increasing amount of data collected and analyzed, a highly competent understanding of data visualization tools and techniques can significantly benefit any organization. But how does one become an expert in data visualization? In this blog post, we will explore the key steps needed to become an expert in this field, from mastering the fundamentals to exploring advanced concepts such as machine learning and artificial intelligence. Read on to learn more about how you can become an expert in data visualization.

Importance of making a career in data visualization

Data Analytics

Data visualization is not only about making pretty pictures. It’s about conveying information in a way that is easy for people to understand. When done well, data visualizations can make complex ideas digestible and help people see the world in new ways.

A career in data visualization offers the opportunity to make a real difference in how people understand the world around them. As a data visualization expert, you’ll be able to take data sets and turn them into visually-appealing and informative graphics that tell a story. You’ll help people see the world in new ways and make complex concepts more understandable.

There is a growing demand for skilled data visualization professionals as more and more organisations recognize the power of data visualization to communicate information effectively. A career in data visualization can offer job security, good earning potential, and the opportunity to make a real difference in the world.

Understanding data and its structure for perfection:

Data visualization is creating and manipulating visual representations of data to help people understand it better. It is a powerful tool for exploring and understanding large data sets and can be used to answer questions, find patterns, and support decision-making.

There are many different data visualizations, each with its strengths and weaknesses. The type of visualization you use will depend on the kind of data you have, the question you want to answer, and your audience.

Choosing the right tool for data visualization

Here are some things to consider when selecting a tool:

– The type of data you are working with: Some tools are better suited for specific data types than others. For example, a tool like Excel might be a good choice if you have a lot of numerical data. A tool like Tableau might be better if you have more categorical data.

– The level of complexity: Some tools are better suited for simple visualizations, while others can handle more complex ones. If you need clarification on what you need, it might be worth starting with a more straightforward tool and then moving on to something more complex.

– Your budget: There are both free and paid options available, so you’ll need to decide what you’re willing to spend. Remember that even the free options can require a significant investment of time to learn how to use them properly.

How can a data analytics course help you keep up with industry developments?

Data analytics is an integral part of keeping up with industry developments. By taking a data analytics course, you can learn how to collect and analyze data effectively. This will help you decide where to focus your efforts and how to improve your products or services. In addition, a data analytics course can also help you keep abreast of new developments in the industry so that you can be prepared for changes that may impact your business.

Learn and Grow with Imarticus Learning:

The Data Analytics course at Imarticus Learning intricately focuses on providing an integrated learning experience. With one-of-its-kind career support services, our programs are built to support students as they take a step into their careers with a new perspective. While you become an expert in Data Analytics, you also get access to over 500+ placement partners and explore unlimited opportunities.

This is a full-time 6-month program on weekdays for fresh graduates or early career professionals.

Upon completion, students earn a Postgraduate Program in Data Science and Analytics certificate that adds considerable value to their professional credentials.

Course USPs:

The career services team can help you find a new job with data analytics and machine learning skills.

Redesign your resume professionally and highlight your strengths in the best possible way.

Prepare students for interviews and unique build training module.

Dedicated & Expert mentors for curriculum to ensure up-to-date academic learning

National-level data science competition to showcase data science skills for real-world applications

For further details, contact the Live Chat Support system or visit one of our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.