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Category: Management

Machine learning with python: How to get started?

Posted on by Imarticus Learning

Machine learning is a process whereby the program will automatically detect a meaningful pattern in the provided data. It is a computer program that comprehends data independently without a programmer. With the passage of time and advancement in technology, the potential to interpret information has improved. Now, machine learning programs can predict more accurate results.

Machine Learning with Python

The machine learning program processes a large volume of data. Therefore, to create this program, a programmer should know Python. But what is the need for analytics to learn Python? Python is the object-oriented program language that uses fewer lines of code. Because of its easy syntax, we can also call it a ‘beginner’s language’.

Python is easily accessible to beginners because of its simplicity and versatility. Coders and analytics can accomplish tasks more quickly with fewer lines of code.

Python Library for Machine Learning   

 At Imarticus, we consider Python the ideal language for learning new concepts. So, the first and foremost step for analytics is to learn Python. The next step is to decide the library you want to use for machine language.

Following is the list of some of the libraries that we, at Imarticus, include in Data analytics and machine learning certification:

1.NumPy

NumPy or Numerical Python is one of the influential Python libraries. It provides a fundamental data structure, and it also has NdArray objects that allow users to create N dimension arrays. These objects are several times more efficient than the in-built Python data structures.

The reason to use NumPy as the foundation for other libraries is that its data structures somewhat cover Python’s speed weakness.

2.SciPy

SciPy is among many other libraries that are on NumPy. It is a very effective tool for sophisticated scientific computation. It introduces advanced algorithms for data handling and visualization by using N-dimensional arrays.

SciPy handles the most complex data manipulation as it is well-documented, supported, and intuitive.

  1. Matplotlib

Matplotloib specializes in data visualization. A well-designed visualization is a crucial component of any machine learning company. After all, training your machine learning algorithms to identify patterns is pointless if you can not interpret the findings.

Generally, every popular Python IDE supports Matplotlib. However, such adaptability comes at the expense of usability; it is not as user-friendly as rival data visualization frameworks.

  1. Theano

Theano creates multi-dimensional arrays and makes advanced mathematical operations possible. It is an excellent tool for machine learning which has its integration with NumPy. In many ways, it is an advanced form of NumPy that makes Python as efficient as C or R.

5.TensorFlow

TensorFlow is a Python-based open-source machine learning library. As Google developed it, almost every Google application that utilizes machine learning uses it. For Google photos or even for Google voice search, we use TensorFlow.

TensorFlow has a fast speed, and it has extensive documentation and support. As it is a Python front-end written in C or C++ is a little difficult to understand.

 

  1. Keras

Keras is a high-level library for datasets. It is widely renowned for being one of the most user-friendly machine learning libraries available because it is written in Python and uses either Theano or TensorFlow as a back-end.

It is the most user-friendly machine learning library, with features for building training datasets and more.

The neural networks API of Keras was designed for rapid experimentation and is an excellent choice for any deep learning project requiring rapid prototyping.

In the driver’s seat: Driving value realization in supply chain analytics courses

Posted on by Imarticus Learning

The supply chain system is a build-up of many aspects put together — from demand to manufacturing to transportation and many more. These aspects together are called the drivers in the supply chain system. As such, their value in the system is irreplaceable. This is why learning about them or realizing their value is essential if you are thinking about a career in SCM.

For that, what is primary is for you to pursue a course on supply chain analytics that will help you cover all the basics. Thankfully, a lot of institutions in India offer a compact supply chain analytics course that will help you get ahead in your career. 

Imarticus Learning, with its years of experience and placement offers, ranks in one of the topmost positions. Their supply chain management course with analytics will not only help you become a supply chain analyst with highly sought-after skills but their placement offers will help you to land a job in your dream sector right from the get-go. Here, we are going to talk about the value realization of the drivers in the supply chain management system and how supply chain analytics courses should focus on them in detail. Please read the whole article to learn more.

Production

One of the major aspects or drivers of the supply chain management process is, of course, production. That includes, what is produced, how much is produced, and also the whole manufacturing process through which it is produced. As such, in the supply chain analytics course, realizing the value of this driver is imperative. 

Inventory

Source materials, products still in the manufacturing process, as well as finished products are referred to as inventory in the supply chain management system. Their value lies in determining the storage places as it directly impacts the responsiveness of the supply chain as well as the retailer’s efficiency. As any change in the inventory greatly affects the supply chain system, their value realization in the supply chain analytics course puts the students way ahead in their careers.

Shipment

Shipment or transportation is yet another core driver of the supply chain management system. The inventory is moved from one place to another through transportation means. As such, its importance is quite irreplaceable in the system. The modes of transportation greatly affect the efficiency as well as the responsiveness of the system. Learning about how to manage a smoother and more efficient mode of transportation in the supply chain analytics course will help the students immensely in their careers later.

Factory Location

Facilities or factories are the places where inventory is stored to assemble them into finished products. As such, deciding their location, capacity, as well as adjustability of the facilities, will impact the supply chain management system massively. Learning to make such decisions quickly and as accurately as possible is one of the most sought-after qualities in a supply chain analyst. Their value realization in the analytics course is a huge way to move forward in your career.

Information

Information, by far, is possibly one of the most important drivers in the supply chain management system. It consists of data regarding inventory, facilities as well as transportation, and clients throughout all the points in the supply chain. As it affects all the other drivers in the supply chain it is absolutely necessary for making the system highly efficient and responsive. As such, learning how to read, store, and analyze said data is one of the skills that make an efficient supply chain analyst. 

Conclusion

Driving value realization is an important part of the supply chain analytics course as it directly impacts how the students will perform in their careers later on. Thankfully, with Imarticus Learning’s IIT supply chain management and analysis course, you can now have all these basics covered and get the boost you need in your career.

7 simple hacks to speed up your data analysis in Python

Posted on by Imarticus Learning

Python is a high-level programming language with a built-in data structure with dynamic typing and data binding. It is general-purpose and straightforward to use the language used to create various computer programs. At Imarticus, we help you learn Python online through the PGA program.

Python creates various programs like developing websites, task automation, software, data analysis, and data visualization. Python is an easy-to-learn language; it is shared between accountants and scientists. It is also used to organize finance and numerous other day-to-day chores.

Python and data science go hand in hand with each other. The data analysts use this language to conduct complex statistical calculations, create data visualization, manipulate and analyze data. Python in web development is pervasive; it includes sending and receiving data processing data while communicating with the database. It also helps in the routing of URLs and ensuring security. 

Python is a dynamic language supporting structured and object-oriented programming. It is the language that focuses on readability, and it is the most accessible language that is why it attracts developers and thereby has a large developer community. 

Python helps in data analysis through the following steps:

  • Python helps to efficiently perform high computational tasks with libraries like Pandas and Numpy.
  • Libraries like beautiful soup and scraps help extract data from the net.
  • Python libraries like Matplotlib and Seaborn help in the analysis of pictographic representation and visualization of data.
  • The Scikit-learn library in Python makes complex mathematical calculations efficient and straightforward.
  • Python library such as OpenCV handles the operations on the image. 

Data Analysis With Tableau

With the PGA course at Imarticus, we help you learn Tableau, data visualization, and data analytics. This course will help you build interactive dashboards and publish them on online Tableau. 

Data analytics is the presentation of data with a blend of colors, dimensions, and labels to create a visualization for providing insight into business and making informed decisions. It is an unavoidable aspect of business analytics as it helps enterprises analyze trends and make decisions quickly and visually. For this visualization and data discovery, you need the Tableau tool.

For business intelligence and data visualization, you need Tableau as it is easy to learn, fast to use, and intuitive for consumer use. 

Data Analysis Using SQL

SQL is the database querying language that helps simultaneously interact with multiple people’s databases. One of the most flexible languages combines a learning curve with a complex depth to help users create tools and dashboards for data analytics.

SQL is famous for quick creation and interaction with the database, and it is also a simple language performing complex data analysis. This language uses many valuable tools such as excel popular python libraries like pandas combined with its internal logic to interact with the data sets. 

So, what are the ways to use SQL for data analytics? SQL uses its base infrastructure and easy-to-use dashboards and reporting tools for communication with complex instructions and fast manipulation of data. One of the other interesting properties of SQL is simple accessibility, strategic organization, and simple, manageable, and understandable interaction. 

Through the PGA program at Imarticus, we help you unfold many uses of SQL in data analytics, such as direct integration into other frameworks, additional functionality, and the ability to communicate effectively. SQL is the tool that acts as an intermediary between the usage and storage of complex data and the end-users, and for using this tool, you need to know Python.

Does Machine Learning Excite You? Check Out Our Data Analytics Course!

Posted on by Imarticus Learning

Machine learning (ML) is truly a blessing to modern computing and technology, possessing the ability to endow systems and machines, the ability to think for themselves and tackle tasks on their own without any supervision of humans. Machine learning is able to do this by creating artificial neural networks which simulate how human brains work. Machine learning is assisted by data science and supports its applications in various fields.

Even though machine learning was initially invested upon with the primary focus on Artificial Intelligence, it was later recognized as a separate field and started being heavily invested upon from the 1990s and is one of the most valuable fields of computing that has one of the highest industry requirements of skilled professionals and freshers holding expertise in various skills and tools which assist in machine learning.

In this article, we will learn more about machine learning and how a well-planned data analytics course can help you progress in your career if you are already in this field or how it can help freshers get exposed to ML. 

What is machine learning?

Machine learning first came into existence due to the interest of having systems and computers learn from data on their own. “Machine learning” was first termed by Arthur Samuel in 1959, who was working in IBM at that time. During his tenure there, he was responsible for various important projects related to computer gaming and AI. It all started when Mr. Samuel took the initiative to teach computers how to play games through the game of Checkers on IBM’s first commercially available computer, the IBM 701.

Eventually, machine learning started being used for various purposes and borrowed many models and approaches from statistics and probability theory. AI uses predictive analytics along with machine learning to execute the various responses or trigger actions. All of this is acquired from the training data set which helps the machine in learning and equips it with the information.

Machine learning is an important branch of computing and data science that creates autonomous systems which learn from data on their own. A machine trained with clean processed data eventually identifies trends and patterns to respond to situations without human supervision.

Machine learning also promotes the automatic improvement and development of algorithms or data models which improve on their own. Machine learning is an important part of Artificial Intelligence which uses data mining, predictive analytics, and various tools to assist machines in learning more extensively with methods like deep learning to allow them to execute functions that emulate the responses of a human, just much more accurate and fast.

Machine learning is also not biased unless specifically asked to do so, hence promoting unbiased AI-supported systems that make fewer errors. Data mining is also a very relevant field and quite valuable to machine learning as it helps systems come to conclusions without having some bits of data or having unknown bits of information. Machine learning is a type of predictive analytics which is backed by data and is exploratory in nature.

Perks of a Data Science Prodegree from Imarticus

The Data Science Prodegree is a great data science course that students and working professionals can choose to gain more exposure and skills in the fields of machine learning, business analytics, and AI.

 

  • Acquire skills and learn how to use required tools and algorithms
  • Gain valuable industry and course certifications
  • Get placement support and opportunities from the best companies
  • Advanced live classroom learning supported by technology and real-life projects

 

Imarticus’s Data Science course with Placement is a great choice if you wish to advance in your career and learn about machine learning, AI, business analytics, or data analysis which will help you become more effective as a data scientist and pursue your dream career in this respectable field.

Robot pizza delivery: The tale of AI and smartest self-driving engineers

Posted on by Imarticus Learning

The demand for AI certification courses is increasing day by day. People are interested in understanding this technology and learning how they can utilize this knowledge for their growth and how they can come up with unique innovations to help make their lives easier. One such innovation is the Robot pizza delivery system!

The tale of self-driven pizza delivery ‘robots’

When you order pizza online, you will see options- takeaway and delivery. Now, people in Houston, Texas have an additional option for their Domino’s Pizza delivery, NURO 2. It is a smart robot that delivers your pizza to your doorstep. Customers can track these self-driven robotic vehicles on GPS. On delivery, they need to enter the PIN to get access to their orders. 

NURO is a startup founded by Dave Ferguson and Jiajun Zhu. This AI-based company added robotics into the picture to produce the spawn of their original project Google’s self-driven cars. The founders were the leads of the projects that included Machine Learning, computer vision, prediction, for the former and the latter handling the perception and stimulators.  

After quitting their jobs with Google, they founded this company and now have thousands of employees and are making hundreds of self-driving robotic vehicles for deliveries. 

What it means is that their core technologies involve AI and Machine learning which helps achieve this feat. It also shows that even a startup by someone having expertise in the technology can achieve much more and faster than the giants in the field. 

Learning AI and ML

These days one can easily find a course to learn AI and ML. But what matters more is what exactly you learn and from where you get the expertise. When you choose a course, it’s better to be from one of the leading institutions such as the IIT. 

You can enroll for the IIT AI ML course, the Certification In Artificial Intelligence & Machine Learning, By E&ICT Academy, IIT Guwahati. It is a 36-week course with a 3-day on-campus immersion at the IIT. The curriculum covers all the latest technologies associated with AI to help you get a strong base of the technology. 

Participants will be getting to work on the ML and Deep learning projects to shape them into experts in this field and prepare for the in-demand job profiles. 

More about the AI ML course

This Artificial Intelligence certification is not for novices but for those with a degree in related subjects such as Computer, engineering, science, maths, economics, or statistics or having at least 2 years of experience in related fields. 

The classes will be online-mode except for the 3 days on the campus. The live training classes will be conducted by experts in the industry and by the professors at the academy. 

There will be a Capstone project towards the end of the course which will be from various industries such as real-estate, security and surveillance, mobile manufacturing, hospitality, airline, marketing, healthcare, advertisement, education, e-commerce, etc. You can choose your project as per your interest. 

Apart from the project the participants will be getting mentorship assistance for motivations, tips, and encouragement for the progress, and will also get long-term assistance and connections, even after the course. 

Conclusion

The certification in AI is available for beginners and experts and one can choose the most suitable one according to the industry and interests. The prospects of using AI in day-to-today lives is increasing exponentially. Those who have creative visions must surely learn more about this technology. Who knows, maybe you can also come up with such innovations as the robot pizza delivery!

Here’s how digital transformation will impact supply chain analytics course training

Posted on by Imarticus Learning

The recent tide of digitization has impacted all corporate sectors heavily. And the supply chain management sector is not left behind. From logistics, and manufacturing to customer satisfaction, digitization has impacted this sector massively. Now the question stands at what changes or impacts digitization has had on the supply chain analytics course training.

Obviously, with a new form of trading system, the training for that is also going to enhance or differ from the traditional system a bit. The best thing you can do here if you are interested in a supply chain analytics career is to pursue a proper supply chain analytics course training. 

A lot of institutes in India offer a stellar supply chain analytics certification course. Imarticus Learning with their incorporated IIT supply chain management course will help you hone your skills to the max. Not only that, but their real industrial exposure and placement offers will help you start your career right off the bat with great opportunities. Here, we are going to talk about how digitization will impact the supply chain analytics course training and what students will be learning in this training. Please go through the whole article to learn more.

Real industrial case files

An approach to teach and learn about the supply chain system with real industrial cases. These include but are not limited to topics related to procurement, warehousing, production, and distribution as well as planning. This segment will elaborate on how digitization can and will help in the betterment of these processes. So that they run smoothly, efficiently and stay up to par with customers’ needs.

Supply chain analytics and more

Digitization of the supply chain analytics will also need the professionals to have a skill set that can provide the companies help with any problems they might be facing in the digital analytics department. For that, a training course will need to focus on supply chain analytics, inventory management as well as machine learning forecasting, and so on.

A managerial approach to cases

Another thing the courses will have to focus on is a managerial approach to real industrial cases. Although a digitization process seems at its heart tech-centric, in the supply chain management sector, what needs to be done is to build management professionals who can maintain the smooth flow of operations through advanced digital means.

Intelligent optimization techniques

With intelligent transportation, logistics network design, transshipment, drones as well as linear programming and sensitivity, students are going to enhance their skills of supply chain optimization techniques in their course training with models. This will ultimately help them tackle real problems of the industry later on in their careers.

The cyber designing system

Students will also need to learn about the basics of cyber designing systems. That is, they will need to learn how to build as well as interpret a linear model to predict trends. With digitization impacting the course, the segment will focus on visualization techniques as well as pre-processing. Data collection and analysis are a crucial part of the model-building process.

Big data analytics

As the market moves forward to more niche problems, big data comes in to solve many of them. At once, the impact, as well as the value of inventory analytics in this process, is irreplaceable. By storing and analyzing this, it provides a lot of insight into the past trends, present conditions as well as future predictions. The training will be focusing on value, tools, and trends as well as the economic indication and financial performance of the inventory management. 

Conclusion

Digitization of the supply chain management is a big step forward to make the system transparent and customer-friendly. For people looking into a career in this sector, check out Imarticus Learning’s supply chain management certification course and give your career the push it needs.

AI Courses: The Future of eSports?

Posted on by Imarticus Learning

Over the last few years, eSports has gained popularity. There are various online games and leagues where players can participate and win. This industry is currently booming but it can experience more gains through the implementation of AI and machine learning. If you are interested in a career that combines AI with eSports, then you can choose an artificial intelligence certification from Imarticus Learning. 

How Can AI Influence the Future of eSports?

Artificial intelligence, in the current times, influences almost every industry. eSports or electronic sports can benefit from AI and machine learning as it is online and already relies on technology. Thus, its presence and outcomes can improve through the correct implementation of artificial intelligence. If you have an AI certification from a reputed institution, you can opt for a career in the eSports sector and use your AI and ML skills to make a difference. 

Take a look at the aspects in which artificial intelligence can affect the future of eSports. 

 

  • Better Sponsorship Deals

 

For eSports leagues to improve viewership and become more popular, an online or social media presence is necessary. With artificial intelligence and machine learning solutions, you can increase the visibility of a particular eSports platform or even a game. This leads to better sponsorship. Sponsors offer deals to leagues or games that already have a following and can provide a platform for advertisements. Therefore, to receive more sponsorships, and become visible to potential investors, eSports companies can use artificial intelligence tools. 

 

  • Improvement in Broadcast

 

Broadcast is only possible if the right audience is present to watch the games. Analysis through artificial intelligence can determine which is the ideal audience. AI can analyse audience responses and predict what kind of games will gain the most views when broadcast. 

 

  • Advanced Training in eSports

 

Artificial intelligence is primarily used for accurate analysis. In eSports, AI solutions can analyse data across platforms and leagues to determine what the condition of the players are and how the audience is responding to certain games. After analysing individual players, the insights can provide important information on how a player can improve their techniques. This ensures advanced training and better outcomes. 

Where Can You Receive the Best Training in AI and Machine Learning?

A degree in artificial intelligence and machine learning can open many doors in multiple industries. To receive holistic education in AI and ML, you can opt for Imarticus Learning’s Certification in Artificial Intelligence & Machine Learning. For this course, we have collaborated with the E&ICT Academy and IIT Guwahati, to provide you with the best hands-on training on AI and machine learning. Our course curriculum has been created by industry experts and renowned academicians who have been in the field for a long time. Therefore, you will get to learn about current business conditions and industry needs. The IIT AI ML course will include more than a hundred assignments and twenty-five industry projects. This course is ideal for students who have a Bachelor’s or Master’s degree in Mathematics, Science, Engineering, Economics, Statistics, or Computer Science. If you have scored at least 50% in your graduation, you can apply for the AI ML course. At Imarticus Learning, we provide the best training and hands-on experience through this online course. 

The certification in artificial intelligence & machine learning will help you learn about the implementation of AI on various industry processes and organisations, including eSports. You will learn through live lectures where our academicians will interact and encourage you to participate in learning sessions. This course is, therefore, ideal for networking and landing a rewarding job. 

Hands-on Python and R in Data Science

Posted on by Imarticus Learning

Hands-on Python and R in Data Science

R and Python are both equally great programming languages. However, each has its own set of advantages that it offers to the user. For example, when we are talking about development processes or IT operations, Python is always a better option. But when it comes to statistical tasks or analytics, R can prove to be a much more suitable alternative.

This is because R has been created for Statisticians and for statistical projects. Skilled programmers can, however, employ any of the two languages to perform any task. For instance, Python can be used for using statistical techniques like Regression Analysis or Bayesian inference on datasets. Similarly, R can also be used for building new Data Science tools and data models.

Python is quite easy to learn with simple syntax while R has various language boundaries. One can easily learn Python online with the help of a course. However, R is far superior in graphical and statistical procedures. R is more scientific in nature and will be much easier for statisticians or professionals who have worked with MATLAB, another scientific programming language.

Python is more focused on running algorithms and makes it easy to build programs in general. In Data Science, however, both are equally important and competent languages. R and Python can work with massive databases and are equally good at Machine Learning projects. 

R in Data Science

R is used in Data Science for graphical and statistical purposes. This language can help users create advanced visualisations and high-quality graphics as well as dynamic graphics. R is also great for data mining and statistical computing in general.

R has a set of functions that allows datasets to be loaded into the memory with program statements, but that is only required if you are going to be building R programs that will keep getting used for various functions. Otherwise, you can simply use the data import function in R Studio (IDE for R). R is able to work with data in two different formats in R studio, CSV and TXT. Once you select the dataset you wish to be working with, R will load the dataset and you can then work on it.

Python in Data Science

Python is a high-level programming level that does not require compilation before running code. This makes it very fast to use for all kinds of projects, including Data Science and Data Analytics. Python is extremely flexible and is a multi-paradigm language, thus, allowing Data Scientists to use different approaches and keep improvising the language with extensions and plugins.

Before loading a dataset in Python, you must first import pandas (library) and install it in Python. After that, a custom function must be created that loads the dataset for you. By creating a load_csv function, you can fundamentally provide an argument for your dataset’s file path. Also, the readlines() is used in order to ensure that the program returns a list containing the lines inside the .csv dataset. You can also additionally present the data in a more visually appealing way by returning the dataset in a dataframe format. This makes it easier to view the data as compared to the native list format or NumPy arrays.

Conclusion

Both the languages also are open-source and have huge communities behind them to keep providing new libraries. There are also a good number of updated tools and powerful IDEs (Integrated Development Environments) available for both R and Python.

If you wish to learn Python or R for Data Science, you should definitely opt for a solid Post Graduate Program in Data Science. You can also choose to take up a Machine Learning course with placement or a Data Analytics course with placement as well. 

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Why Should You Learn Python For Data Analytics and Artificial Intelligence?

Posted on by Imarticus Learning

2022 has seen a huge increase in both the number of data science applicants and also future aspirants all around the world. In India alone, LinkedIn, the global job search leader, announced a 25% spike in data science professionals as compared to 2021, and that is a huge number by a number of standards.

But one of the most common questions that ponders the mind of data science aspirants most often is why they should learn Python to get an edge in this profession?

Thus if you are planning for a career in data analytics and artificial intelligence in the future, and too have this question in your mind, in this article, we will answer exactly that.

Let’s get started.

Why Is Python Important for Data Science?

At the present moment, there are more than 35 different programming languages that are actively used by developers and coders all around the world. But among all of these, Python is undeniably one of the most versatile and well equipped, especially in the field of data science.

The reason behind this is simple; if you take a look at some of the most common tasks that are executed by a data scientist on an everyday basis, starting from data extraction and ELT (Extraction, Loading, and Transformation) all of them require a solid knowledge of Python coding and operation.

Along with this, another important reason why Python is so often relied upon as the go-to programming language for data science professionals is the fact that it comes with a suite of different packages, starting from SciPy, NumPy and pandas, which make complicated and time-consuming tasks easier, more efficient and effortless.

For instance, Matplotlib, which is one of the leading Python packages, is often used by data science professionals when they want to include visualizations or any sort of graphics in their simulations.

If all of this is not enough good reason, as to why you should learn Python

Learn Python ProgrammingTo get an edge for your career in artificial intelligence, here are some of the biggest advantages of the standalone programming language in itself.

Advantages of Python

Easy to Learn

If you are a beginner at coding and you are looking for a programming language that is easy and fast to learn, then Python should be your choice.

The programming language has been around since the late 1980s and has thus undergone several developments and improvements in the last couple of years, which has made the current version extremely user and beginner friendly to learn and execute.

Since data science is such a fast-paced career, where developments are literally happening overnight, the need for a language that is easy and fast to learn is a no-brainer, thus making Python one of the most obvious choices.

Scalable

When you would have spent a considerable amount of time coding, one of the first things you will realize is the fact that Python as a programming language is immensely scalable. Compared to programming languages like Matlab and Stata, which are industry leaders in their own might, Python makes it very simple and straightforward for anyone to scale. The language comes inbuilt with a suite of features that allows you to have multiple approaches and perspectives to solve the same problem, along with having a wide range of flexibility to try out new things as and when they are developed.

Learn Python ProgrammingThe scalability aspect of Python is the second reason why companies prefer their data science professionals to know Python. Scalability is immensely important in both data science and artificial intelligence, mainly because of the fact that new discoveries are being made on the regular.

Conclusion

Thus, if you want to give your career in data science an edge, now is the time to start enrolling for an Artificial Intelligence Training that comes packed with a course in Python.

We offer analytics and artificial intelligence courses at our centers in Mumbai, Thane, Pune, Jaipur, Delhi, Gurgaon and Bangalore.

Supply chain analytics certification fundamentals – Best practices

Posted on by Imarticus Learning

Supply chain analytics is one of the most critical aspects of business in the 21st century. More companies now realize the importance of supply chain analytics and implement programs to improve their efficiency every day. If you want to advance in this field, it’s crucial to understand supply chain analytics fundamentals. This guide will discuss the best practices for supply chain analytics in 2022.

What is supply chain analytics?

Supply chain analytics is the process of gathering, analyzing, and reporting data from across the supply chain to help improve performance. You can use it to make a good decision about allocating resources, identifying areas for improvement, and optimizing processes.

The scope of supply chain analytics varies depending on the organization but typically includes:

  • Forecasting and demand planning
  • Inventory management
  • Capacity planning
  • Transportation and logistics
  • Supplier management

Fundamental practices that will be important in 2022

Supply chain analytics will continue to be one of the most critical aspects of business in the 21st century. Here are some fundamental practices that will be important for companies looking to get ahead:

  • Data collection and analysis: Data is one of the essential aspects of supply chain analytics because it provides insight into how your business operates.
  • Demand forecasting: The ability to forecast demand for products and services will help companies optimize inventory levels and avoid overstocking or understocking items that aren’t selling as well as expected. As we get closer to 2022, more demand forecasting tools will be available for businesses looking to improve their efficiency in this area.
  • Inventory management: Identify the best path forward by looking at what your competitors have done to stay competitive and profitable over time, whether that’s inventory control or pricing strategy.
  • Capacity planning (warehouse space): Warehouses are becoming increasingly important due to the growth of e-commerce. As the market for online purchases grows, it’s essential to have enough warehouse space to accommodate all of your products.
  • Transportation and logistics: It is critical for supply chain analytics; by understanding how different modes of transportation impact delivery times and costs, you can identify opportunities to improve efficiency while reducing overall expenses.
  • Supplier management: It’s essential to have a good understanding of your supplier base. Analyzing their performance will help you understand what needs improvement so that they can continue providing high levels of service (and profits) for years to come.
  • Customer service: Customer service is a vital part of supply chain analytics. It allows companies to analyze data about their customers, which you can then use for marketing or other initiatives to improve delivery times and costs.

Discover Supply Chain Management Course with Imarticus Learning

Hands-on training with top professionals in logistics and supply chain management is available through our partnerships. The Supply Chain Management course with analytics offers an experience beyond just classroom learning – it’s the perfect preparation for those students looking to enter one of today’s most lucrative fields!

Course Benefits For Learners:

  • We give students a thorough understanding of the sector through six real-world projects, which will help them prepare for their rewarding career and become Supply Chain Analyst.
  • This IIT Supply Chain Management online course will prepare students to take on competitive jobs such as Demand Planner or Data Scientist.
  • Supply Chain Management course with Analytics is the key to making data-driven strategic, operational, and tactical decisions.

Contact us through chat support, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

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