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Category: Analytics

Analytics and Visualisations for Businesses: Getting the Most Out of Data

Posted on by Imarticus Learning

What is Data Analytics?

Data analytics involves acquiring, arranging, evaluating, and transforming diverse raw data into comprehensive insights to enhance a business’s or organisation’s operational efficiency and performance.

This multifaceted approach comprises distinct phases:

  • Data categorisation: Grouping data based on various parameters, such as demographic factors (e.g., age, gender, income). 
  • Data acquisition: Gathering data from diverse sources, including computer systems, cameras, personnel within companies/organisations, and more. 
  • Data structuring: Organising data utilising spreadsheets or specialised software to help ensuing analysis. 
  • Data cleansing and preparation: Ensuring the accuracy, consistency, and elimination of errors or duplicates, enabling analysts to start the data analysis process.

What Are the Types of Data Analysis Techniques?

Data analysis encompasses four categories: descriptive, diagnostic, predictive, and prescriptive. These analyses enable businesses to make informed decisions.

Descriptive analysis

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The descriptive analysis focuses on understanding past events or trends. It provides insights into sales volumes, fluctuations, and other relevant information without delving into causality.

Diagnostic analysis

The diagnostic analysis aims to uncover specific outcomes or events’ root causes or factors. It investigates reasons for sales increases or decreases, such as seasonal patterns or marketing campaigns.

Predictive analysis

The predictive analysis leverages statistical techniques and data mining to forecast future outcomes or trends. It creates visual representations to help understand and inform decision-making.

Prescriptive analysis

The prescriptive analysis offers recommendations based on predictive analysis outcomes. It suggests specific actions to take and assesses the potential implications of those actions.

What Are the Components of Data Analytics?

Data analytics elements cover various techniques for processing data. They include:

Text analysis: Text analysis involves analysing large volumes of text to develop algorithms. It is applied in autocorrect features, linguistic analysis, and pattern recognition, such as in Microsoft Word.

Data mining: Data mining focuses on extracting valuable insights from vast datasets. It helps identify behavioural patterns in clinical trials and breaks down large data chunks into smaller, purposeful segments.

Business intelligence: Business intelligence is a vital process for successful enterprises. It transforms data into actionable strategies, guiding decisions like product placement and pricing to drive commercial success.

What is Data Visualisation?

Data visualisation involves presenting information, such as graphs or maps, to improve understanding and extract insights from data. Its primary aim is to ease the identification of patterns, trends, and anomalies within large datasets.

Data visualisation is often used with terms like information graphics, information visualisation, and statistical graphics.

Within the data science process, data visualisation is a crucial step. Once data is collected, processed, and modelled, visualising it enables drawing meaningful conclusions. 

Additionally, data visualisation is a component of the broader discipline of data presentation architecture (DPA), which focuses on identifying, manipulating, formatting, and delivering data.

What Are the Types of Data Visualisation Techniques?

Visualising data can range from simple bar graphs and scatter plots to robust analyses comparing variables like the median age of the United States Congress to that of Americans. 

Some common data visualisation types include:

Table: Data organised in rows and columns, created in Word documents or Excel spreadsheets.

Chart or graph: Data presented in tabular form with values plotted along the x and y axes, using bars, points, or lines to represent comparisons. Infographics combine visuals and words to illustrate data.

Gantt chart: A timeline-based bar chart that visualises tasks and their duration in project management.

Pie chart: Data divided into slices representing percentages, combining to form a whole (100%).

Geospatial visualisation: Data displayed on maps using shapes and colours to highlight relationships between specific locations, such as choropleth or heat maps.

Dashboard: Business-focused display of data and visualisations, providing analysts with an overview and deeper insights.

Each visualisation type serves different purposes, aiding in data understanding, analysis, and presentation.

What Are the Advantages of Data Analytics and Visualisation?

Data analytics and visualisation play vital roles in the business decision-making process, offering many benefits:

Enhanced decision-making: Using skilled data analysts and appropriate software, companies can identify market trends and make informed decisions to boost sales and profits.

Deeper insights: Data analytics and visualisation enable companies to gain valuable insights into their customer base. Businesses can better understand clients’ preferences and behaviours by breaking down large datasets.

Improved productivity and revenue growth: By analysing data, companies can identify areas for investment and process automation, leading to improved efficiency and revenue growth.

Real-time market behaviour monitoring: With real-time data analytics and visualisation dashboards, stakeholders can identify changes in market behaviour and adapt their strategies.

Market analysis: Data analytics and visualisation techniques allow companies to analyse different markets, enabling informed decisions on which markets to focus on and which to avoid.

Business trend analysis: Data analytics and visualisation enable businesses to examine present and past trends, facilitating predictions and guiding future strategies.

Data relationships: By exploring data relationships, companies can uncover valuable insights and make informed decisions based on these findings.

What Are the Differences Between Data Analytics and Data Visualisation?

Data visualisation and data analytics are distinct careers with differences in how they work with large datasets and communicate their findings.

Data use

Data analysts study datasets with a specific purpose, drawing conclusions and making predictions based on the data. They provide recommendations and insights to decision-makers in organisations.

Data visualisation experts focus on presenting data visually to improve understanding. They don’t reach conclusions or make predictions themselves but translate the findings of data analysts into visually appealing and understandable formats.

Communication methods

Data analysts primarily communicate through written and oral reports, conducting in-depth analyses of their research questions. Their reports include the question, methodology, and findings of their analysis.

Data visualisation experts present their reports using graphs, charts, and visual aids, simplifying complex data into easily understandable visuals. Their presentations often consist of a series of visual aids without providing direct conclusions or recommendations.

Conclusion

Businesses and organisations can make informed choices based on analysed data by using the power of data analytics and visualisation, improving performance and profitability. 

Businesses can identify the value of their collected data using a data-driven approach, making it a significant advantage they should consider.

Embark on a data-driven career journey with Imarticus Learning’s online BBA course in Business Analytics by Geeta University.

Gain comprehensive data visualisation and analytics skills to make informed decisions and excel in business. Start shaping your future today!

Visit Imarticus Learning for more information on our BBA in Business Analytics program.

Robotic Process Automation (RPA) in Procurement and Supply Chains

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RPA, or Robotic Process Automation, is a technology that automates repetitive and rule-based tasks using software bots. These bots mimic human actions and interact with digital systems to perform tasks like data entry and report generation.

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RPA enables the automation of tasks such as order processing, shipment scheduling, logistic management, and invoicing, leading to improved logistics performance and cost reduction.

The Advantages of RPA for Modern Supply Chains

RPA use cases in supply chain management provide lots of benefits to businesses. It is the ultimate solution to improve your online or offline business.

Some of the specific benefits are:

  • Enhanced Accuracy: RPA eliminates the potential for human errors in data entry and processing. Bots follow predefined rules and perform tasks consistently, improving data accuracy and reliability throughout the supply chain. 
  • Improved Productivity: By automating routine tasks, RPA boosts productivity by reducing the time and effort required. It enables supply chain teams to handle increased workloads, meet tight deadlines, and achieve higher output levels. 
  • Scalability: RPA can quickly scale to accommodate fluctuations in demand or business growth. RPA helps supply chains handle increased volumes without the need for significant additional resources. 
  • Cost Savings: By automating manual tasks, RPA reduces labour costs and decreases the likelihood of errors or rework. It also optimises resource utilisation, leading to cost savings in the long run.

RPA-Automated Supply Chain Processes

Robotic Process Automation (RPA) has revolutionised various industries by automating critical processes in the supply chain.

Let’s see how RPA has aided in automating processes across different sectors:

  • Order Processing and Payments: RPA streamlines order processing by automatically extracting sales order data from multiple sources such as emails, faxes, and EDI. It eliminates data entry errors and simplifies order entry and fulfilment by managing complex business rules. 
  • Onboarding of Partners: RPA simplifies the onboarding process by creating intelligent bots that synchronise and automate the onboarding of new goods and services from partners. It helps streamline the integration and collaboration with suppliers and other business partners. 
  • Shipment Scheduling and Tracking: RPA automates scheduling and tracking shipments by automating data entry, applying relevant conditions for scheduling, and assigning unique IDs for tracking purposes. It improves efficiency and accuracy in managing the shipment process. 
  • Invoicing: RPA facilitates invoicing by automating data entry, extraction, and calculation tasks. It ensures accurate and efficient invoicing processes, reducing manual errors and improving overall efficiency in financial transactions. 
  • Procurement and Inventory: RPA automates procurement and logistic management processes by automatically updating data entries and utilising unique identifiers to track and manage goods efficiently. 
  • Supply and Demand Planning: RPA supports supply and demand planning by automating data updates and streamlining the process of managing new goods entries. By leveraging RPA, organisations can forecast demand more accurately and efficiently, improving customer satisfaction. 
  • Customer Services: RPA improves customer service by enabling quick and efficient responses to customer requests and demands. By automating receiving and addressing customer inquiries or interest changes, organisations can deliver timely and attentive service, enhancing overall customer satisfaction.

Implementing an RPA Program in Your Supply Chain

Implementing an RPA program in your supply chain can bring numerous benefits, such as increased efficiency, cost savings, and improved accuracy. 

Here are the key steps to consider when implementing an RPA program in your supply chain:

Identify Suitable Processes

Start by identifying the supply chain processes that are repetitive, rule-based, and prone to human errors. These processes are ideal candidates for automation through RPA.

Conduct Process Analysis

Analyse the identified processes to understand their steps, dependencies, inputs, and outputs. Document the existing workflows and identify any pain points or areas for improvement.

Prioritise Processes

Prioritise the processes based on their potential impact, complexity, and feasibility for automation. Begin with smaller, less complex processes to gain experience and build momentum before tackling more critical or intricate processes.

Engage Stakeholders

Involve key stakeholders from IT, supply chain, and relevant departments in the implementation process. Seek their input, insights, and buy-in to ensure the successful adoption of RPA in the supply chain.

Select RPA Tools

Evaluate and select suitable RPA tools that align with your supply chain requirements. Consider factors such as ease of use, scalability, compatibility with existing systems, and support for process integration.

Develop RPA Solutions

Work closely with RPA developers or experts to design and develop automation solutions for the identified processes. Collaborate to create bots to perform the desired tasks, integrate with relevant systems, and handle exceptions effectively.

Test and Validate

Thoroughly test the RPA solutions to ensure they function as intended and deliver the expected results. Validate the automated processes’ accuracy, reliability, and efficiency before deploying them in the live environment.

Train and Educate Employees

Provide training and education to employees who will manage and oversee the RPA program. Help them understand the benefits, purpose, and functionalities of RPA and address any concerns or misconceptions.

Monitor and Optimise

Continuously monitor the performance of the implemented RPA program and gather feedback from users. Identify opportunities for further optimisation, refine processes as needed, and make adjustments to maximise the benefits of RPA in your supply chain.

Scale and Expand

Once you have successfully implemented RPA in selected processes, consider scaling and expanding the program to cover other functions in your supply chain. Use the insights and lessons learned from initial implementations to guide future deployments.

Supply Chain Challenges for RPA

Supply chains encounter several challenges when implementing Robotic Process Automation (RPA). Some of them include:

Data Integration: Integrating data from several systems, including enterprise resource planning (ERP) and logistic management systems, can be complex. RPA installations need seamless data connectivity for decision-making and automation to guarantee accurate and current information.

Exception Handling: Supply chain processes often encounter exceptions or deviations from the standard workflow. Handling these exceptions and developing automation solutions to address them can be complex, as they may require human judgment and decision-making.

Change Management: Introducing RPA in the supply chain requires change management efforts to address potential employee resistance. It involves educating and training employees on the benefits of automation and addressing any concerns about job security or changes to their roles and responsibilities.

Process Standardisation: RPA implementations work best when processes are standardised and well-defined. In cases where supply chain processes vary across locations or departments, standardising procedures may be necessary before implementing automation.

Conclusion

Robotic Process Automation (RPA) holds immense potential in transforming procurement and supply chains. By leveraging RPA, organisations can achieve increased efficiency, enhanced accuracy, improved productivity, and streamlined processes.

RPA plays a crucial role in logistic management, enabling supply chains to optimise operations, respond to customer demands, and gain a competitive edge in the market.

To further enhance your expertise in Supply Chain Management and understand the application of RPA in procurement and supply chains, consider enrolling in Imarticus Learning’s Digital Supply Chain Management With E&ICT, IIT Guwahati course.

This Supply Chain Management certification course offers all the necessary knowledge and skills that you will need to excel in the digital era. Visit Imarticus Learning for more information.

Network Optimisation for Efficient Distribution

Posted on by Imarticus Learning

An efficient distribution network is the heart of a successful supply chain. In today’s world, companies that are operating on e-commerce platforms are gaining a competitive edge with better logistics and distribution networks. When we speak about distribution network optimisation, we mean the availability of enough distribution centres at locations in proximity to delivery points and adequate numbers of vehicles to transport the materials there. 

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Network optimisation is also required for the time taken in loading and unloading materials and during planning the inventory for each distribution centre. All these may be achieved by strong sales and operation planning.   

Factors Affecting Distribution Network

Some of the factors which affect the distribution network are as follows – 

Customer analysis 

The demand volume history and financial credibility of the buyer need to be researched. Simultaneously the payment cycle or policy of the customer also needs to be assessed. 

Analysis of suppliers 

Purchase order history and logistic details of suppliers are to be monitored. Besides this, the commitment to quality and timeliness of supply needs to be maintained. 

Inventory assessment

The inventory assessment for each distribution point should be prepared. Inventory management should be considered keeping in mind the sudden upsurge in demand.

Financials

Cash flow, capital investment, availability of working capital, legal obligations etc. are to be monitored. At the end of the day, the objective of any organisation is to make a profit out of a business. 

Trade zone analysis

Preparing a detailed report on geography with a proposal for a new distribution centre is essential. It needs to be checked whether the benefits of a free trade zone are available or not. Marketing potential also needs to be reviewed.

Sales and Operation Planning

Before setting up an efficient distribution network, any manufacturer should have a master plan for its sales and operation. The planning should be aligned with the demand and supply of its products and its financial planning.

Strategic decisions of the sales team include elements like whether the demand for a product in a specific geography should be more than it was in the last year or whether this demand should shift to a more promising geography. These strategic sales decisions influence tactical operational decisions like whether to increase or reduce the production capacity and manpower. Other long-term vendor management policies are also determined accordingly.

All these planning parameters are to be backed up by strong financial planning or budget. There are several challenges to a proper error-free sales and operation planning process. They are as follows –

Zeroing down on accurate information regarding supply and demand at a given point in time is a rigorous task.

In cases where demand shifts significantly from its previous earlier values, sales and operation plans need to be amended with the approval of top management.

Making a presentation to the top management incorporating all real and assumed parameters for the purpose of decision-making is challenging. Assimilating a database from multiple systems for making visible interactive reports is a complex activity.

Planning for new products as a result of demand shift or merger and acquisition of companies leads to newer hurdles for the team.

The latest technology trends have been incorporated into the sales and operation planning exercise. The usage of Enterprise Resource Planning (ERP) software and Supply Chain Management (SCM) software have become quite common nowadays. Besides the above-mentioned, Artificial Intelligence (AI) and the Internet of Things (IoT) have been introduced. 

Supply Chain Design

A real ground working model that elaborates on the structures or distribution outlets of the supply chain and the available logistics network for calculating the time and cost to deliver goods to the market is loosely understood as supply chain design. The model points out the errors committed in the system during the planning stage and flags potential risks involved in the process under different given conditions.

It aims towards reducing inventory, working capital and logistics costs and, in turn, increases operational efficiency, transparency and cost savings. The design model also aims to match supply to its demand under uncertain business scenarios by leveraging on its efficient inventory management skills.

It deals with strategic parameters like the number of distribution centres, location and size of the centres and deals with all global and domestic sourcing strategies. The design model is fully equipped to respond to all possible “what if” scenarios. It also has the flexibility to adjust to any shift in strategic decisions due to changes in the supply-demand curve.

Tips for Maintaining an Optimised Distribution Network

Optimising the distribution network is an ardent activity. Dynamic business conditions and varying parameters make the task difficult. However, a few tips may be followed to optimise the distribution network. They are as follows – 

Early engagement of top management of business owners and other stakeholders is a must-do-thing. A high-level meeting regarding network optimisation at the beginning clarifies many issues, which otherwise could have posed a serious threat due to wrong assumptions.

The meeting should be done in the presence of cross-functional leaders from both sides so that several overlapping functions get clarity right at the very beginning. All the parameters affecting network distribution should be chalked out and debated in a thread-bare manner.

The usage of commercially available modelling tools is better to tackle complex problems than homegrown spreadsheets. The model should be flexible to record all relevant parameters and able to show visually a tangible solution.

Inventory distribution should be the top planning criterion.  

A study or research on network distribution typically takes three to six months’ time. Organisations must allow this time for a stress-free operational experience in future. 

Conclusion

The supply chain management system has come a long way. A supply chain analytics course equips a prospective candidate with all the lessons to be learned. The candidates may find lucrative placement offers in the port and logistics companies besides other opportunities.     

The Professional Certification in Supply Chain Management and Analytics by Imarticus will enable the prospective candidate to boost their career towards a bright future. With the help of this supply chain analytics course, the candidates learn job-relevant skills from experienced IIT faculty. 

Visit the official website of Imarticus for more course-related details. 

Image Recognition and Computer Vision: Extracting Information from Images

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Image recognition is a result of the incredible fusion between artificial intelligence and computer vision that has led to the emergence of this technology. Image recognition software or applications take the help of camera technology and various AI models.

Image recognition technologies can distinguish and identify objects, people, texts and so on by extracting information from the images it captures. The emergence of this technology has revolutionised the industrial platform, be it pharma companies or retail shops and made brilliant opportunities for building a career in data analytics.

How does computer vision help in image recognition?

The role of computer vision is very pivotal in image recognition. It incorporates various processes like Optical Character Recognition (OCR) with the help of which it extracts textual data from images it captures. Two technologies are employed in this case: convolutional neural networks (CNN) and deep learning which generally use Python programming.

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With the aid of machine learning, computers can distinguish between images without the necessity of detailed programming. This is done by breaking down images into minute pixels and then making predictions based on that. By undergoing repetitive iterations, the predictions are made more accurate which is almost parallel to human perception.

Using CNN for image recognition

The Convolutional Neural Network inculcates a strong algorithm which it uses for image processing. It uses three different layers for analysing the images namely, Convolutional Layer, Pooling Layer and Fully-Connected Layer. 

In the Convolutional Layer, a small portion of input neurons is connected to hidden neurons. The dimensionality of the feature map is reduced by the Pooling Layer which follows the Convolutional Layer. In the end, the Fully-Connected Layer assesses the input data from the previous two layers and helps make assessments through predictions based on memory.

How does image recognition work?

There are a series of steps that are followed to convert images into textual data. These steps include:

  • Acquisition of image: this is the first step where the image is retrieved from an external source
  • Enhancement of image: this step involves changing the picture quality for better assessment
  • Restoration of image: this utilises certain mathematical tools for improving the quality of the image
  • Multiresolution processing: here the image is divided into smaller wavelets for the compression of data
  • Morphology-based processing and segmentation: analysis of images is done based on their shapes and then subdivided into smaller individual components
  • Description and representation: each component is analysed and quantitative information is derived

At the end of these steps, image recognition is made possible where the objects are tagged with a label centred on their characteristic features.

This entire process includes pre-determined signal processing methods which are employed to derive the information from the captured images. These methods include object visualisation, recognition, pattern measurement and so on.

Challenges faced in image recognition

The evolution in image recognition has brought with it several technological advancements. However, the advancements are augmented with various challenges and limitations which need to be overcome. The challenges are as follows:

  • Model generalisation improvement: the challenge here is to ensure that the system can run well in real-world scenarios that can differ from training and test sets. One finds varying distributions in real-world scenarios like different viewing angles, size of the objects and camera features.
  • Failure to read small and huge data sets: here the challenge is to enable the system to learn new data by introducing it to small and limited datasets in the beginning and utilising deep learning and machine learning to learn new information and ultimately recognise new objects. Similarly, another challenge here is that the current models lack the efficiency to read huge datasets to perform critical tasks.
  • Limitations to cognitive understanding: the challenge here is the inefficiency to go beyond just object recognition and achieving a cognitive understanding of objects to interpret inter-relationships between objects like humans to humans, humans to cars and so on.
  • Limitations to automate engineering of networks: the challenge here is that, instead of focusing on some specific features, the efforts are now to build novel network architectures. However, this is quite a difficult task involving myriads of parameters and choices.

Applications of image recognition

Some of the major arenas where image recognition is used are as follows:

  • Face Recognition: This is used in surveillance and security works. 
  • Remote Sensing: various sensors are used to extract information about a distant object. This is used in ships, aircraft and satellites to name a few.
  • Medical sectors: image recognition is being used in image diagnosis of a disease in medical sectors. It is also used in augmenting Computational Tomography (CT) scans and Magnetic Resonance Imaging (MRI).
  • Processing of Video: it is used to process visual data in television sets and other visual electronic systems.

Conclusion

A massive revolution in the industrial sector has been brought about by the advancements in technologies supporting image recognition and computer vision. Utilising deep learning and machine learning integrated neural connecting systems have been developed which aim at getting much better in the coming days. 

However, it has yet to overcome a number of challenges to attain its maximum potential. To gain expertise in such technological backgrounds you can check out Postgraduate Program In Data Science And Analytics provided by Imarticus. This 6-month long program will help data science aspirants with a better chance of securing a career in data analytics with a machine learning certification. 

A Guide to Getting Datasets for Machine Learning in Python

Posted on by Imarticus Learning

Welcome to the world of Machine Learning! 

Data gathering can be challenging when creating your first machine-learning project, especially for beginners. Finding datasets for machine learning is essential, but it may also be one of the most difficult parts of the process. Your ML model’s dataset serves as its building block, and you cannot train your model to provide reliable predictions without it.

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But don’t worry; this blog will demonstrate locating and getting the appropriate datasets for your Python ML project. You’ll discover where to hunt for datasets and how to obtain them using Python, whether you’re a professional or a student. 

Before diving into how to get datasets for machine learning in Python, let’s first understand what is machine learning.

What is Machine Learning?

Machine learning is basically a field of computer science and artificial intelligence that involves developing algorithms and statistical models. In other words, it’s a way for computers to automatically improve their performance at a specific task by learning from experience rather than being explicitly programmed.

If we talk about types, there are numerous machine learning types, such as supervised learning, unsupervised learning, and reinforcement learning, each with its own set of algorithms and techniques. In general, machine learning involves three main steps: preparing the data, training the model, and using the model to make predictions or decisions.

Furthermore, Machine learning has numerous applications, from image recognition and natural language processing to self-driving cars and personalized recommendations. It’s a rapidly growing field, with new techniques and models being developed all the time, and it’s expected to play an increasingly important role in many industries in the years to come.

Why is Python Used for Machine Learning?

Python has become a favored linguistic medium for machine learning due to its ease of use, versatility, and an extensive assortment of libraries and utilities. Python was the third most in-demand language among recruiters in 2022, according to Statista.

Some of the key essentials why Python is used for machine learning are:

Easy to learn: Python has a simple and intuitive syntax that makes it very easy to learn and use, even for those without a background in programming.

Rich library ecosystem: Python has a vast collection of open-source libraries that support various machine learning tasks, such as data preprocessing, feature selection, model building, and evaluation.

Strong community support: Python has a large and active community of developers who directly contribute to developing machine learning libraries and tools, making it easier for users to find resources and get help with their projects.

Versatile: Python is a universal language for various tasks beyond machine learning, such as web development, data analysis, and scientific computing.

Scalability: Python has robust support for distributed computing, making it possible to scale up machine learning applications to handle large datasets and complex models.

How to Find Datasets for Machine Learning in Python?

Choosing the right dataset is crucial for the success of your machine learning project. Here are some ideal factors to consider when choosing Python Machine Learning Dataset Libraries:

  • Size: The size of the dataset must be large enough to be representative of the problem you are trying to solve. However, it should also be manageable and not too large that it becomes difficult to work with.
  • Quality: The quality of the dataset is also essential. Ensure that the dataset is accurate, reliable, and free from errors or biases.
  • Relevance: Choose a dataset that is relevant to your problem statement. The dataset should contain useful features for solving the problem you are trying to address.
  • Data Type: Consider the data type you are working with, whether numerical, categorical, or text. Choose a dataset that matches the data type of your problem.

How to Preprocess Datasets?

Preprocessing datasets is an essential step in machine learning that involves cleaning and transforming raw data into a correct format for machine learning algorithms. Here are some common preprocessing techniques:

  • Data Cleaning

 Data cleaning involves removing or correcting errors and inconsistencies in the dataset. This step is crucial in ensuring that the dataset is accurate and reliable.

  • Data Transformation

Data transformation simply involves converting the data into a format that machine learning algorithms can quickly analyze. Common techniques include normalization and standardization.

  • Feature Engineering

Feature engineering involves selecting and creating relevant features for the problem statement. This step can improve the model’s accuracy and reduce the data required to train it.

Ending Note

Obtaining high-quality datasets is essential to any successful machine-learning project. With the tools and resources available in Python, it’s easier than ever to collect and preprocess data for use in machine learning models.

Imarticus Learning Certificate Program in Data Science and Machine Learning is a great place to start for those who want to learn more about data science and machine learning. This curriculum, developed with iHUB DivyaSampark @IIT Roorkee, gives students a solid foundation in data science and machine learning ideas and the practical skills they need to put these concepts into practice and apply them to real-world issues.

With the right training and resources, you can become a skilled machine learning practitioner and make a real impact in data science.

Why a Data Analytics Course is the Next Best Thing

Posted on by Imarticus Learning

With the internet ushering us into an age of information, accessing data has never been easier. Data is widely prevalent in every industry and all stages of human society. From governments to MNCs, businesses of all sizes depend on data for their continued growth and existence. This is precisely why a career in data analytics right now is highly rewarding, with innumerable growth opportunities.

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Data analytics is a fast-expanding field that involves studying big data sets to gain information so as to make educated choices. Opting for a data analytics course can be the best course of action for those who wish to develop the relevant skills and knowledge required to become a professional data analyst.

Read on to learn how pursuing a data analytics course in 2023 will benefit you in the near future.

How data analytics can help professionals

Data analytics is a dynamic and demanding field requiring unique analytical skills and resourcefulness. As a data analyst, you will collaborate with various teams to provide insights on improving their processes. 

To become a Data Analyst, you must have certain technical abilities, such as statistics and R or Python programming. Even those who do not wish to become full-fledged Data Scientists can benefit from learning data analytics. 

Being data-literate assists one in finding answers hidden in vast datasets that can address a range of issues. Hence, enrolling in a data analytics course can be an ideal method to gain the requisite knowledge and expertise for a successful career in data analytics or to improve one’s data literacy.

Reasons to study data analytics 

Training in data analytics can help candidates land various job roles across industries, for instance, Data Scientist, Data Engineer, Data Architect, or data analytics professional. The growing scope of the sector, along with its other advantages, make it a lucrative career option for students as well as working professionals looking for a career switch.

Below is a list of reasons you should choose a data analytics course.

  • Fast-track your career 

Data analytics training can help you fast-track your career and apply for top-paying jobs. Studies reveal working professionals who completed a data analytics course experienced a considerable salary hike.

  • High demand 

The main driving force behind the increasing popularity of data analytics courses is a high demand for skilled data analysts across various industries, including finance, healthcare, and technology. 

One study found that data analysis skills are so in demand that even non-technical managers can expect a significant raise in their salary by learning these skills. LinkedIn states it is among the top skills employers seek in the current job market.

  • Flexible online learning options 

Many certification and certificate courses are available online, allowing flexible learning options that fit your schedule. For example, Google’s Data Analytics Professional Certificate is a flexible online data analytics course.

  • Specialisation options 

Depending on your interests and career goals, you can specialise in different areas of data analytics, such as data visualisation, machine learning, or predictive analytics. This allows you to adjust your learning to your career objectives and enhance your expertise in a specific area. Some popular domains of data analytics include Marketing Analyst, Financial Analyst, Sales Analyst and Operations Analyst.

  • Hands-on experience

Many data analytics courses go beyond theory-based learning to offer hands-on experience with real-world datasets, allowing you to hone your practical skills and apply your learning in a professional setting.

  • Improve decision-making

By gathering new insights from data, data analytics skills can assist organisations in making better decisions pertaining to their daily operations and future too. You can acquire skills that can be employed in a range of jobs and sectors by taking a data analytics course. 

  • High earning potential

Data analytics specialists have incredible income potential, with an average annual salary of INR 903,864 in India. Earning potential is predicted to rise as the need for experienced workers in this industry grows.

  • Broad working spectrum

A career in data analytics offers prospective candidates the freedom to choose from a wide set of industries according to their personal preferences. You can land jobs in marketing, business intelligence, finance, sales, data assurance, data quality, etc., departments of an organisation. Professionals with a data analytics course can expand within the same organisation by switching job roles.

Conclusion

Do you wish to bolster your technical skills, enhance your decision-making abilities, and be a part of an exciting, fast-growing sector with enormous potential? Then a data analytics course is the next best thing for your career. Help organisations turn data into valuable insights and have a meaningful impact on the world with the necessary skills and knowledge.

Enrol in Imarticus’s Postgraduate Programme In Data Science And Analytics to avail yourself of exciting career opportunities. This 6-month long course is taught in hybrid mode through online and classroom learning. With a job-centric curriculum teaching practical applications of SQL, Power BI, data analytics, Python, tableau and much more, candidates will land assured jobs with top-tier companies. 

For more details, visit their website now!

Ultimate Machine Learning Guide for 2023

Posted on by Imarticus Learning

As we approach 2023, machine learning (ML), a subset of artificial intelligence, has shown to be a crucial skill in the digital commerce industry. With the increased demand for intelligent systems and automation, businesses are increasingly resorting to ML to stay ahead of the competition. 

The future of machine learning is promising, with a projected CAGR of 38.8%, reaching $209.91 billion by 2029. The tech industry is enhancing productivity, decision-making, product and service innovation, and customer journey by deploying machine learning-based solutions. 

This blog will look at machine learning principles, cover the latest trends and breakthroughs, and present tools to assist you in tackling the area in 2023.

What is machine learning?

Machine learning (ML) is a subfield of artificial intelligence that leverages artificial neural networks and focuses on creating computer systems that may improve their performance via experience and data analysis. 

Simply put, machine learning is the process of developing models or systems that can learn from data without being explicitly programmed for specific tasks. Instead, these algorithms are intended to recognise patterns, form predictions, or perform actions depending on the data they are subjected to.

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Machine learning algorithms can be classified into various types, including supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning – Models learn from labelled data in supervised learning when the desired output or target is provided with the input data. 

Unsupervised Learning – It is the discovery of patterns or structures in unlabeled data in the absence of explicit target labels.

Reinforcement Learning – Reinforcement learning teaches agents how to perform in a given environment to achieve maximum rewards or outcomes.

Key concepts and techniques of machine learning

  1. Deep Learning: A subclass of machine learning, deep learning involves artificial neural networks inspired by the human brain. It possesses distinguishing features, including its capacity to learn hierarchical representations from unstructured input and its applications in image identification, natural language processing (NLP), and other areas. 
  2. Data Preparation and Feature Engineering: Data preparation and feature engineering are essential steps in the machine learning workflow to improve the performance and effectiveness of machine learning models. 

Data preparation involves cleaning, transforming, and organising the data, while feature engineering involves creating new input variables from existing raw data. Transformations, interaction terms, and domain-specific knowledge can be used to generate new features. 

Model Training and Evaluation: Model training and assessment are critical processes in the machine learning workflow, encompassing the process of training and evaluating a machine learning model on a dataset. 

Model training is the process of teaching a machine learning model to produce correct predictions by learning from the available dataset. The training set trains the model by feeding it input data and known outputs, adjusting its parameters through optimisation algorithms.

Advanced topics and applications

Some of the major applications that are shaping the future of machine learning are -: 

  • Explainable AI: Explainability is becoming increasingly important as machine learning systems become more complex. Explainable AI is the development of AI models and systems that can provide understandable explanations for their outputs and decision-making processes. Various methods for evaluating and explaining machine learning models help improve transparency, trust, and moral concerns. 
  • Reinforcement Learning: Various domains have benefited from successful applications of reinforcement learning, including robotics, games, recommendation systems, and autonomous vehicles. Agents in these applications are trained using RL algorithms like Q-learning and policy gradients, which enable them to learn optimal strategies or policies through trial and error. 
  • Generative Models: Generative models are machine learning models that learn the underlying probability distribution of data and generate similar samples. Generative models have various applications, including image generation, text synthesis, and data augmentation. Examples of generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models.

Trends and forecast for 2023

Edge Computing: Edge computing in machine learning is the practice of performing computation and data processing at the network’s edge, closer to the source of data generation or the end user. It reduces latency and network bandwidth requirements, enhances privacy and data security, and enables offline or intermittent connectivity scenarios. Edge computing in ML has proved useful in healthcare monitoring systems, autonomous vehicles, industrial IoT, and video surveillance.

Federated Learning: This ML approach trains models across various decentralised devices while maintaining data privacy and security.

Responsible AI: It is a methodology for designing, evaluating, and implementing AI systems in a safe, trustworthy, and ethical manner. It emphasises the importance of possible effects and outcomes of AI systems at every stage, including their creation, implementation, and utilisation.

Conclusion

Machine learning will affect a variety of businesses in 2023 as well as in future years. You can harness the great potential of machine learning in this fast-expanding world by grasping the underlying ideas, keeping yourself up-to-date on the newest developments, and honing your abilities through practical applications and learning materials. 

If you are still wondering what is machine learning, enrol in Imarticus’s Certificate Programme in Data Science and Machine Learning to learn the core concepts of the field and start on an exciting career path.

Top Supply Chain Analytics Courses To Look Out For in 2023

Posted on by Imarticus Learning

In today’s dynamic business world, supply chain analytics plays a vital role in assisting organisations in making informed decisions, optimising their supply networks, and gaining the upper hand over their competitors. 

Whether you are a supply chain professional looking to advance your career or an aspiring analyst looking for an introduction into supply chain analytics, enrolling in a top-tier supply chain analytics course can give you the knowledge and expertise you need to succeed. 

This blog will look at some of the best supply chain analytics courses to watch out for in 2023.

Introduction into supply chain analytics

Supply chain analytics is critical in enhancing supply chain management, operations, and efficiency by employing data analytics methodology and tools. It entails analysing massive amounts of data modern supply chains produce to obtain insights, identify trends, and address shortcomings. The introduction of computer-based technologies, such as artificial intelligence (AI) and machine learning (ML), has dramatically improved supply chain analytics in recent years.

Best supply chain analytics courses

Top Supply Chain Analytics Courses

Pursuing a supply chain analytics course can help expand your expertise and contribute to better decision-making, and drive efficiency in supply chain operations. Here are some of the top courses you can consider:

  • Supply Chain Analytics: Rutgers the State University of New Jersey

Offered by Rutgers University, this comprehensive course focuses on supply chain analytics and equips learners with the necessary skills to generate actionable insights for effective decision-making. It covers data-driven decision-making, demand forecasting, inventory management, and optimisation techniques. Through hands-on exercises and real-world case studies, students gain practical experience in applying analytics to supply chain scenarios.

Duration: 5 months approximately

Fees: N/A

  • RA: Data Science and Supply Chain Analytics (A-Z with R) by Udemy

This course on Udemy provides a comprehensive overview of data science and supply chain analytics using the R programming language. With a focus on practical applications, this course empowers learners to analyse supply chain data and extract valuable insights. Students will learn to leverage data science techniques for supply chain optimisation, inventory management, forecasting, and revenue management.

  • Professional Certification in Supply Chain Management & Analytics by Imarticus Learning

Designed in collaboration with IIT Roorkee, the course covers supply chain performance, drivers and metrics, designing the supply chain or distribution network, planning and coordinating demand and supply, sales and operations planning, managing uncertainties in a supply chain, determining the optimal level of product availability and more. The programme offers project-based learning focused on helping candidates tackle real-world scenarios using cutting-edge tools and technology

  • Executive Programme in Supply Chain Management and Analytics at IIT Delhi

This online programme is specifically designed for working professionals aspiring for a role in logistics and supply chain management. The key learning aspects of this course include an in-depth knowledge of supply chain analytics, multi-criteria decision-making techniques like AHP, TOPSIS, DEA, regression models, etc. This case-based training programme helps participants understand and solve real-world problems. Post course completion, candidates get the prestigious IIT Delhi certificate.

  • Supply Chain Analytics at MIT Cambridge

This online course is part of MIT’s MicroMasters programme with edX. Designed by eminent MIT faculty, the course exposes students to industry-relevant, real-life case studies training them to handle various analytical tools and techniques to manage supply chains better. The programme commences with an introduction to probability and moves on to regression analysis and statistics.

  • Demand and Supply Analytics at Columbia University, New York

The course provided by edX in collaboration with Columbia University is ideal for students looking for a career switch or professionals looking to bolster their knowledge in demand and supply analytics. The programme focuses on basic analytical methods, inventory management, applying analytical methods in real-life supply chain problems, and stochastic inventory management. It also helps students develop predictive abilities to identify and mitigate issues before they can hinder operations.

Conclusion

To excel in the field of supply chain analytics, professionals need to acquire the necessary skills to work effectively with data. Supply chain analytics has become increasingly vital in today’s business landscape, and keeping up with the latest techniques and tools is essential for professionals in this field. Whether new to the field or looking to upskill, enrolling in a supply chain analytics course offers valuable knowledge, practical experience, and industry-relevant insights. 

Imarticus Learning’s Professional Certification in Supply Chain Management and Analytics, designed by IIT Roorkee, can land high-demand job roles like Demand Planner, Supply and Operations Planner and Supply Planner. By enrolling in this supply chain analytics course, you can expand your expertise, contribute to better decision-making, and drive efficiency in supply chain operations.

A Guide to Basic Python Programming

Posted on by Imarticus Learning

To learn Python means to practice Python. Python is a popular high-level, interpreted, interactive, and object-oriented programming language invented by Guido van Rossum in 1991. Its popularity rests on its simplicity, readability, and ease of use, making it a popular option for novices and professionals. 

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It may be processed in a procedural, object-oriented, or functional style and operates on an interpreter system. It features a clean and focused syntax centred on readability and a vast and active community offering assistance, tools, and libraries to help developers solve issues and build new things.

Getting started with Python

Here are some key points to consider:

Choose an Integrated Development Environment (IDE) or code editor to develop and execute Python code. Some prominent choices are:

  • PyCharm 
  • Visual Studio Code 
  • Sublime Text 
  • Atom 
  • IDLE

Install Python on your PC. You may grab the latest version of Python from the official website: https://www.python.org/downloads/

Learn the basic principles of Python programming language, including:

  • Variables and data types
  • Operators
  • Control structures (if/else statements, loops)
  • Functions
  • Modules and packages

Practice developing Python code by tackling exercises and challenges. Some resources for Python exercises are: https://pynative.com/python-exercises-with-solutions/

https://www.w3resource.com/python-exercises/python-basic-exercises.php

Join online groups and forums to network with other Python developers and seek support when required. Some popular communities are:

  • Reddit’s r/learnpython
  • Python Discord server
  • Stack Overflow

Read Python documentation and tutorials to learn more about the language and its capabilities. Some resources for Python tutorials are: 

https://docs.python.org/3/tutorial/index.html

https://realpython.com/ 

Data types in Python

Python includes various built-in data types used to represent different sorts of data. Here are the most popular data types in Python

Numeric types: int, complex, float 

String type: str

Sequence types: list, range, tuple

Set types: frozenset, set

Mapping type: dict

Boolean type: bool

None type: None

Binary types: memoryview, bytes, bytearray

Here are a few examples in detail:

Integers: Whole numbers, such as 1, 2, 3, and so on.

Floating-point numbers: They are decimals, such as 7.21, 5.168, etc.

Tuples: Tuples are ordered, immutable collections of elements, such as (1, 2, 3), (“apple”, “banana”, “cherry”), and others.

Strings: They are sequences of characters, such as “hello”, “world”, etc.

Dictionaries: Dictionaries are unordered collections of key-value pairs, such as {“name”: “John”, “age”: 30}, {“fruit”: “apple”, “colour”: “red”}, etc.

Lists: They are ordered collections of elements, such as [“apple”, “banana”, “cherry”], and others.

Here’s an example of how to create variables of different data types in Python:

a = 42

b = 3.14

c = 2 + 3j

str_var = “Hello, world!”

lst_var = [1, 2, 3]

tpl_var = (4, 5, 6)

rng_var = range(10)

dct_var = {“name”: “Alice”, “age”: 30}

st_var = {1, 2, 3}

fst_var = frozenset({4, 5, 6})

bool_var = True

bts_var = b”hello”

ba_var = bytearray(bts_var)

mv_var = memoryview(ba_var)

none_var = None

Functions in Python

Blocks of code called functions may be reused to carry out different tasks. Using the def keyword in Python, you may create your functions. Here is an example of a function that computes a number’s factorial:

def factorial(t):

    if t == 0:

        return 1

    else:

        return t * factorial(t – 1)

The following is how your Python code should call this function:

def factorial(n):

result = 1

for i in range(1, n + 1):

result *= i

return result

num = 5

print(factorial(num)) # Output: 120

Control structures in Python

Python has several control structures that let you manage the application’s flow. These consist of try-except blocks, for loops, while loops, and if-else expressions. An overview of various control structures is provided below:

If-else clauses: If-else clauses let you run several chunks of code in response to a condition. For instance:

if y > 0:

print(“y is positive”)

else:

print(“y is non-positive”)

For loops: For loops allow you to iterate over a sequence of elements. For example:

for k in range(20):

    print(k)

While loops: While loops let you continually run a section of code as long as a condition is met. For instance:

x = 0

while x < 10:

print(x)

x += 1

Try-except blocks: Try-except blocks allow you to manage potential exceptions in your code. For instance:

try:

    num = int(input(“Enter a number: “))

    result = 1 / num

except ValueError:

    print(“Invalid input”)

except for ZeroDivisionError:

    print(“Cannot divide by zero”)

else:

    print(“Result:”, result)

This guide covers Python fundamentals such as data types, control structures, functions, and modules. The best way to learn Python is to start with the basics and then slowly master it by practising coding or taking part in a workshop. 

Conclusion 

Imarticus Learning offers a Postgraduate Programme in Data Science and Analytics (PGA). This programme uses data science and creates analytical models that enhance corporate performance. Machine learning and Python programming are among the basic and advanced data science and analytics techniques covered in the curriculum. The course is one of the best to learn Python for beginners. It offers certification programmes and job assurance.

An Introduction to Warehouse and Order Management

Posted on by Imarticus Learning

Warehouse and order management are critical components of supply chain operations, ensuring efficient goods flow and storage. Warehousing simply means working on where and how to keep goods before they are delivered directly to customers or shops or before being displayed. 

Order management, on the other hand, deals with the entire process starting from order placing to order delivery.

Employing the best warehouse and order management practices is essential for businesses, regardless of their size, to optimise space use, minimise time lost in finding items, maintain optimal product condition and avoid delivery delays. 

This article will answer what is warehouse order management and discuss in detail its importance, fundamental principles, and best practices.

Introduction to warehouse operations and management 

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Warehouse management entails effectively controlling and optimising numerous activities within a warehouse facility. Its primary goal is to guarantee that products move smoothly from receiving and storing to collecting, packing, and shipping. Efficient warehouse management reduces inventory holding costs, optimises space usage, and improves processes pertaining to the completion of orders.

Key functions of warehouse management

The primary tasks involved in warehouse management include : 

Receiving: Incoming items are inspected and recorded, quantities and quality are verified, and inventory records are updated. Accurate and timely receiving improves inventory accuracy and allows for efficient planning of subsequent warehouse operations.

Inventory and Storage Management: Warehouse managers must strategically plan and store products to maximise space utilisation and enable effective order pickup. Inventory management systems are used to keep track of stock levels, monitor replenishment requirements, and avoid stockouts or overstock situations.

Order Picking: The process of removing products from storage locations to complete customer orders is known as order picking. It entails using effective picking methods such as batch picking, zone picking, or wave picking to boost production and reduce trip time inside the warehouse.

 Packing and Shipping: Orders must be carefully packed after they are picked to ensure that the goods are not compromised during shipment. Warehouse management involves packaging, labelling, and working with shipping carriers to ensure on-time and precise order delivery.

An overview of order management

Order management deals with the entire process of receiving, processing, and filling client orders. Coordination among other divisions, such as sales, inventory, and logistics, is required to maintain smooth order flow and customer satisfaction.

Key components of order management

Order Processing: Order processing includes processes such as order entry, validation, and verification, as well as customer information, product availability, and pricing. Efficient order processing ensures accuracy, minimises errors, and allows for timely order fulfilment.

 Order Fulfilment: Order fulfilment involves collaborating on the collection, packing, and shipping processes within the warehouse. The aim is to provide clients with accurate items, on schedule and in perfect condition.

 Order Tracking and Customer Communication: Providing customers with real-time order tracking information is an integral part of order management. Effective communication about order status, shipment tracking information, and anticipated delays increase customer satisfaction while minimising queries.

Returns and Exchange Management: Order management includes handling of product returns, exchanges, and refunds. Streamlining the refund and exchange process and managing customer expectations increase consumer loyalty while reducing operational disruptions.

Best practices in warehouse and order management

Implementing the best warehouse and order management practices ensures the overall improvement of operations, safety, productivity, space utilisation and inventory control. Given below is a list of the best practices that can help businesses manage the entire process efficiently.

  • Warehouse Management Systems (WMS) Implementation: WMS software automates and optimises warehouse operations, promoting inventory accuracy, order fulfilment efficiency, and overall productivity. 
  • Using Technology: Employing technology like barcode scanning, RFID (Radio Frequency Identification), and automation systems improves inventory visibility, minimises errors, and accelerates order processing. 
  • Continuous Process Improvement: Reviewing warehouse and order management procedures regularly, finding interruptions, and adopting process changes can result in increased operational efficiency and customer satisfaction. 
  • Collaboration and Integration: Effective warehouse and order management requires seamless coordination and information exchange among multiple parties involved, including suppliers, manufacturers, and transportation providers.

Conclusion

Warehouse and order management are essential elements of supply chain operations, ensuring that commodities are delivered from suppliers to clients promptly and efficiently. Businesses may optimise their operations, improve customer experience and satisfaction, and gain a competitive advantage in the market by knowing the fundamental concepts and best practices in the warehouse and order management.

Imarticus Learning and E&ICT and IIT Guwahati offer advanced certification in Digital Supply Chain Management. The course specifically trains SCM professionals in using technology in logistics, procurement, inventory and vendor management. The new-age digital programme has an industry-focused curriculum and offers the unique opportunity to learn from real-life case studies.