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Category: Management

Tableau for Advanced Data Analytics and Data Visualization: Examples

Posted on by Imarticus Learning

Tableau is an extremely popular data visualization tool. This tool has been rapidly adopted by the Business Intelligence sector and is being used for a variety of applications. The main task of Tableau is to simplify raw data in a straightforward and comprehensive manner. 

Companies are adopting Tabaleu because it can create simple data that is cognisable for professionals at every level. It is also useful for non-technical users as it allows them to make their dashboards and worksheets. These dashboards can be customised at any time. 

Normally the Tableau tool does not require any complicated codes to operate. When this tool collaborates with data analytics, they transmute various text information into visual forms at a very high pace. This process of visual rendering is often termed data visualization with Tableau.  

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With the assistance of an excellent machine learning certification course, one can learn more about Tableau. It will also allow an individual to create a strong career in data science. 

Let’s drive in this article to learn about Tableau, data analytics, and data visualization with help of examples.

 What is Advanced Data Analytics?

Advanced data analytics is a group of advanced procedures that allows any venture to foresee future patterns and trends. This technique can easily predict future patterns and trends by deeply analysing the pieces of information and data of their potential customers. 

Various techniques like machine learning, data mining, visualization, and pattern matching all fall under the umbrella of advanced data analytics. It also uses various analysis methods to function properly. These analysis methods include cluster, semantic as well as sentiment analysis. 

Advanced data analytics provides small and big businesses with data insights and well-organised annual plans. It also makes better business decisions than human beings. Frauds are quite common in any venture therefore advanced data analytics is designed to reduce business risks as well. It also keeps a check on any future threads. 

Advanced Analytics Projects and Tableau 

Data Scientists are utilising Tableau’s tools for completing advanced analytics projects in a short period. Tableau’s predictive tool is used to complete these advanced projects. 

There are various ways by which Tableau is used for finishing off an advanced analytics project. These ways of using Tableau are mentioned below:

Predicting or forecasting is one of the essential capacities that Tableau possesses. This is because it was designed with several predictive technologies. Professionals use this particular technology to figure out inactive threats or variables. 

Besides, Tableau allows foreseeing a statistical graph by simply adding any data or trend line to it. It also allows one to select these predictions and drag them to a new graph with the help of a right click. 

  • Segmentation 

The full form of segmentation is drag-and-drop segmentation. With help of this tool, Tableau can easily boost the cohort analysis and intuitive flow. 

This tool will help to build a dashboard on any subject. It will contain all details regarding that subject. 

  • Intricate Calculation 

One can make wrong calculations and manipulate complicated data while working with Tableau. This is because it has a strong calculation programme that improves any wrong analysis. 

Level of Detail (LOD) Expressions and Table Calculations are two characteristics of Tableau that help to enhance any wrong analysis or calculation. With this technique, it is easier to calculate logical problems, arithmetic sums as well as specific operations. Hence, Tableau makes any advanced analytics project a lot easier. 

  • What-If Analysis 

Test scenes can be altered by simply linking Tableau’s front end with its strong input capacity. It also allows a user to alter any calculation pretty quickly. 

What-If Analysis also allows a user to change filters and select data from the dashboard. It also permits one to generate an interactive report. 

  • R Integration

R integration allows Tableau to access any function that is present in the R data. Tableau can also send data to R by linking to the Rserve process. It also allows changing of any model that is made in R with the assistance of Tableau. 

These are the procedures by which Tableau can help any venture to complete advanced analytics projects rapidly.

A real-life example of the usage of Tableau 

Walmart is one of the largest American retail ventures that use Tableau. They use it to collect various analytics from their customers. Many companies purchase Tableau to collect information like customer information, sources, implementation of the law, IT-related information, and more about industries. 

Many other companies have purchased Tableau for Analytics purposes. They are as follows:

  • Amazon is an American retail organization that gives employment to many across the globe. It is one of the most famous customers of Tableau. 
  • CVS Health Corporation is an American Healthcare company that also used Tableau. 
  • European companies are also the popular purchaser of Tableau. The English gas company BP has purchased Tableau for further enhancement in their business. 
  • Apple Ireland, an Irish manufacturing company has also purchased Tableau. 

All these companies earn billions and have given employment to many. To improve the infrastructure, these companies have bought Tableau. There are many other companies around the globe that have also purchased Tableau for running their venture smoothly. 

Conclusion 

Data Visualisation with Tableau is fairly a new concept. Professionals are still grasping it. Thus, before initiating a career in data science one must learn data mining, one of the main components of Tableau. 

Imarticus Learning has brought an IIT data science course for those who are willing to commence a career as a data science professional. This online course will cover every aspect of data visualisation with Tableau and will also incorporate vivid knowledge about Neural Networks. 

This online data science training course is a collaboration between Imarticus Learning and IIT. Therefore, the top-notch faculty of IIT will teach the learners with extreme dedication. To grab this opportunity, get yourself enrolled in this course without any further delay. 

Disaster Response and Recovery in Supply Chain Management

Posted on by Imarticus Learning

In today’s world, supply chain disruptions caused by natural disasters, political instability, and pandemics are becoming more frequent, emphasising the need for a robust disaster response and recovery plan in supply chain management.

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Professionals with a certification in supply chain management, mainly those trained in supply and operations planning, play a crucial role in ensuring that businesses are prepared for potential disruptions and can recover quickly. 

With their expertise, they can help organisations navigate challenges and minimise the impact of disasters on supply chain operations.

Importance of Disaster Preparedness in Supply Chain Management

Supply chain disruptions caused by natural disasters, geopolitical events, and other unexpected circumstances can significantly impact businesses, causing delays, shortages, and loss of revenue. 

Some of the importance of disaster preparedness in the supply chain is as follows:

Risk Mitigation

Disaster preparedness aims to reduce the risk of supply chain disruptions caused by natural disasters, accidents, or other unexpected events.

This includes identifying potential risks and developing strategies to mitigate them, such as:

  • Conducting risk assessments and mapping out potential supply chain disruptions
  • Identifying critical suppliers and developing backup plans for alternative sourcing
  • Creating redundant systems to ensure continuity of operations in case of a disruption

Cost Savings

Preparing for disasters can save supply chain managers high costs in the long run. This includes:

  • Avoiding stockouts and lost sales due to disruptions
  • Minimising costs associated with rush shipping or expediting production to catch up on lost time
  • Reducing the need for emergency response services or additional labour to handle disruptions.

Reputation Management

Supply chain disruptions can also damage a company’s reputation, particularly if it cannot meet customer demands during or after a disaster. By being prepared, companies can:

  • Maintain a positive image by ensuring business continuity and customer satisfaction
  • Demonstrate their commitment to sustainability and corporate social responsibility by minimising the environmental impact of disruptions.

Key Elements of an Effective Disaster Response Plan for Supply Chains

An effective disaster response plan is essential for minimising supply chain disruptions caused by natural disasters, accidents, or other unexpected events. 

Here are some key elements that businesses should include in a comprehensive disaster response plan for supply chains:

  • Risk Assessment: A risk assessment should identify potential risks and vulnerabilities in the supply chain network. This includes mapping out potential supply chain disruptions, identifying critical suppliers, and assessing the impact of disruptions on business operations. 
  • Business Continuity Plan: A business continuity plan should ensure critical business functions can continue during a supply chain disruption. It includes identifying alternative sourcing strategies, developing redundancy plans, and establishing communication protocols and emergency response procedures for all stakeholders in the supply chain. 
  • Technology: Technology can be critical in disaster response efforts, including real-time supply chain operations monitoring and data analytics to identify potential disruptions. 

By including these key elements in a comprehensive disaster response plan, supply chain managers can be well-prepared to navigate unexpected events and minimise the impact of disruptions on business operations.

Role of Technology in Supply Chain Disaster Recovery

Technology is critical in supply chain disaster efforts by enabling real-time monitoring, data analytics, and supplier communication. Here are some specific ways technology can support disaster recovery in the supply chain:

Real-time Monitoring: Technology enables real-time monitoring of supply chain operations, helping managers quickly identify disruptions such as weather conditions and traffic patterns. This allows for prompt action to minimise the impact on the supply chain.

Data Analytics: Data analytics can identify potential disruptions in the supply chain, allowing managers to adjust their operations proactively and avoid potential issues, such as changes in demand or disruptions in logistics.

Communication: Cloud-based supply chain management systems use technology to communicate with suppliers, track shipments, and manage inventory levels. Real-time visibility into operations allows managers to make informed decisions and respond promptly to potential disruptions.

Automation: Automation is a valuable tool for streamlining supply chain operations and minimising disruptions caused by human error. Businesses can achieve it through automated warehousing systems, autonomous vehicles, and other technologies that enhance speed and efficiency.

By leveraging technology in these ways, supply chain managers can improve their disaster recovery efforts and ensure that their operations are resilient to potential disruptions.

Best Practices for Managing Supply Chain Disruptions During a Disaster

Disasters such as natural calamities, cyber-attacks, and geopolitical issues can cause significant disruptions to global supply chain networks. 

Here are some best practices for managing supply chain disruptions during a disaster:

Collaboration

Collaboration is key to managing supply chain disruptions during a disaster. Supply chain managers should work closely with suppliers and customers to identify alternative sourcing strategies and develop contingency plans.

Redundancy

Redundancy is critical to ensuring business continuity during a disaster. Supply chain managers should identify critical suppliers and establish redundancy plans to ensure that essential products and services can continue to be delivered.

Flexibility

Flexibility is essential during a disaster, as disruptions occur quickly and unexpectedly. Supply chain managers should adjust their plans and operations to respond to the changing situation.

Risk Assessment

A risk assessment should identify potential risks and vulnerabilities in the supply chain network. It includes mapping out potential supply chain disruptions, identifying critical suppliers, and assessing the impact of disruptions on business operations.

Contingency Planning

Companies should develop a contingency plan to ensure critical business functions can continue during a supply chain disruption. 

Contingency planning includes identifying alternative sourcing strategies, developing redundancy plans, and establishing communication protocols and emergency response procedures for all stakeholders in the supply chain.

Testing and Training

Regular testing and training ensure that the disaster response plan is effective and that all stakeholders respond to potential disruptions. 

Testing and training include conducting tabletop exercises, reviewing emergency response procedures, and providing training to supply chain employees.

Case Studies: Lessons Learned from Supply Chain Disasters and Recovery Efforts

There have been several high-profile supply chain disasters over the years, which have provided valuable lessons on how to manage supply chain disruptions and recovery efforts. Here are a few examples: 

The 2011 Thailand floods disrupted the hard disk drive supply, causing a global market shortage. Companies that had diversified their suppliers were better able to weather the disruption.

Companies that relied on a single supplier scrambled for alternatives and paid a premium for scarce inventory. The floods highlighted the importance of supply chain risk management and the need for redundancy.

The 2011 earthquake and tsunami in Japan disrupted the supply of electronic components, semiconductors, and automotive parts. Companies with a detailed understanding of their supply chain networks were better able to respond to the disruption.

Many companies implemented contingency plans and alternative sourcing strategies to minimise the impact of the disruption. The disaster highlighted the importance of supply chain visibility and the need for agile response capabilities.

Conclusion

Effective disaster preparedness and management are crucial for maintaining a resilient and efficient supply chain. With the help of technology and automation, supply chain managers can better monitor and adjust their operations, minimise the impact of disruptions, and prepare for potential disasters. 

Data analytics can provide valuable insights for identifying weaknesses and improving processes.

If you want to advance your supply chain management and analytics career, consider enrolling in Imarticus Learning’s Professional Certification in Supply Chain Management & Analytics.

This program provides a comprehensive understanding of supply chain management concepts and tools, including the role of a Supply and Operations Planner. Visit Imarticus Learning for more information.

Benefits of Data-Driven Decisions in Supply Chain Management

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One of the major attributes driving business success is customer satisfaction. With proper data in hand, companies can work on their supply chain management strategy to cater to customer demands and combat delivery inefficiencies. Supply chain as well as procurement process management produces huge volumes of data. It is important for leaders to adopt the right approach to using valuable data for improved operations. 

Supply chain leaders like the Chief Supply Chain and Operations Officer uses Machine Learning and AI models for improving supply chain operations. Automating a data pipeline is also of great help. A data-driven supply chain provides higher agility and greater productivity, particularly when there are chances of disruptions. Professionals who have completed a supply chain management certification course deal with this subject better. 

Understanding data-driven supply chain management

Data-driven supply chain management includes data usage strategically for better prediction of inventory and production changes, which is closer to real-time. This directly plays a crucial role in quicker decision-making. 

When it comes to a data-driven approach to devising a supply chain management strategy, primarily there is the use of new data sources like AI or ML technologies for making predictions. Data-driven supply chains provide a complete and vivid picture of the entire supply chain performance. 

Benefits of data-driven decisions in supply chain management

As a Chief Supply Chain and Operations Officer, you cannot undermine the importance and benefits of data-driven decisions in supply chain management. Let us look at some of these benefits:

  • Actionable insights

With the use of data-driven decisions within the supply chain, businesses can gain insights into the demands of various products and materials. This helps in getting more accurate forecasts. With access to data in real-time, organisations are in a position to adjust to demand fluctuations or emerging trends. This opens up opportunities in generating more revenue. 

Businesses also get an insight into the logistical capacity requirements thus dealing with inventory undersupply or oversupply issues successfully. Data-driven decisions help in meeting a balance between demand and supply, leading to cost-savings and more importantly, enhanced customer experience. 

  • Improved accountability and end-to-end visibility

When businesses implement data-driven decisions in devising strategies for supply chain management, they have improved accountability of the whole work. Moreover, there is a detailed record of every step in the supply management chain offering end-to-end visibility. 

Transparency in various operations is a vital point of a sustainable supply chain. With real-time data-driven decisions, organisations have complete transparency, which proves to be useful for all stakeholders. Transparency is inclusive of the due diligence measures in supplier appointments, identifying procurement sources as part of procurement process management and the metrics used for calculating carbon emissions. 

  • Improved inventory and logistics management

Inventory and logistics management are integral parts of the supply chain management process. With data-driven decisions, vendors and suppliers have immediate updates if there are any kinds of order backlogs. Along with attending to the backlogs immediately, businesses can devise strategies to adjust the inventory management process.  

The logistics department also benefits from data-driven decisions as they receive real-time information regarding their consignments, different cargo batches and updated delivery status of each consignment. With enhanced operational management, you can track your goods conveniently. The final result is a happy and satisfied customer. 

  • Better planning capacity

Every business wants to expand and for that proper planning is a prerequisite. Data-driven decisions in supply chain management provide valuable insights into emerging trends and the demand for a product. This proves to be highly useful for future project planning and devising and implementing effective business strategies. 

For instance, in a manufacturing company, real-time data and visibility offer valuable insights into various things including manufacturing inefficiencies, production volumes, various challenges related to raw material sourcing, etc. When businesses get more accurate information from data, they can have better planning. With planning, a business gets a competitive edge over others. 

  • Cost-savings

Data-driven decisions help in saving good amounts of money in supply chain operations. This is done by making sure that the demands for raw materials and related products are fulfilled timely. This is important so that there is no delay in the production plants or in the assembly lines. Supply chain operations can be made cost-effective if the products move on the quickest routes in the shortest time span. 

With necessary insights and transparent visibility from data-driven decisions, supply chain executives are successful in addressing various challenges in the supply chain.  Some of these include boosting profitability, reducing wastage and increasing operational efficiencies. 

  • Enhanced customer experience

Whether it is procurement process management or supply chain management, the main aim of any business is to provide the best customer experience. A Chief Supply Chain and Operations Officer implements data-driven decisions for enhanced customer experience. The majority of customers have a positive and smooth experience, making them happy customers. 

With real-time data, officers and managers monitor and analyse supply chain operations closely. They can work on ways to increase accuracy and minimise waiting times for product deliveries to customers. As a result, customers receive their orders on or before time and in good condition. 

Conclusion

Businesses are realising the value and importance of data and data-driven decisions in supply chain management and procurement process management. Right from making improved data-driven decisions for a particular supply chain management strategy to enhancing end-to-end operations, businesses are leveraging data in the best possible manner. 

If you are interested in making a career in the supply chain industry as a Chief Supply Chain and Operations Officer, a supply chain management certification course will be of great help. You can take up an IIM supply chain management programme and become a leader in the supply chain and operations industry. 

Imarticus Learning in collaboration with IIM Raipur offers an Executive Certificate Programme for Global Chief Supply Chain and Operations Officer. This 10-month programme targets senior supply chain professionals and helps them in acquiring operational, technological, strategic and personal skills for carving a niche in the supply chain industry. The study programme has six modules, covering various critical features of supply chain management and operations. 

Why is Noise Removal Important for Datasets?

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Noisy data in datasets impact the prediction of meaningful information. Studies stand evidence that noise in datasets leads to poor prediction results and decreased classification accuracy. Noise impacts algorithms in missing out patterns in any dataset. To be precise, noisy data is equivalent to meaningless data. 

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When you learn data mining, you get to know about data cleaning. Removing noisy data is an integral part of data cleaning as noise hampers data analysis significantly. Improper data collection processes often lead to low-level data errors. Also, irrelevant or partially relevant data objects might hinder data analysis. For enhancing data analysis, all such sources are considered noise.  

In data science training, you will learn the skills of removing noise from datasets. One such method is data visualisation with tableau. Neural networks are also quite efficient in handling noisy data. 

Effective ways of managing and removing noisy data from datasets

You must have heard the term ‘data smoothing’. It implies managing and removing noise from datasets. Let us look at some effective ways of managing and removing noisy data from datasets:

  • Regression

There are innumerable instances where the dataset contains a huge volume of unnecessary data. Regression helps in handling such data and smoothens it to quite an extent. For the purpose of analysis, regression helps in deciding the suitable variable. There are two variables in regression, which are as follows:

  • Linear Regression 

Linear regression deals with finding the best line for fitting between two variables so that one is used for predicting the other. 

  • Multiple Linear Regression

There is the involvement of two or more variables in multiple linear regression. By using regression, you can easily find a mathematical equation for fitting into the data. This helps in smoothing out the noise successfully to quite an extent. 

  • Binning

When you learn data mining, you will surely learn about binning. It is one of the best and most effective ways of handling noisy data in datasets. In binning, you can sort the data. You can then partition this data into bins of equal frequency. You can replace the sorted noisy data with bin boundary, bin mean or bin median methods.

Let us look at the three popular methods of binning for smoothing data:

  • Bin median method for data smoothing

In this data smoothing method, the median value replaces the existing values that are taken in the bin. 

  • Bin mean method for data smoothing

The mean value of the values in the bin replaces the actual value in the bin in this data smoothing process. 

  • Bin boundary method data smoothing

In this data smoothing method, the maximum and minimum values in the bin values are then replaced by the boundary value that is closest.

  • Outlier Analysis

Outliers are detected by clustering. It is evident from the name that close or similar values are organised in clusters or in the same groups. The values which do not fit into the cluster or fall apart are considered outliers or noise. 

However, outliers provide important information and should not be neglected. They are extreme values which deviate from other data observations. They might be indicative of novelty, experimental errors or even measurement variability. 

To be precise, an outlier is considered an observation which diverges from a sample’s overall pattern. Outliers are of different kinds. Some of the most common kinds are as follows:

  • Point outliers

These are single data points, which rest away quite far from the rest of the distribution.  

  • Univariate outliers

These outliers are found when you look at value distributions in a single feature space. 

  • Multivariate outliers

These outliers are found in an n-dimensional space containing n-features. The human brain finds it very difficult to decipher the various distributions in n-dimensional spaces. To understand these outliers, we have to train a model to do the work for us. 

  • Collective outliers

Collective outliers might be subsets of various novelties in data. For instance, it can be a signal indicating the discovery of any new or unique phenomena. 

  • Contextual outliers 

Contextual outliers are strong noises in datasets. Examples to illustrate this include punctuation symbols in text analysis or background noise signals while handling speech recognition. 

  • Clustering 

Clustering is one of the most commonly used ways for noise removal from datasets. In data science training, you will learn how to find outliers and also the skills of grouping data effectively. This way of noise removal is mainly used in unsupervised learning. 

  • Using neural networks

Another effective way of removing noise from datasets is by using neural networks. A neural network is an integral part of Artificial Intelligence (AI) and a subset of Machine Learning, in which computers are taught to process data inspired by the human brain. It is a kind of Machine Learning process known as Deep Learning where interconnected nodes are used in a layered structure for analysing data. 

  • Data visualisation with tableau

Tableau is a data processing programme which creates dynamic charts and graphs for visualising data in a professional, clean and organised manner. While removing noise from datasets, this programme proves to be truly effective. Clear identification of data is possible with data visualisation with tableau. 

Conclusion

Almost all industries are implementing Artificial Intelligence (AI), Machine Learning (ML) and Data Science tools and techniques in their works. All these technologies work with huge volumes of data, using the most valuable ones for improved decision-making and forecasting trends. Noise removal techniques help in removing unimportant and useless data from datasets to make them more valuable. 

If you are looking to make a career in data science, you can enrol for an IIT data science course from IIT Roorkee. You can also go for a Machine Learning certification course in conjunction with a data science programme. 

Imarticus Learning is your one-stop destination when you are seeking a Certificate Programme in Data Science and Machine Learning. Created with iHub DivyaSampark@IIT Roorkee, this programme enables data-driven informed decision-making using various data science skills. With the 5-month course, learn the fundamentals of Machine Learning and data science along with data mining. Acclaimed IIT faculty members conduct the course. Upon completion of the programme, you can make a career as a Data Analyst, Business Analyst, Data Scientist, Data Analytics Consultant, etc. 

Enrol for the course right away!

Why Supply Chain Analytics Matthers in SCM

Posted on by Imarticus Learning

Supply Chain Management (SCM) is a complex and critical process that involves planning, executing and controlling the flow of goods and services from the point of origin to the point of consumption. 

One of the most complex challenges in SCM is reconciling supply and demand, to which sales and operation planning provides a solution. However, successful sales and operation planning implementation demands a deep understanding of the supply chain processes and data. This is where supply chain analytics becomes pivotal, adding insight and innovation to optimise SCM operations. 

This article will attempt to explore the importance of supply chain analytics in SCM. Read on to learn more.

What is Supply Chain Analytics?

supply chain management courses

Supply chain analytics is the practice of using data analysis and business intelligence tools to gain insights into the performance of a supply chain. It involves collecting and analysing data from various sources such as suppliers, manufacturers, logistics providers and customers.

By utilising supply chain analytics, companies gain unprecedented visibility into their supply chain operations, allowing them to track inventory levels, monitor supplier performance and avoid potential bottlenecks or disruptions.

Armed with these powerful insights, companies can make data-driven decisions to optimise their supply chain efficiency, reduce costs and provide unparalleled customer service.

What are the Different Types of Supply Chain Analytics?

Supply chain analytics encompasses various analytical methodologies, each with distinct characteristics and analytical approaches. The types of these techniques are as follows:

  • Descriptive Analytics: It is concerned with getting insights into the past events of an organisation’s supply chain operations. It employs techniques such as data visualisation, statistical analysis and trend analysis that can summarise historical data and detect prevailing trends and patterns.
  • Predictive Analytics: This involves harnessing past data to generate predictive models for future occurrences. It is particularly useful for helping businesses anticipate future demand, identify risks, optimise supply chain operations and pinpoint opportunities.
  • Prescriptive Analytics: It offers guidance on the most optimal course of action to achieve predetermined outcomes. It integrates both descriptive and predictive analytics to suggest an action plan for a given scenario.
  • Diagnostic Analytics: It focuses on ascertaining the underlying causes of issues or anomalies in supply chain operations. It uses techniques such as data mining and drill-downs that can identify inefficiencies within a firm’s supply chain processes, allowing for targeted improvements.
  • Real-time Analytics: It enables businesses to obtain instantaneous insights into their supply chain operations. It leverages current data to provide real-time decision-making support for inventory management, logistics, transportation and other real-time supply chain activities.

The Significance of Supply Chain Analytics

The significance of supply chain analytics in SCM cannot be overstated. It provides valuable insights into the complex processes of supply chain operations that are often unpredictable and irregular.

Below are a few reasons why supply chain analytics is important in SCM:

Better Decision Making 

By offering significant insights into supply chain operations, supply chain analytics assists organisations in making educated decisions. These insights may be utilised to optimise supply chain operations and save costs.

Inventory Optimisation 

By giving real-time access to inventory levels, demand trends and supplier performance, supply chain analytics may assist organisations in optimising their inventory levels. This data may be utilised to make better inventory management decisions and lower inventory expenditures.

Enhanced Customer Service 

Organisations may use supply chain analytics to track the delivery of goods and services, monitor customer feedback and respond to consumer concerns as quickly as possible. This enables organisations to enhance customer satisfaction and deliver better customer service over time.

Risk Management

Organisations can detect and manage supply chain risks, supply chain interruptions, supplier performance concerns and demand fluctuation. This is also achieved through supply chain analytics, which gives them real-time visibility into their supply chain management operations.

Conclusion

The world of supply chain analytics is an ever-evolving and complex arena, with businesses striving to gain a competitive edge by harnessing the power of data analytics. It is imperative that organisations recognise the pivotal role of supply chain analytics in driving business success and invest in the technologies and expertise necessary to unlock its full potential.

If you are an aspiring supply chain professional looking to enhance your supply chain management skills, then look no further. Imarticus’s Advanced Certification Program in Digital Supply Chain Management is the ultimate IIT supply chain management course incorporating the latest supply chain analytics concepts. 

This supply chain analytics course provides a comprehensive and practical digital supply chain management approach. You’ll learn how to apply cutting-edge supply chain analytics techniques, design effective supply chain networks and optimise supply chain operations. With expert instruction from industry leaders and real-world case studies, you’ll gain the knowledge and skills required to excel in the dynamic world of supply chain management.

Using PowerPivot for Advanced Data Science

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Power Pivot is a powerful data modelling and analysis tool and can play an essential role in a Career in Data Analytics. Power Pivot is a vital tool for any data analyst or data scientist with its ability to handle large data sets and perform complex calculations.

This blog will explore how to use Power Pivot for advanced data science, including data preparation and cleaning, data modelling and analysis, and data visualisation. 

Whether you’re starting your Career in Data Analytics or are a seasoned data professional, this blog will provide valuable insights and best practices for using Power Pivot in your data science projects.

What Is Power Pivot?

Power Pivot is a Microsoft Excel add-in that provides data analysis and modelling capabilities for business intelligence and data analysis. 

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A familiar Excel interface allows users to import, manipulate, and manage large amounts of data and create custom calculations, relationships, and reports. 

The Power Pivot feature enhances Excel’s capabilities, enabling users to perform advanced data analysis and reporting, including data modelling and visualisation.

Why Use Power Pivot?

Power Pivot is a powerful data analysis tool that allows you to process large amounts of data and perform complex data manipulations in minutes. It is used for advanced data analysis, particularly in business intelligence and data science. 

There are several reasons to use Power Pivot:

Ease of Use: Power Pivot has a handy interface, making it easy for non-technical users to perform complex data analysis.

Speed: Power Pivot can handle large amounts of data, allowing you to process, manipulate, and analyse data in a matter of minutes.

Integration with Excel: Power Pivot is integrated with Microsoft Excel, making it a convenient tool for those familiar with spreadsheet software.

Data Manipulation: Power Pivot enables you to perform complex data manipulations, including data cleansing, data aggregation, and data modelling, making it an ideal tool for data scientists and business analysts.

Enhanced Data Analysis: Power Pivot provides advanced data analysis features, such as pivot tables, charts, and data visualisations, which are not available in standard Excel.

Scalability: Power Pivot can scale to handle large amounts of data, making it an ideal tool for large-scale data analysis projects.

Power Pivot Use Case

A use case for Power Pivot might be for a business analyst who needs to analyse sales data from many departments and stores. 

The data is stored in separate Excel spreadsheets, and the analyst needs to combine the data and perform analysis to identify trends and make recommendations to the company.

With Power Pivot, the analyst can:

Import data from many Excel spreadsheets into a single data model.

Create relationships between the tables to link the data together.

Create calculated fields using DAX (Data Analysis Expressions) to perform custom calculations, such as finding the total sales for each department or store.

Build tables and charts to analyse the data and identify trends and patterns.

Share the data model and analysis with others by creating a Power BI report or publishing the Excel workbook to the web.

This use case demonstrates the ability of Power Pivot to handle large data sets, perform complex calculations, and provide interactive data analysis and visualisation capabilities.

How To Enable Power Pivot?

To enable Power Pivot, you need to have the Power Pivot add-in installed in your Microsoft Excel software. You can download and install it from the Microsoft website if you don’t have it.

Here are the steps to enable Power Pivot:

Open Microsoft Excel and then click on the “File” tab.

Go to the “Options” tab and select “Add-Ins.”

In the Manage box, select the option “Excel Add-ins” and then click on “Go.”

Check the “Microsoft Power Pivot for Excel” check box and click “OK.”

You should now see a Power Pivot tab in the ribbon menu.

Once you enable Power Pivot, you can import and manage large data sets, perform advanced data analysis, and create robust pivot tables and models. 

What Are the Benefits of Using a Power Pivot?

Some benefits of using Power Pivot include the following:

Handling large data sets: Power Pivot can take large amounts of data from various sources, making it easier to work with and analyse data.

Data Modeling: Power Pivot allows you to create relationships between tables, perform calculations and create calculated fields using DAX (Data Analysis Expressions).

Integration with Power BI: Power Pivot can be a data source in Power BI, a cloud-based business intelligence and data visualisation platform.

No need for VBA or Python: Power Pivot does not need any programming skills or knowledge of Python or VBA, making it accessible to a broader range of users.

Improved performance: Power Pivot uses columnar storage and in-memory technology to improve query performance, making it faster and more efficient than traditional Excel pivot tables.

Conclusion

Power Pivot is a critical tool to master for anyone aspiring to build a career in Data Science. It offers the ability to process massive data sets, execute complex calculations, and provide interactive data analysis and visualisation capabilities. 

Businesses can establish a robust data science workflow by integrating Power Pivot with other data science tools like Python and Power BI.

A data analytics certification course is a valuable investment to enhance your existing data science skills or start your journey in the field.

Imarticus’ Postgraduate Program in Data Science and Analytics is a comprehensive course that covers all aspects of data science, including data preparation, modelling, analysis, and visualisation. 

With a focus on hands-on learning and real-world projects, this data analytics certification course equips students with the necessary skills and knowledge to succeed in data science.

This data science course with placement assistance helps students with practical and job-ready education, preparing them for a successful Career in Data Science.

So take advantage of this opportunity to further your Career in Data Science. Enrol in Imarticus Learning’s Postgraduate Program in Data Science and Analytics today.

Popular Methodologies for Supply Chain Management

Posted on by Imarticus Learning

Supply chain management deals with cut-edge tools and methods in which businesses can be revolutionised. From acquiring raw materials to manufacturing and delivering a product, the supply chain management is responsible for completing orders at the right time while ensuring the highest quality of any product.

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As we share our insight on the current landscape and processes that the Chief Supply Chain and Operations Officer overlooks, we aim to outline the seemingly complex process that stands as a critical element to any business. This article will explore the supply chain management strategy while delving into the procurement process.

How Does Supply Chain Management Work? 

Supply chain management (SCM) is an elaborate process that controls the existing incoming and outgoing flow of goods and services. It examines all processes, from acquiring materials needed for making the product to delivering the final product. 

It works by optimising the procurement process in a way that is in the company’s best interest. While the process entangles numerous management areas, these fields conclude the entirety of the SCM process —strategic planning, procurement, product development, inventory management, and logistics. 

Overview of Popular Methodologies 

The Chief Supply Chain and Operations Officer looks after the two main areas of SCM, namely logistics-based methodologies and demand-based methodologies. The logistics process consists of a multitude of areas including inventory, transportation, warehousing, and distribution. 

Furthermore, it handles the streamlining of resources. On the other hand, demand-based methodologies analyse customer demand and the costs of meeting that demand.

Logistics-Based Methodologies

Just-in-Time (JIT)

Just-in-time management is a methodology aimed at reducing costs and space related to inventory by storing only items according to existing demand. This process ensures ample use of storage for manufacturing and retail settings.

Cross-Docking Logistics

Cross-docking is a logistics practice where products are received from a supplier and immediately reloaded onto a different vehicle for delivery to the customer. This approach eliminates the need for warehousing, reducing inventory costs and shortening delivery times.

Lean Logistics

Lean logistics is a methodology designed to increase efficiency and reduce costs by eliminating waste in the supply chain. It focuses on streamlining processes and eliminating non-value-added activities, such as unnecessary handling and storage of products. 

Reverse Logistic

Reverse logistics return goods from customers to the manufacturer or supplier. It is used to manage returns of defective or repaired products or dispose of excess inventory. 

Supply Chain Visibility

This approach focuses on increasing visibility into the supply chain by leveraging technology such as tracking systems, sensors, and RFID tags. It creates a real-time view of the supply chain, allowing organisations to anticipate and respond to changes more quickly.

Vendor-Managed Inventory (VMI)

Vendor-managed inventory (VMI) is a supply chain management system in which the supplier of a product is responsible for maintaining the appropriate inventory level of that product at the customer’s site. The supplier replenishes inventory as it is consumed for forecasting customer demand for the product.

Quick Response (QR) 

The Quick Response (QR) method is a supply chain management method seeking to maximise efficiency and minimise costs by using technology and data to make decisions quickly. It is designed to reduce inventory costs, improve customer service, and eliminate waste in the supply chain. 

Demand-Based Methodologies

Demand-Driven Materials Requirement Planning (DDMRP)

The Demand-driven MRP is a planning system for managing inventory within a given supply chain. It uses a pull mechanism to adjust inventory levels based on demand signals as opposed to the traditional push system method.

Distribution Requirements Planning (DRP)

The Distribution Requirements Planning (DRP) division ensures the efficient delivery of systems at the right time by combining inventory, sales, and product data. The system creates a plan for product distribution used for both long-term and short-term needs.

Automated Replenishment System (ARS)

As the name suggests, the Automated Replenishment System (ARS) combines the tracking of inventory levels with the automated generation of orders on the fall of stock levels. This automated system ensures the availability of inventory on customer demand.

Warehouse Management Systems (WMS)

The Warehouse Management System (WMS) is an adequate supply chain management strategy that drives companies to manage the storage and movement of inventory within the existing supply chain. It is a software application that can be easily integrated with other systems to generate real-time visibility.

Enterprise Resource Planning (ERP)

Enterprise resourcing planning is a system with demand-based methodologies that handles procurement process management by offering real-time visibility. Apart from procuring, the ERP helps companies source and distribute goods and services.

Advanced Planning & Scheduling (APS)

The Advanced Planning and Scheduling (APS) system is an integrated process aligned with the supply chain for planning, scheduling, and optimising the flow of materials and resources. This process identifies and mitigates supply chain risks and improves customer service expectations.

Conclusion 

The popular methodologies used in supply chain management are beneficial to the overall improvement of supply chain operations. Not only do they help to reduce overriding costs, but they also enhance the supply chain and deliver better alternatives to ongoing operations.

Per a report by Grand View Research, supply chain management is projected to grow by a minimum of 11.1% CAGR by 2030. An IIM supply chain management course delves into the intricacies of the supply chain management process while also discussing the skill set and knowledge required to apply these methodologies. Seize the opportunity today and apply for an extensive SCM programme with Imarticus’ Executive Certificate Programme in collaboration with IIM Raipur!

Expertise in supply chain management equips managers with the proper understanding for handling elaborate processes. Furthermore, you can always refer to a supply chain management certification course to ensure that your employers have the best impression of you.

Top 5 Data Mining Tools

Posted on by Imarticus Learning

In today’s world, data mining is an important process when organisations think about business decisions. Data is a very valuable asset for modern companies. It is necessary to extract data from a data source.

This is when data mining tools come into the picture. They enable companies to find out data trends and establish links between them. The extracted data is used for data analysis.

Learning how to use basic data mining tools is quite significant in keeping with today’s work environment. One way to do this is by taking part in data science training. Learn data visualisation with Tableau as well as using other tools such as RapidMiner and KNIME. Keep scrolling to learn about data mining tools and how neural networks are utilised for coherent data mining.

What is Data Mining?

Simply put, data mining is the method of analysing huge amounts of data and using it for mitigating risks and solving problems. The analogy of mining is accurate since you are extracting material from mountains of data in order to find items which you need.

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Learn data mining today as it will be beneficial for your career as a Data Analyst or Business Intelligence Expert. Finding patterns to tackle problems and categorising data are some other uses of data mining. Data mining helps companies to reduce costs and improve customer service.

You must know about neural networks when discussing data mining. Extracting huge chunks of data can be done easily by using neural networks. These help in finding out hidden data from large chunks. You can use this for data analytics at your company.

Benefits of Data Mining

A career in data science has huge scope in present times as we live in a data-driven world. From providing valuable insights to increasing profits, there are various advantages of data mining. You can read about some of them below. 

  • Helps to detect fraud – You can identify risks using data mining and consequently detect fraud. Traditional methods often fail to find these, especially when using unorganised datasets. You can detect types of risks and find out ways to tackle them in the future. 
  • Helps to collect reliable data – You can use data mining to gather reliable information to use in market research. This information can be vital for the company as they can find out what customers want. Data mining is also useful for companies to evaluate themselves as to where they stand in the current market scenario.
  • Analyses huge chunks of data in quick time – The sheer volume of data becomes too much to handle for companies. Data mining is a boon in this regard. Most modern companies use data mining to analyse large volumes of data rapidly with accurate results.
  • Aids in identifying customers – Each product in the market has a unique customer base. With data mining, the job of identifying clients is much easier. Using the right tools, companies can target specific customers and showcase products which they are most likely to purchase.
  • Increases company revenue – Data mining analyses large volumes of data and subsequently enables companies to find out what their customers like and dislike. By using this information, the company can make future predictions and improve its products. This is helpful for the revenue growth of the company.

Best Data Mining Tools

There are quite a large number of data mining tools in use at present. Both beginners and experts have their own tools to work with for their specific domains. Take a look at the 5 most popular tools which a Data Analyst uses.

RapidMiner

One of the most used data mining tools in the market is RapidMiner. The data science platform can almost do any job related to data – clustering, preparing and predictive modelling. Even if you lack technical skills, you can use this tool easily.

A large number of online courses can make anyone an expert on RapidMiner. The inbuilt algorithms and drag-and-drop interface are a few highlights of this tool.

You can spot patterns and trends after analysing your data in RapidMiner. The large user base is always enthusiastic when it comes to lending help to new users. You can visualise data and create data models with the help of this popular software.

KNIME

Konstanz Information Miner or commonly known as KNIME is an open-source data mining tool. The customisable interface is one of the best features of this tool.

You can perform all types of data mining jobs with KNIME. These include regression, classification and data simplification. You might even apply machine learning algorithms to perform tasks.

Seamless integration with Python and R extends the service of KNIME. From small business firms to large financial firms, KNIME is a widely popular tool in the world of data mining.

Apache Mahout

Creating scalable applications is easier and faster than ever due to Apache Mahout. Using machine learning methods such as categorising, filtering and clustering are some highlights of this data mining tool.

Data Scientists use Apache for analysing huge volumes of data. They use their own algorithms inside this free tool. Leading companies such as Yahoo and LinkedIn use Apache Mahout for their work.

Python

The most popular programming language, Python is a must-have for any Data Analyst. Its user-friendly interface and open-source platform give this tool a huge boost over many others.

The applications of Python are seemingly endless. Handling voluminous data and organising it is quite easy to do. Writing codes and automating data are some other uses of Python.

What makes Python really popular is its free platform and its library of packages. Most companies make use of this programming language in their functionality.

Tableau

Tableau is a data visualisation tool which is hugely popular among data scientists. Data visualisation with Tableau is mostly used for large datasets.

You can create maps, charts and graphics without writing any code. This data mining tool is available both as desktop software and mobile application. Quick data analysis is one of the top features of this data visualisation tool.

Conclusion

Learn data mining by enrolling in a machine learning certification course. It is vital to gain insights into the various tools that you will need. Apart from the list on this page, there are quite a number of other tools out there. Find the ones that suit you and hone your skill in them.

Enrol at Imarticus Learning which is your one-stop solution for making progress in your career in data science. This IIT data science course is one of the best you can take part in. It combines online classes with campus immersion at IIT Roorkee.

Enrol in the Certificate Program in Data Science and Machine Learning today and get the best guidance on data science. Created with iHUB Divyasampark @IIT Roorkee, this course enables candidates to build a strong base in data science. Join the course today!

Python vs R: Why is Python Preferred for Data Science?

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Python is a high-level and fast-growing programming language which is ideal for scripting both applications as well as websites. Even though there are several programming languages such as C++, SQL or R that are widely used by aspiring data scientists, Python stands out from the rest. 

become a Data Analyst

A career in Data Analytics ensures a promising future for those who can master the fundamental programming concepts and apply them to solve real-world problems in any business.  

It is imperative to know how to employ data analytics tools to evaluate the performance of a business. Knowing a programming language like Python can be extremely effective as it helps you build these tools. 

Some data scientists, on the other hand, use R to analyse data through interactive graphics. In fact, R is a frequently chosen programming language for data visualisation. It is, however, important to understand on what grounds Python and R are different and why Python is the most preferred programming language in this profession. 

What Is Python?

Python is extremely versatile and it is among the most dynamic and adaptable programming languages used in data analysis. It is used to develop complex numeric as well as scientific applications. You can use this programming language to perform scientific calculations.  

It is an open-source and object-oriented programming language with a rich community base, libraries and an enormous arrangement of tools. Compared to other programming languages, Python, with its straightforward code, is much simpler to learn because of its broad documentation. 

What Are the Features of Python?

The significant features of this programming language are

Readable: In comparison to other programming languages, it is much easier to read. It uses less code to perform a task. 

Typed language: The variables are automatically created as it is a typed language. 

Flexible: It is quite easy to run this programming language on multiple platforms as it is flexible and adaptable. 

Open-source: It is a free programming language. It uses an easily accessible and community-based model. 

Why Is Python Important in Data Science?

Whether you are already a professional data analyst or someone who aspires to explore a lucrative career in Data Analytics, it is imperative that you know how to use Python. Some of the most prominent reasons why this programming language is preferred for data science are:

Easy to learn and use: With better comprehensibility and simple syntax it has become extremely popular over the years. It is also quite easy to handle the data through its data mining tools such as Rapid Miner, Weka, et cetera.

Builds superior analytics tools: It is a dynamic programming language that provides better knowledge and correlates data from large datasets. It also plays a crucial role in self-service analytics. 

Important for deep learning: It assists data scientists to develop deep learning algorithms which were majorly inspired by the architecture of the human brain. 

Creates data analysis scripts: Data analysis scripts can be created using this program within Power BI. 

Has a rich community base: Python developers are able to address their issues within a huge community of data scientists as well as engineers. Python Package Index, for example, is a great place for developers to explore this programming language. 

What Is R?

R is a versatile, statistical and advanced programming language which is primarily used for interpreting data. It works perfectly for data visualisation, web applications and data wrangling. It is also used to perform statistical calculations and that too without vectors. R makes collecting and analysing large datasets easy. 

What Are the Features of R?

The important features of R include

Open-source: R too, is free, adaptable and accessible to all. It can be easily integrated with multiple applications. 

Static graphics: R has powerful and interactive static graphics which produce high-quality data visualisations. 

Statistical calculations: This programming language can perform both simple as well as complex statistical calculations. 

Compatibility: This programming language is compatible with other programs such as C, C++, Java, et cetera. 

Python vs R: Which Programming Language Is Preferred in Data Science?

The primary reasons why Python is often preferred over R are:

Purpose: Both these programming languages serve different purposes. However, even though both are used by data analysts, it is Python which is considered more versatile in comparison to R. 

Users: The software developers prefer Python over R as it builds complex applications. Statisticians and researchers in academia, on the other hand, prefer using R. 

Ease of use: Beginner programmers prefer Python because of its English-like syntax. R, on the other hand, can be difficult once a programmer starts exploring its advanced functionalities. 

Popularity: Python outranks R mainly because it can be used in several software domains. 

However, despite these differences, both these programming languages have robust ecosystems of libraries and are extremely crucial for an aspirant who wishes to start a prospective career in Data Analytics.

Conclusion

A career in data science is considered one of the most successful professions in recent years. If you want to learn data analytics techniques, it is imperative that you learn Python. 

In order to pursue a career in Data Science, you should choose a proper data analytics certification course that introduces you to this programming language. 

Imarticus’ Postgraduate Program in Data Science and Analytics is a job-assurance program that helps you navigate all aspects of this profession. The curriculum of this course covers all the fundamental data analytics concepts including data analysis, introduction to important programming languages such as SQL and Python, data visualisation with Power BI or Tableau and applications of machine learning. 

How can I make a successful career by pursuing a Machine Learning and AI course?

Posted on by Imarticus Learning

Introduction

In recent years, the demand for skilled professionals in the field of Machine Learning (ML) and Artificial Intelligence (AI) has skyrocketed. As a result, pursuing an ML and AI course can offer a path towards a highly rewarding career.

In this article, we will explore how to become a successful AI Engineer and develop a fulfilling career in ML and AI.

Steps to Become a Successful AI Engineer

1. Developing a Strong Foundation

To become a successful AI Engineer, it is essential to have a strong foundation in the principles and concepts of ML and AI. Some of the key skills required include programming, data analysis and knowledge of algorithms and statistical modelling. Pursuing an ML and AI course can provide a structured approach to gaining these skills.

2. Choosing the Right Course

Choosing the right ML and AI course is crucial to gaining the skills and knowledge needed for a successful career in this field. Various online and offline courses are available, ranging from beginner to advanced levels, which provide a comprehensive understanding of the field.

Factors to consider when selecting a course include the reputation of the institution, course content and the availability of hands-on experience opportunities.

To pursue a career in ML and AI, the following skills are required:

Strong programming skills in languages such as Python, R and Java

Knowledge of statistical modelling, probability theory and data analysis

Familiarity with ML libraries and frameworks, such as TensorFlow, PyTorch and Scikit-learn.

3. Hands-on Experience

best artificial intelligence certification course

One of the most crucial aspects of becoming a successful AI engineer is having hands-on experience applying ML and AI techniques to real-world problems. A good ML and AI course will provide ample opportunities for students to practice and apply these skills.

4. Building a Portfolio

Building a portfolio of projects is an excellent way to showcase your skills and expertise to potential employers. Completing real-world projects can also help you gain practical experience and hone your problem-solving skills. Sharing your portfolio online through platforms like GitHub can help you gain visibility and connect with other professionals in the field.

5. Networking and Collaborating

Networking and collaborating with other ML and AI professionals is essential to building a successful career in this field. Connecting with others through online forums, attending industry events and collaborating on projects can help you gain valuable insights and expand your knowledge.

6. Keeping Up With the Latest Trends

Machin learning and AI are rapidly evolving fields. So, it is crucial to stay current with the latest trends and technologies. Regularly reading research papers, attending conferences and participating in online courses can help you stay up to date with the latest developments in the field.

7. Overcoming Challenges

Pursuing a career in ML and AI can be challenging. So, it is essential to be prepared for common obstacles such as a lack of practical experience, difficulty finding job opportunities and managing the steep learning curve. It is essential to stay focused, committed and persist through these challenges.

8. Job Opportunities and Career Paths

A career in ML and AI offers numerous job opportunities in various industries such as healthcare, finance and technology. Some common roles include Data Scientist, ML Engineer and AI Researcher.

In healthcare, predictive models assist with diagnosis and treatment planning, while in finance, algorithms detect and prevent fraud.

In the technology industry, ML and AI are used to automate tasks, improve user experience and develop intelligent systems.

Data Scientists collect and interpret complex data sets, while ML Engineers develop and deploy ML systems.

AI Researchers conduct research and develop new algorithms to improve the capabilities of intelligent systems.

Pursuing an ML and AI course can provide a pathway towards these rewarding and high-paying career paths.

Embracing the Future of Work

Artificial intelligence is a super cool technology that can do amazing things. It can help us solve some of the world’s biggest problems like climate change, food and water shortages and disease.

AI is getting better and smarter every day, and it’s becoming a big deal in the job market. Lots of companies are looking for people with AI and ML skills.

In the next five years, there will be a lot more jobs in AI. AI is already making a big impact

on the healthcare industry by changing the way doctors diagnose and treat illnesses.

Wrapping Up

Pursuing a Machine Learning and Artificial Intelligence course can provide a path towards a successful career in this rapidly evolving field. By developing a strong foundation, gaining hands-on experience, building a portfolio, networking and keeping up with the latest trends, you can become a successful AI engineer and achieve your career goals.

Imarticus Learning provides structured technical proficiency development courses for fresh graduates, young professionals, and individuals seeking to enhance their skills.

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