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How does sales and operations planning work in a Supply Chain

Posted on by Imarticus Learning

How Does Sales and Operations Planning Work in A Supply Chain

Meta Description: Benefits of sales and Operations planning in the supply chain. Know more about a career in supply chain and operations

What is sales and operations planning?

One of the most challenging aspects of a supply chain is sales and operations planning. However, it is also a crucial variable on which the success of a business depends. The main goal of the planning is to create a unified business plan. Those planning to have a career In Supply Chain and Operations need to learn it works.

The sales and operations planning gets inputs from teams like:

·        Manufacturing

·        Distribution

·        Marketing and

·        Finance

Businesses combine these inputs to simplify the process for stakeholders. It also helps them to create a support system for the same.

Need for sales and operations planning

The goal of sales and operations planning is to mix the components and activities of demand and supply. It should revolve around a consensus-based plan. Building consensus is easier with planning in place. If we don’t bring the team together under planning, it is impossible to make a successful design.

Thus, sales and operations planning provides a holistic view of every component. Then collective adjustments and inputs are made. This collaboration creates a resilient and shock-proof supply chain. Which, in turn, results in the success of a business.

Steps to create sales and operations planning

Generally, senior management works on sales and operations planning. Following are the steps in the process:

1. Forecasting

Data and trends are the major components of sales and operations planning. At first, data on the sales history is collected. During this stage, it is important to consider internal and external variables. These components affect sales like customers, competition and industry demands.

Then the data aids to forecast future sales. It helps to analyse and identify trends. It then builds the process in a supply chain based on the forecast.

2. Demand planning

Demand is a major factor that runs sales and operations planning in a supply chain. In this process, cross-collaboration is important. After the forecasting analysis, businesses adjust the inventory and customer service policy. The adjustments depend upon factors like demand and the source of the product.

According to the analysis of revenue and units, the businesses measure demands.

3. Supply planning

Another major aspect of sales and operations planning is sales planning. During this process, teams assess the capacity of the supply chain and products. The finance, operations and materials teams meet for the assessment. They determine the issues related to

·        Manpower

·        Machinery

·        Suppliers and

·        Distributors

During this process, the teams create a supply chain plan to check the limitations of capacity. They also work to come up with ways how to deal with the problems.

4. Pre-sales and operations planning

The first meeting of planning in the supply chain is crucial. Various teams take part in this process namely:

·        Sales

·        Finance

·        Marketing

·        Materials

·        Operations

·        Product management

·        Human resources

The teams cooperate and compare the forecasting analysis of the demand and supply components. They also discuss the financial impact of both plans.

5. Executive sales and operations planning

The executives of teams meet to analyse the demand and supply plans. They also go through forecasts and recommendations. In the meeting, the executives approve the final sales and operations plan.

6. Finalization and implementation

After approval of the plan, the teams work on implementing it. Then, they assess the factors for implementation. It is important to assess the sales and operations planning regularly. It helps to find faults within the processes.

Benefits of sales and operations planning in supply chain

An integrated and collaborative sales and operations planning ensure:

·        Increase in responsiveness

·        Reduction in operating cost

·        Increase in productivity

·        Increase in revenue

·        Increased transparency between departments

·        Improvement in inventory management

·        Creation of a resilient supply chain

·        Demands of customers are met on time

·        Improvement in budget and sales forecasting

·        Improvement in decision-making about demand and supply

·        Comprehensible understanding and management of a product at every stage

·        Improvement in sales and customer experience

Challenges of sales and operations planning

·        Understanding the complexity of the processes

·        Choosing right sources

·        Maintain transparency between departments

·        Coordination between all stakeholders

·        Scaling progress of a current process

·        Lack of coordination while taking important decisions

·        Usage of all data and analytics

Career In Supply Chain and Operations

For a career in supply chain and operations, you will need the best leadership program for supply chain professionals. Learners can choose from a wide variety of jobs like

·        Client Management

·        Consultative sales

·        Program Management

·        Consulting

·        Supply chain operations

·        Manager Technology

·        Executive

·        Manager Program Management

Why choose Imarticus Learning?

Imarticus Learning offers a certification program for Chief supply chain and operations officers. The program is offered in collaboration with IIM Raipur.

The 10-month-long program is offered in online mode. The 6-module program has benefits for the learners such as:

·        C-suite training

·        SCM Globe simulations

·        Masterclasses by industry experts

·        Campus immersion

·        IIM Raipur alumni status

To know more about how you can have a Career in Supply Chain And Operations, click here.

10 Must-Know Concepts For Your Data Science Online Training

Posted on by Imarticus Learning

Enrolling in a data science online training course from a prestigious institution such as the Indian Institute of Technology (IIT) guarantees that we will be learning from experts in the field. IIT Roorkee’s Data Science and Machine Learning Course is one of the best data science online courses available. The course covers the essential concepts that are essential to any data science online training, such as machine learning algorithms, linear regression, data visualisation, and communication. Completing the course can open up the doors to a prosperous and fulfilling data scientist career.

Data Science Course

However, before we begin our journey with The IIT Roorkee Data Science Online Course, we need to be familiar with foundational concepts of data science that can make our learning journey smoother. Mastering these concepts beforehand will also enable us to understand the IIT Roorkee Data Science Online Course better and help us make the most of it.

Here’s a list of 10 must-know concepts for our data science online training:

1. Datasets

The most basic yet most important concept we must know is the dataset. A dataset is a collection of data points, usually consisting of different variables such as numerical values and textual information. Datasets are an essential part of data science and machine learning projects because they provide the information needed to train models. Understanding how to select, manipulate, and analyse datasets is crucial for data science online training.

2. Data Cleaning & Pre-Processing

Dirty and unstructured data can impede our data science projects. Data cleaning and pre-processing are the processes of making sure our datasets are in a usable state. It involves dealing with issues such as missing values, outliers, or incorrect data types.

3. Data Science Tools & Technology

Data science tools and technologies such as Python, R, SQL, Tableau, Hadoop, and Spark are used to manipulate data and create models. It is important to understand the purpose of each technology and its advantages and disadvantages so that we can choose the right tool for the job.

4. Machine Learning

Machine learning is a subfield of artificial intelligence that enables computers to learn from data and make decisions without explicit programming. Understanding the basics of machine learning algorithms is a key concept for data science online training and IIT Roorkee’s Data Science Online Course.

5. Regression & Classification

Regression and classification are two of the most popular machine-learning algorithms used in data science projects. Regression is used to predict the outcome of a continuous variable such as stock prices or house prices. In contrast, classification can be used to classify data into different categories, such as spam or not spam.

6. Data Visualisation

Data visualisation is the process of transforming raw data into visual representations, such as charts and graphs. It is used to make data easily accessible and understandable to humans. With IIT Roorkee’s Data Science and Machine Learning Course, we can learn the fundamentals of data visualisation and use tools such as Tableau and D3.js to create stunning visualisations.

7. Natural Language Processing

Natural language processing (NLP) is a subfield of artificial intelligence that deals with understanding and generating human language using computers. It is a rapidly advancing field in data science, and many modern applications use NLP, such as chatbots and text-based search engines.

8. Big Data

Big data refers to large, complex datasets that cannot be processed using traditional methods. IIT Roorkee’s Data Science and Machine Learning Course will teach us how to use big data tools such as Hadoop and Spark to process, analyse, and visualise big datasets. Thus, we need to familiarise ourselves with big data concepts and technologies.

9. Mathematical & Statistical Concepts

Mathematical and statistical concepts are foundational for data science. We need to be strong in topics such as linear algebra, calculus, and probability to understand the underlying mathematics behind many data science algorithms.

10. Data Science Real-World Applications

Finally, we need to understand the real-world applications of data science. We can use data science for a wide range of tasks, such as predicting customer churn, forecasting sales, or detecting fraud. Being cognizant of the potential of data science can help us with projects in the IIT Roorkee Data Science Online Course.

Data science is a rapidly evolving field. As per estimates, the number of data science jobs is expected to increase by 28% in the next five years. In India alone, there will be about eleven million job openings in the data science field. Moreover, there is a shortage of data scientists in the job market globally.

Thus, embarking on a data science journey with IIT Roorkee’s Data Science and Machine Learning Course is a great way to develop the skills needed to stay ahead of the competition. Following the 10 must-know concepts outlined above, we will be well on our way to becoming successful data scientists!

Four Ways to Apply Data Visualisation to Your Daily Life

Posted on by Imarticus Learning

Data is present everywhere. We are surrounded by different forms of data, be it Google Maps or diet planners. One of the major reasons for the widespread use of data is the ease of communication. Visuals communicate more easily and effectively than words. Medieval paintings are a testament to the fact that people have used data visualisation since time immemorial for conveying complex ideas.

Keep reading to understand the meaning and logic behind data visualisation.

What is Data Visualisation?

data analytics courses

Raw, written data is available in both organised and unorganised forms. Using data visualisation, it is translated into visible forms like graphs, pie charts, videos, GIFs, reports and images. You do not have to go through large chunks of data. You can quickly understand the trends in the data through visuals. Companies use these techniques for the process of decision-making.

Four ways to apply data visualisation in real life

Raw data is of no use to a company. The primary aim of processing data and generating visuals is to easily identify the patterns in big data. Companies all around the world are seeking individuals with knowledge of big data and its applications. Let us understand how to perform the visualisation of big data in the real world.

Bar Charts: Bar charts are one of the best visualisation options for comparing two or more quantities. You can use it to analyse your income and spending in different months of the year. Companies can use this tool for measuring the performance of their internal departments against each other.

Mapping: Maps are used for understanding the distribution of resources or products for a company. You can use a map for planning the itinerary of your international or local trips. If a company has operations overseas, a map helps in comparing the performance of different branches against that of the headquarters.

Matrix: A matrix is a visual representation that displays the existence of several factors (or groups) in a general location. If you have a big data set, a matrix helps in understanding the relationship between different elements of this data set. You can use a matrix for identifying the cause of a problem. Thus, you can allocate resources according to the needs of different areas.

Box Plots: A box plot helps in understanding the range of a data set and several other characteristics of a given group. From this representation, you can understand the outliers, range and variance of a particular data group. It can then be compared with others. Using this representation, you can check if there are any unused groups in a data set.

Is data visualisation scientific?

Data analytics (or visualisation) is much more than turning written data into visuals. The process of accomplishing this goal has a lot of scientific reasons. They show how humans gather and process any type of information.

According to several psychologists, the human thinking process can be generally categorised into two systems. System one is responsible for reflex thoughts. These refer to the ideas or solutions that the brain gives without processing any information. On the contrary, system two generates thoughts and solutions based on the results obtained after processing the information received from outside.

When system one receives data in a familiar format, it becomes easier for system two to process it more efficiently. System one is used to immediately process the information received from our visual senses. Thus, it is better if data is represented visually to allow better decision-making.

Start a Data Analytics Course with Placement

Imarticus Learning offers PG in Data Analytics and Machine Learning. This course can be helpful for aspiring data scientists. It guarantees job interviews opportunities in renowned companies. These include HSBC, Standard Chartered, ICICI Bank, Infosys, Wipro, HDFC Bank, Deloitte, Oracle, and many more. The curriculum is designed by professionals to include the latest job-relevant skills using tools, packages and programming languages. They include Python, Spyder, pandas, NumPy, OpenCV, Colab and TensorFlow.

This course demands the completion of 25 projects. These will help you understand concepts better and improve your job prospects. These real-world projects are completed in class. You can add these to your resume as a part of your portfolio. Additionally, you can be a part of competitions like Hackathon. You can also participate in mock interviews to improve your skills. The professionals at the institute will help you create LinkedIn and GitHub profiles to attract employers.

Learn data analytics by visiting the website. You can interact with professionals for solving your queries through call or chat support on the website. You can also visit one of our centres.

Interested in taking the Certificate Program in Data Science and Machine Learning? Read on

Posted on by Imarticus Learning

Today, Machine learning is being leveraged in almost every sector to bring more efficiency, intuition, and applicative use to products and services. The global machine learning market is expected to grow at a compound annual growth rate of 38.8% between 2022 to 2029, from a value of $ 21.17 Billion in 2022 to $209.91 billion. On the other hand, the market size of data science platforms market size is estimated to become a $ 378.7 billion industry by 2030, growing at a Compound annual growth rate of 16.45 3%, between 2022 to 2030.

As the applicative uses of these two functions keep growing, various sectors will experience a growing demand for skilled professionals with expertise in data science and machine learning. In this blog, I delve deeper into these domains and review the skill sets, and growth opportunities available. I also talk about how pursuing a contemporary machine learning certification course can give you a competitive edge in the job market.

The growing importance of machine learning

Today, consumers are growing increasingly dependent on the benefits of machine learning for various purposes. Here is an example. Machine learning is leveraged by a popular language learning app to curate an intuitive learning experience. The app can gauge the proficiency level of the learner and adapt a gamified learning plan, based on the data captured. This application completely eliminates the need for human teaching intervention. It enables millions of learners to learn simultaneously but at their own pace. It also enables them to learn through a format best suited to them. The application of machine learning is enabling platforms to reduce costs, and grow at scale. There are many such examples of how machine learning is transforming lives daily.

Machine learning simplified

So what exactly is machine learning? It is a branch of artificial intelligence (AI) and computer science, which mirrors how consumers engage and learn, aided by data usage and algorithms. The accuracy of engagement and responses gradually improves. Those engaged in machine learning work are working at the cusp of innovation. Having an in-depth understanding of data science coupled with the principles of machine learning can give one an edge in this domain.

Data Science Course

To build these combined skills, I recommend pursuing a Certificate Program In Data Science And Machine Learning. A professionally designed programme can equip ambitious professionals with the tools and techniques needed to excel in this innovative field.

5 attributes of a superior programme

Today, the market is flooded with several free programmes and paid programmes. So, how do you choose the right one for you? According to me, your machine learning certification course should have these five attributes

1. A superior curriculum

Make sure that the programme covers all the contemporary subjects required to excel in data science and machine learning, today. Machine learning with Python, fundamentals, and uses of SQL, data visualisation with tableau, Python programming, and Statistics with data science.

2. A pedigree certification

It is important to have recognised credentials on your resume. For instance, an IIT Roorkee certification course not only offers a superior pedagogy, but the certification is also well-respected in the industry. Another benefit is that you will also have access to world-class faculty from such an institution.

3. Hands-on training

Today when companies hire you they are looking for professionals who can hit the ground running from Day 1. I suggest choosing programs that offer live training via capstone projects. These projects simulate real-world environments, in which learners need to address real challenges, and also work collaboratively with other learners. This is a great opportunity to build real-world skills.

4. Campus immersion

While a good certification program may be hosted completely online, Some also offer a compass immersion engagement. This means that you may be able to spend some time at campuses like IIT Roorkee which celebrate a culture of innovation. You can get to engage with faculty batchmates as well as mentors from the campus.

5. Career services and support

The fifth pillar that completes the circle includes access to comprehensive career-related services. This includes job interview training, resume/profile building, a network of mentorship, and placement support.

In Conclusion

The future is bright for those who invest in skill-building early on. Pursuing a professionally-designed certificate programme in data science and machine learning is an investment in your career. It equips you to develop contemporary in-demand skills that help you leverage technology to address real-world challenges and accelerate career growth.

To know about the Certificate Program In Data Science And Machine Learning, contact us through chat support, or drive to our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Job Assurance in top companies: Can your data science course get you a job?

Posted on by Imarticus Learning

Data science is a rapidly growing field that has seen a huge surge in demand over the past few years. As the demand for data scientists grows, many individuals are asking the question – can my data science course get me a job in one of the top companies?

In this article, we will explore the answer to this question and how a Data Science Course With job assurance can help you secure a placement in one of the top companies.

Data Science Overview

Data science is a field of study that combines computer science, mathematics, and statistics to analyse and interpret large datasets. Data scientists are responsible for collecting, analysing, and interpreting large datasets to answer business questions and develop predictive models. Data science is becoming increasingly important in many industries, including finance, healthcare, retail, and education.

Data Science Job Assurance

The demand for data scientists is growing rapidly in the corporate world as companies are looking to leverage data to gain a competitive edge. Data science professionals have the potential to make a significant impact on a company’s bottom line by uncovering new insights and driving innovation. Top companies such as Google, Apple, Amazon, and Microsoft are actively recruiting PG In Data Analytics for their teams.

Data Science Course with job assurance plays an important role in the placements of top companies, as it helps organisations identify the best candidates for their roles. Data scientists are skilled in using advanced analytics tools, including machine learning and AI, to analyse large datasets and uncover patterns and trends. They also help organisations build predictive models to make better decisions and improve their business operations. Data science is also used to optimise marketing and product strategies and identify new opportunities in the marketplace.

Benefits of Taking a Data Science Course

Here are some of the many benefits of taking a Data Science course:

Learn to Analyze Data

best data analytics course

A data science course will provide students with the necessary skills to analyze data and draw meaningful conclusions. This is essential for any modern business, as data is constantly being generated and needs to be interpreted in order to make informed decisions.

Develop Problem-Solving Skills

A data science course will also help students develop problem-solving skills. Data science requires students to identify a problem, understand it, and find the best solution to it. This is a valuable skill to have in any field.

Understand Big Data

Big data is becoming increasingly important in the business world. A data science course will help students understand how to process, store, and analyze large datasets. This can be a great asset when looking for a job in the current market.

Develop Technical Skills

Finally, a data science course will also help students develop technical skills such as coding, database management, and machine learning. These skills are becoming increasingly important in the current job market, and a data science course can be the perfect way to gain these skills.

Better Placement Opportunity

One of the biggest benefits of a Data Science Course With Placement is the improved placement opportunity it can provide. Data science is a highly sought-after skill, and employers are always looking for candidates that possess the necessary skills and knowledge. By completing a Data Science Course With Placement, you will demonstrate to employers that you are up-to-date with the latest advancements in the field and have the ability to apply them to real-life scenarios.

Finding The Right Data Science Course

There are a few factors that you must have in your mind while finding the right data science course:

A. Researching Available

Courses When looking for the right data science course, the first step is to do some research. There are many online courses available, so it’s important to evaluate each one to determine the best fit for you. Look for courses that cover topics that are relevant to your career goals or that focus on specific programming languages or technologies. Be sure to read reviews and ask for feedback from other students who have taken the course.

B. Cost and Time Commitment

When looking for a data science course, be sure to consider the cost and the time commitment. Many online courses are offered at a discounted rate, but be sure to read the fine print to ensure that you understand what you’re signing up for.

Parting Words

Data science is an exciting and fast-growing field that has become increasingly popular over the past few years. With the right data science course and the right attitude, it is possible to secure a placement in one of the top companies. However, it is important to remember that success does not come overnight and that dedication and hard work are needed in order to achieve success. With the right attitude and determination, you can make the most of your Data Analyst Training and secure placement in one of the top companies.

Ready to take your data science skills to the next level and land the job you’ve always wanted? Sign up for our PG In Data Analytics program today!

Which are the best python courses to learn online?

Posted on by Imarticus Learning

Python is a versatile programming language that has become increasingly important in fields such as modern software development, infrastructure management, data science, and artificial intelligence. It has a reputation for being relatively easy to learn, and its ability to speed up technology has made it a popular choice for many programmers.

This article will explore what python is, why we should learn python, and the best platforms to learn python online.

What is Python?

Python has modules, classes, exceptions, high-level dynamic data types, and dynamic typing. Many Python programmers report large reductions in development time and greater code reliability when switching from other languages.

Python’s simple syntax, code readability, and ability to handle several tasks make it reliable and easy to learn. It accepts various programming paradigms, such as object-oriented, imperative, and functional programming.

Why Should We Learn Python?

best data analytics course

Learning Python can be an excellent investment for our personal and professional development. With the help of python programming, we can simplify our codes and create applications faster than we could ever imagine. Many paid and free online courses are available to learn python programming language.

Python’s broad capacity of libraries and frameworks can help us to complete specific tasks more efficiently. In today’s world, it has become an essential skill in the job market.

Uses of Python and Career Options

Uses of Python

We can use python for a wide range of tasks, including web and software development, scientific computing, data analysis, system scripting, etc. Some uses of Python are mentioned below:

Web development: Django and Flask are the frameworks of Python that make it easy to develop and maintain web applications.

Scientific computing: NumPy and SciPy are Python libraries. These are widely used for scientific computing and data analysis.

Data analysis: Python libraries such as Pandas and Matplotlib make it easy to analyze and visualize data.

Scripting: Python can automate repetitive tasks such as scraping data from websites, running batch jobs, etc.

Game development: Pygame is the library of Python that allows the creation of games.

Career Options Through Python

Python is the perfect entry-level programming language. For many people, it’s their first language, and they become very successful in the industry. Here are some career options in Python  language:

Data Scientist

These professionals use Python for data analysis, visualization, and machine learning to build insights from data and make predictions. The minimum salary package of a data scientist is ₹ 3.8 Lakhs to ₹ 26.0 Lakhs.

Software Developer

These professionals use Python to develop various applications, having web, desktop, and system scripts. Their minimum salary package is about ₹ 2.5 Lakhs to ₹ 13 Lakhs.

Information Security Engineer

These experts use Python to create safety tools and automate network and safety tasks. An Information Security Engineer can expect a minimum salary package of ₹ 3 Lakhs to ₹ 20 Lakhs.

Financial Analyst

They use Python to make economic models and for automating financial procedures. As financial analysts, we can get a minimum salary package of ₹ 2.5 Lakhs to ₹ 11.9 Lakhs.

Game Developer

These professionals use Python to develop several games and script game events. The minimum salary package of a game developer is ₹ 2 Lakhs to ₹ 12 Lakhs.

The salaries of the jobs mentioned above can vary depending on location, company size, and experience. We can expect a competitive salary package with good knowledge and experience.

Python Learning Courses: Free and Paid

Learning a new programming language can be tough. There are course websites and online tutorials for beginners and experienced professionals.

Google’s Python Class

Free online courses in Python are good for basic understanding; however, they do not provide in-depth information, interaction with instructors, personalized feedback, etc. We can face difficulty in finding answers, obtaining official certifications, placements, and more. In contrast, online paid courses such as Imarticus’s python online training provide all these and many additional things.

Paid Courses

When going for online paid python courses to learn python online, it is important to research the course before paying. We also suggest speaking to the institute to get more insight into teaching methods and a detailed syllabus.

Conclusion 

Python courses online can deliver a complete introduction to the language. The combination of video lectures, interactive exercises, and hands-on projects provides a solid understanding of the programming concepts. The demand for Python in companies is beneficial. Therefore, our decision to learn python is beneficial but without the best online python course.

What is the procedure of network design in the supply chain

Posted on by Imarticus Learning

What is supply chain network design?

Supply chain network design is the process of designing a product supply chain with a better understanding of time, costs, resources, and location. Designing a supply chain depends on determining the best location and optimal-sized- facilities. This, in turn, ensures an uninterrupted flow of products using an appropriate mathematical model.

The supply chain network design is important for the success of any business. It depends on various factors, such as

Warehouses,

Suppliers,

Factories,

The flow of product from origin to customer

It is important to ensure that the number of factories or facilities and their location is suitable. It reduces operational costs and increases overall efficiency.

The procedure of network design in a supply chain

1. Evaluate the scope of the supply chain

Creating a scope of the supply chain before designing it is crucial. Once the scope is decided, important deadlines and goals can be scheduled realistically.

2. Collection of supporting data

In the second step, elements of supply chain designs are determined to create a modelling framework. It is important to focus on collecting proper data to support the supply chain network model. The elements of the supply chain include:

Planning

Sourcing raw materials

Manufacturing

Delivering and returns

Warehousing costs

Inventory costs

Transportation rates

3. Analysis and modelling

An analysis is conducted based on the company’s performance. Companies also carry out analyses regarding the demand for products. The company then runs the model to identify any problematic areas. They also find solutions to troubleshoot the same.

4. Creation of alternative models

The next step in designing a supply chain network is creating an alternative model. The alternatives help the companies to ensure supply in case the original model breaks down. It includes:

Adding and removing products or locations

Adding or removing resources

Adding or removing transportation elements

Changing network flow and sources of supply

Changing locations according to the demand of customers

5. Detailed planning and implementation

The last step is creating a final detailed plan and its implementation. The chosen network of the supply chain is analysed further for availability and implementation. During this step, spending and savings are analysed. The planning should be revisited and checked regularly to tweak the model according to any changes in the previous steps. It helps to decrease the chances of interruptions in a supply chain. Remember, the design and analysis in designing a supply chain network is an ongoing process.

Areas of focus while creating a supply chain network design

1. Logistics

To design an efficient system, route planning and optimisation are essential as it supports the systematic and timely transportation of products. It includes planning the proper transportation of raw materials and the final product and determining that the operational process is working properly.

2. Placement of warehouses and distribution points

Warehouses and distribution points are key to an effective supply chain. They ensure that the product reaches customers on time. A good supply chain network design ensures that these are located in appropriate locations. The process focuses on finding an optimum number of facilities to build an efficient supply chain. It is also important that the sizes of warehouses match the requirement.

3. Inventory

Inventory planning and management are major areas while designing a supply chain network. It means that inventory should be stored and transported properly. A proper design should also ensure that the response time is maintained for increased efficiency.

4. Cost and process of the procurement

While designing a network of the supply chain, the materials sourcing and vendor management must be done in terms of

Cost

Time

Location

Identifying the quality and number of manufacturers and suppliers is also important.

Advantages of supply chain design

Supply chain network design is important since it helps to optimise the resources to achieve business goals. It also increases efficiency and overall profitability. It also helps in the following:

Reduction in production costs

Increased efficiency

Improvement in customer satisfaction

Receiving raw materials at competitive prices

Increase in brand loyalty

Balanced cost and revenue to generate profits

Highly optimised operations

Reduction in transportation cost

Reduced transit time and fuel cost

Performance visibility of the supply chain

Why choose Imarticus Learning?

Imarticus Learning offers Executive certification Chief Supply Chain and Operations Officers Online Training. The online+ campus immersion program spans around 10 months. The Supply Chain Management Strategies Course is offered in collaboration with IIM Raipur. The program offers the following benefits:

c-suite training

SCM Globe simulations

Masterclasses by industry leaders

Campus immersion

IIM Raipur alumni status

To know more about how to get a successful Career In Supply Chain And Operations, click here.

Define linear regression and its role in machine learning

Posted on by Imarticus Learning

Statistical analysis has played an important role in data interpretation and analysis for several decades now. However, with the emergence of technological solutions like machine learning, it has become possible for businesses to process a large amount of data and establish a relation between various variables.

Linear regression is one method of statistical analysis typically used as part of machine learning analysis in cases where establishing a definite relationship is important between two or more variables.

Here is all you need to know about Linear Regression and the role it plays in machine learning:

What is Linear Regression in Machine Learning?

Linear regression is a statistical technique used to model the relationship between a variable that is dependent (also known as the output or target variable) and independent variables that can be one or more (also known as predictors, inputs, or features). It is mainly used for various outputs like predictive analysis, and is useful in supervised machine learning. It helps to predict the result of an event based on independent variable points.

The goal is to find the line of best fit (referred to as the “regression line”) that describes the cause and effect between variables. The line of best fit is represented by a linear equation in the form of y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope of the line (representing the effect of x on y), and b is the y-intercept (representing the value of y when x = 0).

What is the role of Linear Regression in Machine Learning?

In machine learning, linear regression is a supervised learning algorithm that can be used for both regression and classification problems. It is simple, easy to implement and understand, and works well when the relationship between the variables is linear.

Linear regression can predict a continuous variable (such as price, temperature, or weight) or classify a binary variable (such as pass/fail or yes/no). It can also be extended to multiple linear regression, where multiple independent variables are used to predict a single dependent variable.

Linear regression is widely used in many fields, including finance, economics, and engineering, to predict future values based on past data and to identify the strength of the relationship between variables.

In machine learning, linear regression is a simple algorithm and is easy to understand and interpret. Linear regression can be used as a base model and also used as a benchmark to compare other more complex models.

What are the various types of Linear Regression?

Linear Regression can be divided into two broad types. The types of Linear Regression are as follows:

Simple Linear Regression

Simple Linear Regression includes a simple straight line with a slope and intercept. A simplified form of simple linear regression can be explained as y = mx + c. In this case, y is the output, and x is the independent variable. When x = 0 c is the intercept. Following this equation, the machine learning model is trained by the algorithm and provides the most accurate output.

Multiple Linear Regression

In cases where the number of independent variables is more than one, the linear equation follows a different form. An example of multiple linear regression is y= c+m1x1+m2x2. In this case, mnxn is the coefficient that is responsible for the impact of different variables. The machine learning algorithm provides the values of coefficients m1, m2, etc., and provides the best-fitting line.

Benefits of Linear Regression

Linear Regression is a popular statistical method. The key benefits of linear regression are as follows:

Ease of implementation

It is one of the most easily implemented machine learning models. Furthermore, it does not require much engineering overhead to complete this.

Scalability

Linear Regression is easily scalable. It can be applied to cases where scaling is required. It is mainly useful in the use of big data.

Interpretability

Linear Regression is easy to interpret and efficient to train. It is simple and requires less time for training.

Applicability in real-time

Linear regression can be used where real-time results are required. It is a system that can be retrained as per the requirements very easily.

IIT Roorkee Certificate Program in Machine Learning and Data Science

The IIT Roorkee Data Science And Machine Learning Course can help us cover Linear Regressions and other statistical methods that are used in machine learning. It is one of the best Certificate Program In Data Science And Machine Learning that has been built by iHUB DivyaSampark @IIT Roorkee and Imarticus Learning. The IIT Roorkee Machine Learning Certification can provide a strong foundation in data science and machine learning. It can also act as a strong bridge to achieve growth in our careers.

Master The Basics Of Hadoop Online 

Posted on by Imarticus Learning

Big data and Hadoop are two of the most searched terms today on the internet. The main reason behind this is that Hadoop is considered the framework of big data. 

If you are interested in learning about Hadoop, then it is important that you have some basic knowledge of big data. In this article, we will discuss big data first and then move to Hadoop and related aspects.

What is Big Data?

Big data comprises huge datasets, which are extremely large in volume and complex to store and process for traditional systems. Big data faces problems in regards to velocity, volume, and variety. 

The volume of data produced every day is simply enormous. Social media contributes to maximum data generation. The time taken for processing data varies from one enterprise to another. With big data, it is possible to have high-speed data computation. Most importantly, data is available in different formats like images, audio, video, text, and XML. With big data, it is possible to carry out analytics on different varieties of data. 

What is Hadoop?

become a Data Analyst

If you are interested in knowing how to become a data analyst or make a data scientist career, it is important that you know Hadoop and big data. Hadoop provides solutions to various big data problems. Hadoop is an emerging technology, with which you will be able to store huge volumes of datasets on a cluster of machines in a distributed manner. 

Hadoop also offers big data analytics through a distributed computing framework. Hadoop is open-source software, which was initially developed as a project by Apache Software Foundation. Since its inception, two versions of Hadoop have been released.

There are different flavors in which Hadoop is available. Some of them are MapR, Cloudera, Hortonworks, and IBM BigInsight. 

Prerequisites for Learning Hadoop

Whether you are looking to make a career as a data scientist or a data analyst, you have to know Hadoop pretty well. However, before learning Hadoop, there are certain things about which you should have a fair idea. They are as follows:

  • Basic Java concepts – Learning Java simultaneously with Hadoop or having prior knowledge in Java proves to be helpful in learning Hadoop. You can reduce functions or write maps in Hadoop by using other languages like Perl, Ruby, C, and Python. This is possible with streaming API. It supports writing to standard output and reading from standard input. There are also high-level abstraction tools in Hadoop like Hive and Pig. For these, there is no need to be familiar with Java.
  • Knowledge of some basic Linux commands – Hadoop is set over Linux operating system. Therefore, knowing some basic Linux commands is definitely an added advantage. These commands are used for downloading and uploading files from HDFS. 

Core Components of Hadoop

There are three core components of Hadoop. We will discuss them here.

  • Hadoop Distributed File System (HDFS) – Hadoop Distributed File System caters to the need for distributed storage for Hadoop. There is a master-slave topology in HFDS. While the high-end machine is the master, the general computers are the slaves.

The big data files are broken into a number of blocks. With Hadoop, these blocks are stored in a distributed manner on the cluster of slave nodes. Metadata is stored on the master machine. 

  • MapReduce – In Hadoop, MapReduce is the data processing layer. Data processing takes place in two phases. They are:
  • Map Phase – In this phase, there is the application of business logic to data. The input data gets transformed into key-value pairs. 
  • Reduce Phase – The output of Map Phase is the input of Reduce Phase. It applies aggregation depending on the important key-value pairs. 
  • YARN – It is the short form of Yet Another Resource Locator. The main components of YARN are resource manager, node manager, and job submitter. 

The main idea of YARN is to split the work of job scheduling and resource management. There is also one global resource manager and application master per application. A single application can either be one job or a DAG of jobs. 

Different Hadoop Flavours

There are different flavors of Hadoop. They are as follows:

  • Hortonworks – This is a popular distribution in the industry
  • Apache – This can be considered the vanilla flavor. The actual code resides in Apache repositories
  • MapR – It has rewritten HDFS and the HDFS is faster when compared to others
  • Cloudera – This is the most popular in the industry
  • IBM BigInsights – Proprietary distribution

Learning the Basics of Hadoop Online

The best way to learn the basics of Hadoop is online. There are many tutorials and e-books available on the web where you will have a fair knowledge of the basics of Hadoop. Many institutes like Imarticus Learning offer dedicated courses in learning big data, Hadoop, and related subjects. On the successful completion of the course, you will get certification from the institute, which will help in your professional career as well. 

Why should Java Developers learn Hadoop online in 2023?

Posted on by Imarticus Learning

Java is a programming language that has flourished so much over the last few years that it has attracted most students to take computer applications over commerce. As the world keeps on evolving digitally, students are considering building their career in data analytics or a career in Java programming.

Many students are choosing to learn Hadoop online to improve their computer application skills and stand out from the crowd. Since Hadoop specialises in handling huge data, it makes the developers capable of taking various challenges to improve the overall software.

Advantages of learning Hadoop online

  • Better job opportunities

IT industry requires individuals who have acquired specialised knowledge in Java and those who are well-versed in Hadoop. For developers to have specialised knowledge of Hadoop, it is essential for them to master Java. 

  • Better salaries

Once you have struggled enough to become a part of a big industry and have switched to big data Hadoop, you can earn a salary of a staggering $150,000 which is more than triple the amount of what an average American earns annually. In addition to this, once you have switched to the big data Hadoop sector, you will be tackled with bigger, harder and more complex projects, which will boost your career as a developer. You will be required to enhance your quality of work, giving you a higher scope to make a difference and prove to the company that you are a valuable resource to them.

  • Increasing demand for Hadoop professionals

In 2021, there were many investors who invested in the big data industry, upscaling the demand for Hadoop developers. Looking at these predictions and the analysis report of MRFR (Market Research Future), the sector is set to show a promising growth rate of approximately 37.30% by the end of 2023. Amongst all this, the BFSI (Banking, Financial Services and Insurance) segment has also predicted that the Hadoop big data analytics sector will ensure the top position all across the global market.

  • Disruptive technology

In all its glory, Hadoop has proven to the entire world that it is better in every domain than its competition. It is better than the traditional systems of warehousing, data analysing, cost management, etc. The entire Hadoop ecosystem goes through continuous changes and innovations, which lets it stay updated with other competitors. 

  • The king of the industry – Big data

data analytics courses

Big data has transformed itself into a domain, and Hadoop is the stepping stone to entering that domain. To have your career boosted in the big data sector and get elite jobs like big data architect and big data analyst, you are advised to learn Hadoop online. Big data has left no stone unturned. It is present in each and every domain, making organisations realise its capability and business values.

The future of Java industries

The big data segment of the IT industry has crossed the $50 billion mark, witnessing exponential growth in the past few years. Java developers have increased chances of a high payroll if they shift to big data jobs. Being a Java developer, you must have heard of big data Hadoop and how learning Hadoop increases the chance of high salaries by 250%. Most Java developers have already switched to the big data Hadoop segment to push their careers consistently, gaining expertise.

When you know the Java programming language, it is easy to acquire big data knowledge from firms which makes it a potential advantage for future Java developers who are planning to step into this field.

Conclusion

As the world keeps advancing towards digitalisation, the role of Java industries and the digital sectors related to it will only flourish. However, making progress is an essential task. Java developers need to upskill and learn Hadoop online courses to get a better career in Java programming. Imarticus offers a postgraduate program in data science and analytics, which helps students learn practical applications in data science and analytics.