Contact Us
×

Category: Management

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. 

What role does hypothesis testing play in statistics

Posted on by Imarticus Learning

Hypothesis testing is a critical component of the scientific method used to verify or reject a claim. In statistics, hypothesis testing concludes data and determines whether the results are significant. The hypothesis testing procedure involves making assumptions, collecting data, and comparing the results to your initial hypothesis.  This post will explore the role of hypothesis testing in statistics and how you can use it to help make informed decisions.

Introduction to Hypothesis Testing in Statistics

In hypothesis testing, we are interested in using data to conclude population parameters. The goal is to choose the correct statistical model and then use it to make inferences about the population. Statistical inference uses data from a sample to make estimates or predictions about a population. 

There are two types of statistical inference: point estimation and hypothesis testing. Point estimation estimates a single value, such as the mean or median, while hypothesis testing tests for a difference between two values, such as the means of two groups. 

In hypothesis testing, we start with null and alternative hypotheses. The null hypothesis claims no difference between the two values, while the alternative hypothesis claims that there is a difference. We then use statistical tests to decide which hypothesis is more likely to be true given the data.  

Null and Alternative Hypotheses

In hypothesis testing, the null hypothesis (H0) represents the status quo or the default assumption that there is no relationship between variables. The null hypothesis states that two groups or data sets are equal or do not differ.

The alternative hypothesis (Ha or H1) represents the claim or theory being tested and is the opposite of the null hypothesis. It states that there is a difference or a relationship between variables. 

For example, an alternative hypothesis might state that the mean of a particular population is not equal to a specific value or that there is a difference in the proportion of individuals with a particular trait between two groups.

 Steps of Hypothesis Testing

The steps of hypothesis testing include the following:

  • Formulate the null and alternative hypotheses: This step involves stating the claim or theory tested in the form of a null hypothesis (H0) and an alternative hypothesis (Ha or H1).
  • Select a sample and collect data: A sample gets selected from the population, and data is collected.
  • Choose a level of significance: The level of significance, or alpha level, is the likelihood that the null hypothesis will be accepted even if it is true. Common values for the level of significance include 0.05 and 0.01.
  • Calculate the test statistic: The test statistic is a value calculated from the sample data used to decide on the null hypothesis. Different types of tests use additional test statistics.
  • Make a decision: The test statistic gets compared to a critical value determined by the significance level. The null hypothesis gets rejected if the test statistic exceeds the critical value. The null hypothesis is not rejected if the test statistic is less than the necessary value or equal to it.
  • Interpret the results and conclude: The final step is to interpret the results and draw a conclusion based on the decision made in step 5. If the null hypothesis is rejected, the conclusion is that there is enough evidence to support the alternative hypothesis. If the null hypothesis is not denied, the decision is that there is not enough evidence to support the alternative hypothesis.

Explore IIT Roorkee data science online course with Imarticus Learning. 

data science career

Want to take your machine-learning skills to the next level? The IIT Roorkee data science and machine learning course is here!

Start your journey with IIT Roorkee’s iHUB Divya Sampark! Our acclaimed faculty members will help you build on the fundamentals while teaching key concepts like mining tools and how to use insights that drive real-world solutions through Python programming. 

Course Benefits For Learners:

  • Learn from acclaimed IIT faculty in this machine learning certification course, and get a unique insight into India’s vibrant industry. 
  • Learn the fundamentals of Artificial Intelligence, Data Science, and Machine Learning to develop skills that impact today and the future.
  • Give yourself a career advantage with our data science online training – where you will gain an understanding of cutting-edge technology that will open up extraordinary opportunities.

These 5 mindsets can improve your supply chain management career.

Posted on by Imarticus Learning

Various industries, especially e-commerce, depend highly on supply chain management. As the world is transitioning from retail to online shopping, career prospects in supply chain management have risen. More millennials are looking forward to building their supply chain management career.

However, it is important to lay strong foundations to prosper in this field. We can go for certifications for supply chain professionals to gain the required knowledge. However, what’s equally important is to have a proper mindset to progress in our careers. What are these mindsets? What is the best certification for supply chain professionals? Let’s find out!

5 Mindsets for Improving Supply Chain Management Career

Following are the 5 mindsets that will help you prosper in your supply chain management career:

Striving for Continuous Improvement

best supply chain management and analytics training

There is always room for improvement, however small it may be. Therefore, we should always strive to improve our supply chain processes more efficiently and effectively. Adopting an open mindset and welcoming the changes is key to survival and growth. A mindset of continuous improvement also helps us become flexible to changes. In a simple sense, flexibility will help us avoid becoming too comfortable with the status quo. Certain circumstances are bound to crop up that will require us to change our plans. We can’t be rigid against changing circumstances.

Ability to Learn on the Job

While we can go for formal education to improve our knowledge about the industry and learn various strategies, we need to adopt the ability to learn on the job. While working, we will face new challenges that demand different strategies and actions. These challenges are also the catalyst for growth and learning.

Becoming a Team Player

There’s a saying, ‘If you want to go fast, go alone. If you want to go far, go together’. That’s where the team comes into the picture. We need to work in teams at any stage to make things happen. Therefore, it is important to become a team player. Enjoying being a part of the team and helping the team learn and improve is one of the biggest traits and most important to lead a team.

Ability to Leverage Technology

We must learn to harness new and evolving technologies. This is crucial if we want a future-proof supply chain management career. Technology is key to ensuring an efficient supply chain while reducing costs simultaneously. We need to adopt a technology-pro mindset and work on how we can use IT and analytics to best our advantage.

Understanding the Dynamics of Supply Chain

If we want to prosper in our supply chain management career, we must understand the dynamics of the supply chain. We need to learn how the supply chain works, its important components, the emerging technologies in this field, etc. The supply chain involves multiple factors. We need to take care of supply chain performance drivers and metrics. Further, one needs to coordinate with the demand and supply of services and products to design the supply chain distribution network.

How to Begin With a Supply Chain Management Career?

Supply chain management is a lucrative field. Before gaining practical experience, we must gain appropriate knowledge about the supply chain management field. We can kickstart our supply chain management career through formal learning or certification for supply chain professionals. This will help us gain an academic understanding of this field and practical experience. This will be of immense value when we face real-life challenges during our work life.

IIT Roorkee curated a Professional Certification in Supply Chain Management, and Analytics offered by Imarticus Learning that helps us build a future-proof career with a new-age supply chain management and analytics curriculum. The curriculum stretches through the following aspects of supply chain management:

Design Supply Chain distribution network

Drivers and metrics

Planning and Coordinating demand and supply

Sales and operations planning

Supply chain performance,

Determining the optimal level of product availability

Managing uncertainties in the supply chain

Along with the academic insights, we also learn from real-life industry projects that show us how the challenges are solved and strategies used by the big players in the industry. This includes the case studies of 7-Eleven as well as Alko.

IIT Roorkee Supply Chain Management Course also provides the opportunity for campus immersion whereby we can participate in a 3-day campus immersion module on the IIT Roorkee campus. We can interact with some smartest minds and learn from IIT faculty during the program. What could present a better opportunity to kickstart our career in supply chain management than an IIT certification for supply chain professionals? Get enrolled now with Imarticus Learning.

Top 15 Data Visualisations in 2023 and what to learn from them

Posted on by Imarticus Learning

Many different types of data visualizations can be used in real-world scenarios. Some common examples include bar graphs, line graphs, pie charts, and scatter plots. Each type of visualization has its strengths and weaknesses, so it is vital to choose the right one for the specific scenario.

For instance, bar graphs are good for comparing data points side-by-side, but they can be challenging to read if there are a lot of data points. Line graphs are good for showing trends over time, but they can be difficult to compare multiple data sets. Pie charts are good for showing proportions, but comparing data sets with different total values can be difficult. Scatter plots are good for showing relationships between two variables, but they can be challenging to interpret if there are a lot of data points. 

Choosing the correct type of visualization is important, but it is also important to ensure it is effective. This means using colors and labels effectively, selecting an appropriate scale, and avoiding cluttered or confusing visuals. With so many things to consider, it can be helpful to look at examples of data visualizations that have been effective in real-world scenarios.

What are the Key Trends in Data Visualization in 2023? 

Data visualization is an incredibly important tool for understanding data, and the trends in data visualization are constantly evolving. Here are some of the key trends in data visualization that we anticipate seeing in 2023:

  1. Augmented reality (AR) and virtual reality (VR) data visualizations will become more common as technology advances.
  2. Visualizations will become more interactive, allowing users to manipulate and explore data in new ways.
  3. Data visualizations will become more accessible, emphasizing mobile-friendly and web-based solutions.
  4. We will see more use of natural language processing (NLP) to generate insights from data visualizations.
  5. Personalized data visualizations will become more prevalent, tailored to the specific needs of individual users.

Key skills and techniques to learn from data visualization trends

To create compelling data visualizations, there are some key skills and techniques that you can learn from studying the trends in this field. Here are some of the most important things to keep in mind:

-Choose the right chart type for your data: A wide range of different chart types are available, and each one is better suited to displaying certain kinds of data. When choosing a chart type, select one that effectively showcases the information you want to communicate.

-Make use of color and other design elements: Colour can be used to significant effect in data visualizations, helping to highlight certain aspects of the data or drawing attention to particular patterns. Other design elements, such as line thickness and labels, can also be used to add further context and clarity to your visualizations.

– Keep it simple: Data visualizations should be easy to interpret at a glance. Avoid using too many colors or charts, and stick to a clear and concise layout.

– Use interactivity wisely: If using interactive elements such as tooltips or filters, ensure they genuinely add value and do not simply clutter up the visualization. Interactive features should help the viewer understand the data more quickly, not confuse things.

The future of data visualization and the input a data Analytic Course can provide

The future of data visualization is incredibly exciting. We are only just beginning to scratch the surface of what is possible with data visualization, and there are many new and innovative ways to visualize data that we have yet to explore. A data analytics course can provide the skills and knowledge you need to create unique data visualizations. You will learn how to communicate complex information using visuals effectively and how to design visualizations that are both informative and visually appealing.

Learn and Grow with Imarticus Learning:

Are you looking for a postgraduate Program in Data Science and Analytics? Here is an excellent opportunity to build your data science and analytics career with a job guarantee program. The course benefits fresh graduates/early career professionals with Tech backgrounds.

Course USPs:

  • Classroom & Live Online Training
  • Learn the real-world application of data science
  • Build analytical models that enhance business outcomes.
  • Job interview guaranteed for recent graduates and professionals. 
  • Gain practical knowledge to work as a data science professional.
  • Students placed at over 500 top-tier partner organizations
  • Job-specific Curriculum for practical learning 
  • Gain experience in Python, SQL, data analytics, power BI, and Tableau
  • One-on-one career counselling to ensure employment.

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

Courses that boost your skills in machine learning with python

Posted on by Imarticus Learning

The need to develop machine learning skills is becoming more and more important. Machine learning is redefining business as we know it and plays an increasingly important role in many different areas of our lives. There’s no better resource than Python to achieve tremendous success with AI.

This post will provide a basic introduction to machine learning and Python concepts. It will also discuss the best course you can take to boost your skills in machine learning with Python.

Introduction to Machine Learning for Data Science

Machine learning is a tool that has revolutionized the field of data science. It is a branch of AI that allows one to learn from data and make predictions without being explicitly programmed. 

Imagine being able to predict the likelihood of a customer purchasing a product or the probability of a patient developing a particular disease. These are a few examples of the endless possibilities that machine learning can offer.

One of the most exciting aspects of machine learning is that it is constantly evolving and improving. With the explosion of big data and technological advancements, machine learning is becoming more accessible and easier to use. It is no longer the domain of only experts. Anyone interested and willing to learn can start using machine learning to analyze their data and make better decisions.

What is Python?

Python is the world’s most popular and versatile programming language. Its simplicity, readability, and flexibility make it an excellent choice for beginners and experts. Whether you’re a data scientist, software engineer, or just someone looking to automate a task, Python has something to offer.

One of Python’s most significant advantages is its library and framework ecosystem. These libraries and frameworks make it easy to perform complex tasks such as data analysis, machine learning, web development, and much more.

In short, Python is a powerful and versatile language that you can use for a wide range of tasks. Its simplicity, readability, and flexibility make it easy to learn and use. In contrast, its vast ecosystem of libraries and frameworks, strong community, and wide range of applications make it an excellent choice for any project. Whether you’re starting or a seasoned professional, Python is a language you should consider.

Benefits of Taking a Course in Machine Learning with Python

There are many benefits to taking a course in machine learning with Python, some of which include the following:

  • Python has a large and active community, meaning plenty of resources are available for learning and troubleshooting.
  • A course in machine learning with Python will teach you how to use popular libraries such as NumPy, Pandas, and TensorFlow. These libraries are used in industry and academia and are practical data analysis and modeling tools.
  • Machine learning is a highly sought-after skill in today’s job market. A machine learning course with Python will give you a competitive edge and open up new career opportunities.
  • Machine learning has many practical applications in various industries, such as healthcare, finance, and e-commerce. By learning machine learning with Python, you can apply your knowledge to real-world problems and make a positive impact.
  • Machine learning is an evolving field, and by taking a course in machine learning with Python, you can stay up-to-date with the latest techniques and algorithms.

In summary, a course in machine learning with Python will provide you with a strong foundation in the field and give you the necessary knowledge and skills to succeed in the industry. 

Explore Certificate Program in Data Science and Machine Learning with Imarticus Learning. 

Data science and machine learning course

Want to take your machine-learning skills to the next level? The IIT Roorkee machine learning certification is here!

Start your journey with IIT Roorkee’s iHUB Divya Sampark! Our acclaimed faculty members will help you build on the fundamentals while teaching key concepts like mining tools and how to use insights that drive real-world solutions through Python programming. 

Course Benefits For Learners:

  • Learn from acclaimed IIT faculty in this machine learning certification course, and get a unique insight into India’s vibrant industry. 
  • Learn the fundamentals of Artificial Intelligence, Data Science, and Machine Learning to develop skills that impact today and the future.
  • Give yourself a career advantage with our data science online training – where you will gain an understanding of cutting-edge technology that will open up extraordinary opportunities.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch with us via chat support.

Building A New Supply Chain With Macro-Ops Planning? Read on

Posted on by Imarticus Learning

After the advent of digitisation and e-commerce, the supply chain has become the backbone of most businesses. We need to have a proper supply chain mechanism in place if we want to sell through online mediums. Building a new supply chain can be a complex and challenging process. But with the right approach and tools, it is possible to create a highly efficient and effective system that meets the needs of our organization. If we focus on how to become a supply chain analyst, then it can do wonders to create an effective supply chain.

One approach that can be particularly useful in this context is macro-ops planning, which involves taking a holistic view of the supply chain and considering all of the factors that can impact its performance. But how to build an effective supply chain using macro-ops planning? How can we learn about macro-ops planning in the first place? Let’s find out!

Steps to Build a New Supply Chain Using Macro-Ops Planning

Developing a macro-ops plan is essential. We can enrol for a supply chain management course with analytics to learn macro-ops planning. Let’s save it for later. Here are some key steps to follow when building a new supply chain with macro-ops planning:

Defining The Goals and Objectives

Before we start building a new supply chain, it is important to have a clear understanding of what we hope to achieve. This might include goals such as reducing costs, improving delivery times, or increasing customer satisfaction. Defining goals sets a clear direction of where we will be heading.

Assessing The Current Supply Chain

To build a new supply chain that meets our needs, it is important to first understand the strengths and weaknesses of our current system. This might involve analyzing data on key metrics such as delivery times, costs, and customer satisfaction, as well as conducting interviews with key stakeholders to gather insights and ideas for improvement. Once we get an idea of our current systems, we know the lacunas that lie in them and craft a definite plan for improvement.

However, if it is the first time that we are developing the supply chain, then we need to have a thorough understanding of our business model. Knowing about how the supply chain functions in our industry can be of great help in creating an effective supply chain for our organisation.

Identify Key Drivers of Performance

Once we have a good understanding of our current supply chain, the next step is to identify the key drivers of performance that need to be considered in our new system. This might include factors such as demand patterns, supplier capabilities, transportation options, and inventory management practices. Changes in these drivers can be most effective in developing the new supply chain.

Develop a Macro-ops Plan

With an understanding of our goals, current systems and the key drivers of performance, we can then begin to develop a macro-ops plan for our new supply chain. This might involve creating a detailed roadmap that outlines the key steps and milestones that need to be achieved, as well as identifying the resources and capabilities that will be required to implement the plan. The macro-ops plan should be carefully curated as it will be the foundation of all our future actions and the effectiveness of our new system. We can learn to develop the macro-ops plan through the IIT Roorkee supply chain management course (discussed later).

Implement and Optimize the New Supply Chain

With our macro-ops plan in place, we can then begin the process of implementing and optimizing our new supply chain. This might involve making changes to our processes, systems, and infrastructure, as well as working closely with suppliers and other stakeholders to ensure that the new system is running smoothly. What we need to remember is that the changes won’t happen overnight. We need to continuously monitor the execution of the macro-ops plan and how the changes are delivering the results.

Developing an Effective Macro-Ops Plan

Adopting a macro-ops planning approach can help us build a new supply chain that is highly efficient and effective, and that meets the needs of our organization. However, it is important to develop an effective macro-ops plan. Further, we also need to have in-depth knowledge of supply chain systems and how they function. But how can we do so?

best supply chain management and analytics training

We can become supply chain analysts if we focus on learning from a supply chain management course with analytics. IIT Roorkee has designed a comprehensive course on supply chain management – Professional Certification in Supply Chain Management & Analytics that provides us with hands-on learning and deep insights into real-world SCM projects. You can access this course through Imarticus Learning. Mentoring with industry experts provides cutting-edge experience to candidates willing to enter the supply chain and operations industry.

The curriculum of the IIT Roorkee supply chain management course is carefully designed to meet the current age supply chain management needs that use the latest technologies and features. The campus immersion allows us to interact with the top industry leaders and some of the smartest minds and peers in this industry. But it’s incomplete without practical insights, right? The real-life industry projects allow us to learn from the practical projects implemented by some of the biggest MNCs across the world.

Are you ready to develop and implement your new supply chain with macro-ops planning? Get enrolled now with Imarticus Learning.

How to find the correct colour palettes for Data Visualisation

Posted on by Imarticus Learning

Color is an important element in data visualisation as it can help to draw attention to specific areas of your data, highlight patterns and make your visualizations more aesthetically pleasing. There are a few things to consider when choosing colors for your data visualizations:

– The purpose of your visualization: What are you trying to communicate with your data? Choose colors that will support this message.

– The type of data you are visualizing: Some colours work better for specific data types (e.g., using warm colours to represent increasing values or cool colours to represent decreasing values).

– The audience of your visualization: Different audiences may react differently to specific colors. Consider who will be looking at your visualization and choose accordingly.

There are many resources available online to help you choose color palettes for your data visualizations, including colour tools like Adobe Color CC and Coolors. Co. Try out a few different options and see what works best for you and your data!

How to choose the right color palette based on the type of data

There are a few key things to consider when choosing a color palette for data visualizations:

-The type of data being represented

-The purpose of the visualization

-The audience of the visualization

For example, if you are visualizing categorical data, you will want to use a color palette that is easy to distinguish between different categories. If you visualize quantitative data, use a color palette to show the magnitude or change over time. And if you are creating a visualization for a specific audience, you will want to consider what colors they are most likely to respond to.

Using Color to Emphasize Trends and Insights and Best Practices for using color in data visualization

Regarding data visualization, color can be a powerful tool for emphasizing specific trends and insights. However, choosing the right colors for your data can be challenging. In this section, we’ll share some best practices for using color in data visualization and tips for finding the right color palette for your project.

When choosing colors for your data viz, it’s essential to keep in mind both the message you’re trying to communicate and the audience you’re communicating it to. Specific colors can convey different meanings, so it’s important to choose colors that will support the message you’re trying to communicate. For example, if you’re visualizing data representing growth or progress, consider using green or blue, as these colors are often associated with positive connotations. On the other hand, if you’re visualizing data representing decline or danger, you should use red or orange, as these colors are often associated with negative connotations.

In addition to considering the message you want to communicate, it’s also important to consider the audience you’re communicating it to. Different audiences will have different reactions to other colors. Choosing colors that will resonate with your specific audience is vital.

Enroll in a Data analytics course with Imarticus Learning:

If learning python for data visualization is your goal, this data analytics course from Imarticus Learning can help you understand data visualization better. During this course, you get the following:

  1. Understanding of data: You get to learn how to work with and analyze data, which is a key component of data visualization. This understanding allows you to choose the right visual representation for your data and create meaningful insights from your data. 
  2. Knowledge of data visualization techniques: The course includes lessons on data visualization techniques, such as bar charts, line charts, scatter plots, and heat maps. This knowledge helps you choose suitable visual representations for your data and effectively communicate your insights. 
  3. Use of tools: You get lessons on using popular data visualization tools like Tableau, Power BI, and D3.js. This hands-on experience helps you become proficient in using these tools and enables you to choose the right tool for your needs. 
  4. Understanding of color theory: A data analytics course may include lessons on color theory, which is critical for choosing the right color palette for your data visualization. This knowledge helps you create visually appealing and effective visual representations of your data. 
  5. Best practices: Finally, the course covers best practices for data visualization, such as how to effectively label your charts, choose the right scales, and handle outliers. This understanding helps you create visually appealing and effective visual representations of your data.

Overall, this course strengthens your foundation for data visualization, allowing you to make meaningful insights from your data.

Course USPs:

Job Interview Guarantee

Live-Learning Module

Dedicated Career Services

Real Worl Projects

KPMG India COE Organised Hackathons

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

Get a free 1:1 Imarticus counselling
Get a free 1:1 Imarticus counselling