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Category: Analytics

5 tips for supply chain management and analytics in the age of AI

Posted on by Imarticus Learning

Undergoing supply chain management training is a prominent goal of several in the management industry. To become a supply chain analyst, one must complete a certification course. There are various certifications for supply chain professionals, available online. While pursuing this career one must understand how the SCM works in this new age of AI. 

Nowadays, AI is an integral part of the competitive market. Businesses are constantly increasing their profit margin using AI. The supply chain market is volatile with the change in several factors and using AI businesses can keep up with the changes and make necessary changes in their system as needed. 

There are several ways in which AI helps in supply chain management (SCM). One of the most prominent methods it adopts is to analyze the available data, both internal and external. Here are some tips for supply chain management in this AI era.  

 

  • Plan for the IoT Data

 

The various data applications in the supply chain make up one-third of the total IoT data. So it needs proper planning to collect, integrate and utilize it. Since the volume of data is ever-increasing, it needs proper tools to manage it effectively and AI comes in as the best option. It can handle data collection of any volume and streamline it properly. 

While doing so, make sure to bring in variety with the data so that it can help with unprecedented methods and ways that detect any anomalies or disruptions in the SCM system. 

 

  • Make use of external data

 

In supply chain management, the volume of internal data itself can be vast. When using AI, one must also think outside the box and bring in outside data such as the local weather, customer reviews from external sources, vendor details, details about the competitor, etc to have a comprehensive database. 

 

  • Increase reactivity faster with AI

 

AI helps achieve a competitive edge in terms of responsiveness to any issues. It can detect problems and create alerts to take necessary preventive steps or find alternatives. 

 

  • Prioritize root cause analysis

 

AI is an effective tool in detecting issues and finding the root cause of the said problem. It can save time by early detection and gives an unbiased analysis of the root cause. 

 

  • Automation in the management system

 

AI can automate the various steps involved in SCM. It can automate administrative jobs, shipment updates, warehouse management, route planning, quality control, and shipping processes. The collective efforts can improve overall customer satisfaction or supplier selection. 

What do you need to study to become a supply chain analyst?

Supply Chain Management is a popular career option and many are eager to become supply chain analysts. But, what do you need to study to become a supply chain analyst? It requires you to get some kind of supply chain management training. 

Supply Chain Management Certification Course

Though a bachelor’s degree seems to be the basic qualification mark, having a master’s degree is an added advantage. To become an analyst one must take certification courses in the form of Professional Certification In Supply Chain Management & Analytics that provide expert guidance and job placement assistance. 

Conclusion

The popular AI-assisted processes in supply chain management are GPS tracking of the shipment for both the company and customer, regular weather updates to help the shipping industry plan their shipments, keeping inventory to help with warehouse management, etc. Depending on AI has helped businesses to reduce their cost, customize their products, and reach more customers with better customer satisfaction. 

Regression and classification metrics with python in AI/ML

Posted on by Imarticus Learning

Python is one of the most popular languages used in data science. It has a massive library that makes it easy for anyone to conduct machine learning and deep learning experiments. In this blog, we will be discussing regression and classification metrics with python Programming in AI/ML.  

We will show how to use some of these metrics to measure the performance of your models, which can help you make decisions about what algorithm or architecture might work best for your application or dataset!

What is a regression metric?

A regression metric measures how accurately a machine learning model predicts future values. To calculate a regression metric, you first need to collect predicted and actual values data. Then, you can use various measures to evaluate how well the model performs. 

How to use classification metrics with python Programming in AI/ML?

A classification metric or accuracy score measures how accurately a machine learning model predicts the correct class label for each data point in your training dataset. Once you have a classification metric, you can evaluate your machine learning model’s performance. 

You can use many different classification metrics to measure performance for a classifier machine learning model. Common ones include accuracy score, precision, recall, actual positive rate, and recall at different false-positive rates. You can also calculate the Matthews correlation coefficient (MCC) to measure how well your model performs.

Accuracy Score:

Accuracy score measures how often the predicted value equals the actual value. It’s also known as error rate, accuracy, or simply classification accuracy. You can calculate the accuracy score by dividing the total number of correct predictions from all predictions made.

Precision:

Precision is the number of correct predictions divided by the number of predictions made. 

Recall:

Recall, or valid positive rate is the number of correct predictions divided by the number of positives. You can calculate how well your model performs for different classes by plotting a ROC curve and calculating the AUC.

False Positive:

False-positive is also known as Type I Error or alpha error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class, but it belongs to another.

False Negative:

False-negative is also known as Type II Error or beta error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class but belongs to another, and the actual value isn’t present in training data. 

Matthews Correlation Coefficient (MCC):

The Matthews correlation coefficient measures how well your model predicts the labels of unseen instances from training data. 

Area Under Curve (AUC):

The AUC score measures how well your model predicts future values by plotting a ROC curve and calculating the area under it.

Discover AIML course with Imarticus Learning

This artificial intelligence course is by industry specialists to help students understand real-world applications from the ground up and construct strong models to deliver relevant business insights and forecasts. 

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies.
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners.
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

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

Python for Data Science: 5 concepts you should remember

Posted on by Imarticus Learning

Python for Data Science: 5 Concepts You Should Remember

The cheat sheet is a helpful complement to your learning since it provides the fundamentals, which are organized into five sections, that any novice needs to know to get started on data analytics courses online with Python. When learning data science, you should also have python training. Here are the main concepts. 

5 concepts in Python for Data Science

  • Variables and data types: Before you begin learning Python, you must first understand variables and data types. That should come as no surprise, given that they form the foundation of all programming languages.

Variables are used by the computer program to name and store a value for subsequent usages, such as reference or modification. You assign a value to a variable to save it. This is known as variable assignment, and it entails setting or resetting the value stored in one or more places identified by a variable name.

    • String instruments: Strings are a fundamental building component of computer languages in general, and Python is no exception. When it comes to dealing with strings, you’ll need to understand a few string operations and procedures.

 

  • Lists: Lists, on the other hand, will appear to be more useful right away. Lists are used to keep track of an ordered collection of elements that may or may not be of distinct sorts. Commas divide the elements into a list, which is encased in square brackets.
  • Tuple: A tuple is an ordered collection of immutable objects. Tuples are lists of sequences. Tuples and lists vary in that tuples cannot be altered, although lists may, and tuples use parentheses while lists use square brackets.

 

  • Dictionaries and Libraries: Python dictionaries allow you to link together disparate pieces of data. In a dictionary, each item of data is kept as a key-value pair. Python returns the value associated with a key when you specify one. All key-value pairs, all keys, and all values may be traversed. When you’ve mastered the fundamentals of Python, though, it’s time to move on to the Python data science libraries. You should look at pandas, NumPy, scikit-learn, and matplotlib, which are the most popular.

Installing Python

If you haven’t already, you should install Python now that you’ve covered some of the fundamentals. Consider installing Anaconda or another Python distribution. It is the most popular open data science platform, and it is based on Python. The most significant benefit of installing Anaconda is that you have immediate access to over 720 packages that can be installed via conda.

However, a dependency and environment manager, as well as Spyder’s integrated development environment, are included (IDE). As if these tools weren’t enough, you also receive the Jupyter Notebook, an interactive data science environment that lets you utilize your favorite data science tools while easily sharing your code and analyses. In a nutshell, everything you’ll need to get started with Python data science!

After you’ve imported the libraries you’ll need for data science, you’ll probably need to import the NumPy array, which is the most significant data structure for scientific computing in Python.

Conclusion

Here at Imarticus, we offer python training and tools to learn data science via our data analytics courses online. Come visit us today and start your career in data science online. 

Related Articles:

Python Coding Tips For Beginners

Python For Beginners – What Is Python And Why Is It Used?

Why is Python of Paramount Importance in Data Analytics?

Python Developer Salary In Terms Of Job Roles

Here’s how to create your first desktop application in python

Posted on by Imarticus Learning

Most young developers have questions about creating desktop software using python. But before going into the process of developing a desktop application, they should learn python programming beforehand to learn concepts related to python.

Step By Step Guide to Create a GUI App in Python

Step 1

In this step, define the current task. Deciding what needs to be solved with the application explains further steps. The field has a variety of usage, for example, Data Visualizations, personal application performance to work with images, text, Business automation GUI’s for managing tasks, and developing systems and monitoring.

best ai and ml coursesPrimary estimation of the functionality and size of the application is necessary as it will help choose the best-suited GUI tool kit.

In case you are not familiar with Graphical User Interface (GUI), it is recommended to take any of the available AI and machine learning courses to clear the fundamentals.  

Step 2   

Choose the correct GUI package and play around with it using python. There are multiple Python-based packages available to do this. One of the easiest ways to do so is by using Tkinter. It allows developers to create small and simple applications using a GUI interface. Popular third-party packages include PyQt, Kivy, WxPython, and Pyside. To know about these, individuals can look at the Python desktop application development tutorial.

Step 3

Here PyQt5 is used as a GUI toolkit for the desktop application. Next, download and install the package.

Step 4 

Then create a pyqt_app1.py file to import PyQt5 modules. After creating PyqtApp class, in the _init_function, in the bottom, create and import instruction with a file name with if _name+ == “_main”: and type lines with calling pyqt based app, importing sys module, calling show () to start the GUI application.

from PyQt5 import QtWidgets, QtGui, QtCore

class PyQtApp(QtWidgets.QWidget):

   

    def __init__(self, parent=None):

        QtWidgets.QWidget.__init__(self, parent)

        self.setWindowTitle(“PyQt Application”)

        self.setWindowIcon(QtGui.QIcon(“Your/image/file.png”))

 

if __name__ == “__main__”:

    import sys

    app = QtWidgets.QApplication(sys.argv)

    myapp = PyQtApp()

    myapp.show()

    sys.exit(app.exec_())

 

Step 5

Then add some style, font, and position of the application. Change the background colour by altering the line – self.element.setStyleSheet(“background-color: #hex number or rgba(). But to position the window, a desktop resolution is required. But this can be done by using multiple codes.

from PyQt5 import QtWidgets, QtGui, QtCore

class PyQtApp(QtWidgets.QWidget):

   

    def __init__(self, parent=None):

        QtWidgets.QWidget.__init__(self, parent)

        self.setWindowTitle(“PyQt Application”)

        self.setWindowIcon(QtGui.QIcon(“Your/image/file.png”))

        self.setMinimumWidth(resolution.width() / 3)

        self.setMinimumHeight(resolution.height() / 1.5)

        self.setStyleSheet(“QWidget {background-color:

                           rgba(0,41,59,255);}

                           QScrollBar:horizontal {

                           width: 1px; height: 1px;

                           background-color: rgba(0,41,59,255);}  

                           QScrollBar:vertical {width: 1px;

                           height: 1px;

                           background-color: rgba(0,41,59,255);}”)

 

if __name__ == “__main__”:

    import sys

    app = QtWidgets.QApplication(sys.argv)

    desktop = QtWidgets.QApplication.desktop()

    resolution = desktop.availableGeometry()

    myapp = PyQtApp()

    myapp.setWindowOpacity(0.95)

    myapp.show()

    myapp.move(resolution.center() – myapp.rect().center())

    sys.exit(app.exec_())

else:

    desktop = QtWidgets.QApplication.desktop()

    resolution = desktop.availableGeometry()

 

Step 6

In this step, adding functionality to the app is necessary. After all, while solving tasks, a graphical interface will make the user comfortable using the application. We can also add frames, fields, buttons and other graphics into the application. Using buttons and text fields will provide good and effective results. For best view buttons, here is how to create a new class for the application with styling and font.

from PyQt5 import QtWidgets, QtGui, QtCore

font_but = QtGui.QFont()

font_but.setFamily(“Segoe UI Symbol”)

font_but.setPointSize(10)

font_but.setWeight(95)

 

class PushBut1(QtWidgets.QPushButton):

   

    def __init__(self, parent=None):

        super(PushBut1, self).__init__(parent)

        self.setMouseTracking(True)

        self.setStyleSheet(“margin: 1px; padding: 7px;

                           background-color: rgba(1,255,0,100);

                           color: rgba(1,140,0,100);

                           border-style: solid;

                           border-radius: 3px; border-width: 0.5px;

                           border-color: rgba(1,140,0,100);”)

   

    def enterEvent(self, event):

        self.setStyleSheet(“margin: 1px; padding: 7px;

                           background- color: rgba(1,140,040,100);

                           color: rgba(1,140,255,100);

                           border-style: solid; border-radius: 3px;

                           border-width: 0.5px;

                           border-color: rgba(1,140,140,100);”)

   

    def leaveEvent(self, event):

        self.setStyleSheet(“margin: 1px; padding: 7px;

                           background-color: rgba(1,255,0,100);

                           color: rgba(1,140,0,100);

                           border-style: solid;

                           border-radius: 3px; border-width: 0.5px;

                           border-color: rgba(1,140,0,100);”)

class PyQtApp(QtWidgets.QWidget):

   

    def __init__(self, parent=None):

        QtWidgets.QWidget.__init__(self, parent)

        self.setWindowTitle(“PyQt Application”)

        self.setWindowIcon(QtGui.QIcon(“Your/image/file.png”))

        self.setMinimumWidth(resolution.width() / 3)

        self.setMinimumHeight(resolution.height() / 1.5)

        self.setStyleSheet(“QWidget

                           {background-color: rgba(1,255,0,100);}

                           QScrollBar:horizontal

                           {width: 1px; height: 1px;

                           background-color: rgba(0,140,0,255);}

                           QScrollBar:vertical

                           {width: 1px; height: 1px;

                           background-color: rgba(0,140,0,255);}”)

        self.textf = QtWidgets.QTextEdit(self)

        self.textf.setPlaceholderText(“Results…”)

        self.textf.setStyleSheet(“margin: 1px; padding: 7px;

                                 background-color:      

                                 rgba(1,255,0,100);

                                 color: rgba(1,140,0,100);

                                 border-style: solid;

                                 border-radius: 3px;

                                 border-width: 0.5px;

                                 border-color: rgba(1,140,0,100);”)

        self.but1 = PushBut1(self)

        self.but1.setText(“”)

        self.but1.setFixedWidth(72)

        self.but1.setFont(font_but)

        self.but2 = PushBut1(self)

        self.but2.setText(“”)

        self.but2.setFixedWidth(72)

        self.but2.setFont(font_but)

        self.but3 = PushBut1(self)

        self.but3.setText(“”)

        self.but3.setFixedWidth(72)

        self.but3.setFont(font_but)

        self.but4 = PushBut1(self)

        self.but4.setText(“”)

        self.but4.setFixedWidth(72)

        self.but4.setFont(font_but)

        self.but5 = PushBut1(self)

        self.but5.setText(“”)

        self.but5.setFixedWidth(72)

        self.but5.setFont(font_but)

        self.but6 = PushBut1(self)

        self.but6.setText(“”)

        self.but6.setFixedWidth(72)

        self.but6.setFont(font_but)

        self.but7 = PushBut1(self)

        self.but7.setText(“”)

        self.but7.setFixedWidth(72)

        self.but7.setFont(font_but)

        self.grid1 = QtWidgets.QGridLayout()

        self.grid1.addWidget(self.textf, 0, 0, 14, 13)

        self.grid1.addWidget(self.but1, 0, 14, 1, 1)

        self.grid1.addWidget(self.but2, 1, 14, 1, 1)

        self.grid1.addWidget(self.but3, 2, 14, 1, 1)

        self.grid1.addWidget(self.but4, 3, 14, 1, 1)

        self.grid1.addWidget(self.but5, 4, 14, 1, 1)

        self.grid1.addWidget(self.but6, 5, 14, 1, 1)

        self.grid1.addWidget(self.but7, 6, 14, 1, 1)

        self.grid1.setContentsMargins(7, 7, 7, 7)

        self.setLayout(self.grid1)

 

if __name__ == “__main__”:

    import sys

    app = QtWidgets.QApplication(sys.argv)

    desktop = QtWidgets.QApplication.desktop()

    resolution = desktop.availableGeometry()

    myapp = PyQtApp()

    myapp.setWindowOpacity(0.95)

    myapp.show()

    myapp.move(resolution.center() – myapp.rect().center())

    sys.exit(app.exec_())

else:

    desktop = QtWidgets.QApplication.desktop()

    resolution = desktop.availableGeometry()

Step 7

You can add a few more fields and explore the possibilities of PyQt.

Step 8

Then connect the buttons and functions for a calling event. For this, we need to add an additional line to the  _init_ function of the main class.

self.but1.clicked.connect(self.on_but1)

Step 9

You can add images in this step. The second button in the image will call the image file from the text to put it in the right bottom corner. 

Adding QLabel: 

self.lb1 = QtWidgets.QLabel(self)

self.lb1.setFixedWidth(72)

self.lb1.setFixedHeight(72)

 

Adding function:

def on_but2(self):

    txt = self.textf.toPlainText()

    try:

        img = QtGui.QPixmap(txt)

        self.lb1.setPixmap(img.scaledToWidth(72,

                           QtCore.Qt.SmoothTransformation))

    except:

        pass

To connect the second button and the function: 

self.but2.clicked.connect(self.on_but2)

Step 10

Complete and run the application. Apart from this PyQt has various other applications, and you can use those to create complete desktop applications.

 If you are not familiar with coding, you can learn python programming or enroll in any AI and machine learning courses. Apart from this, you can also look at the Python desktop application development tutorial. 

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The role of business intelligence and analytics in the supply chain and analytics industry

Posted on by Imarticus Learning

Business Intelligence and Analytics in the Supply Chain and Analytics industry helps convert the vast amount of data into a usable format.

A successful supply chain management system should have sufficient stock with no overstocking or understocking. Business Intelligence will help in predicting the customer demand so that companies can have the right amount of goods ready when necessary. It is then easier to keep track of the shipment and improve the overall customer service. 

As this career grows to be more in-demand, one can become a certified supply chain analyst by completing a supply chain management online course approved by industry-leading companies. Such courses will explain how analytics is used in the supply chain and prepare you for the challenges in this career. 

Need of Business Intelligence in SCM

Supply chain management has a vast amount of operational data that the Business Intelligence (BI) uses to generate trend analysis which improves the efficiency of the SCM system. Such data includes transportation costs, maintenance costs, and trends, repair expenses, etc. Analyzing this data and creating a design that shows the market tendency will be game-changing for business. BI is useful in all stages of the supply chain. 

BI helps improve the internal efficiency of the management system and monitors the company’s progress and growth. It can also utilize the previous data to forecast possible results for the future. 

Since the supply chain system has multiple departments, getting equal visibility can be taxing. BI uses data from all departments to a single, easy-to-access database. Later, it can go through data if each step and process track them properly. 

How is analytics used in the supply chain?

Analytics helps businesses to visualize their strengths, weaknesses, problems, and forecasts. It can help with real-time problem solutions. 

  • The visual representation of data mainly helps track the demand, manage inventory, and monitor the delivery system.
  • Understanding the demand and the trend in the market is crucial for the success of a business. The analysis of collected data helps monitor, manage, predict, and make necessary changes to the business. 
  • Analytics helps organize the inventory according to the change in the market trend and customer demand. 
  • It helps track the movement of goods and makes communication more effective. With real-time monitoring and tracking of the goods increases customer satisfaction. 
  • Analytics can integrate the various systems to bring more profit for the business by boosting productivity and identifying the areas that need improvement. 
  • Tracking and predicting market trends helps avoid overstocking

How to enter the Supply Chain Analytics industry?

The first step towards the supply chain analytics industry is to enroll in a basic or advanced supply chain management online course. These courses are usually short-term, lasting for a few months. They are flexible so that even professionals can register and become a certified supply chain analyst. 

SCM Analysts should have some necessary skills such as mathematical, analytical, and communication skills. An analyst must also be prepared for any kind of challenges that may appear in their career. Courses such as the Professional Certification In Supply Chain Management & Analytics, are certified courses that offer guidance from experts, mentorship during and after the course, as well as placement assistance. 

Conclusion

Visualizing data by BI explores all levels of the supply chain management and makes it into a more understandable form to identify problems immediately so that it doesn’t escalate too much. It also helps optimize the cost, distribution, and delivery systems. The end product is a more responsive SCM system that can be flexible with the necessary changes.  

Here’s how to create your own plagiarism checker with the help of python and machine learning

Posted on by Imarticus Learning

Although plagiarism is not a legal concept, the general idea behind it is rather simple. It is about unethically taking credit for someone else’s work. However, plagiarism is considered dishonest and might lead to a penalty. 

It is possible for coders to build their plagiarism checker in Python with the help of Machine Learning. Thus, it is advisable to undertake a python course to get a comprehensive idea about this programming language. 

Here, you will get an idea of creating your own plagiarism checker. Once finished, individuals can check students’ assessments to compare them with each other.  

Python Is Perfect for AI and Machine Learning
Python Is Perfect for AI and Machine Learning

Pre-requisites

To develop this plagiarism checker, individuals will need knowledge in python and machine learning techniques like cosine similarity and word2vec.

Apart from these, developers must have sci-kit-learn installed on their devices. Hence, if anyone is not comfortable with these concepts, then they can opt for an artificial intelligence and machine learning course. 

Installation    

How to Analyse Text 

It is not unknown that computers only understand binary codes. So, before computation on textual data, converting text to numbers is mandatory. 

Embedding Words  

Word embedding is the process of converting texts into an array of numerical. Here, the in-built feature of sci-kit-learn will come into play. The conversion of textual data into an array of numbers follows algorithms, representing words as a position in space. 

How to recognize the similarities between the two documents? 

Here, the basic concept of dot product can be used to check the similarity between two texts by computing the cosine similarity between two vectors. 

Now, individuals need to use two sample text files to check the model. Make sure to keep these files in the same directory with the extension of .txt.

Here is a look at the project directory – 

Now, here is a look at how to build the plagiarism checker 

  • Firstly, import all necessary modules. 

Firstly, use OS Module for text files, in loading paths, and then use TfidfVectorizer for word embedding and cosine similarity to check plagiarism. 

  • Use List Comprehension for reading files. 

Here, use the idea of list comprehension for loading all path text files of the project directory as shown –

  • Use the Lambda function to compute stability and to vectorize. 

In this case, use two lambda functions, one for converting to array from text and the next one to compute the similarity between two texts. 

  • Now, vectorize textual data. 

Add this below line to vectorize files.

  • Create a function to compute similarity 

Below is the primary function to compute the similarities between two texts.

  • Final code

During compilations of the above concept, an individual will get this below script to detect plagiarism.

  • Output 

After running the above in app.py, the outcome will look as – 

But, before you create this plagiarism checker, you might need to enroll for a python course or an artificial intelligence and machine learning course, as this programming needs concepts from python and machine learning. 

But, if you are willing to take programming as a career, a machine learning certification might be ideal for you. Nevertheless, to create a plagiarism checker of your own, make sure to use the steps mentioned above to detect similarities between the two files. 

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How to build a robust data analytics portfolio

Posted on by Imarticus Learning

Successful data teams require not just outstanding data analysis, but also a strong product and project management foundation. In this article we will tell you what should be included in a data analytics portfolio, and how here at Imarticus you can subscribe to a data analytics course with placement to obtain a data analytics certification. 

big data analytics certification coursesGood vs. great data scientists: create your products with impact

A skilled data scientist has a large learning reservoir. He knows how to design stunning dashboards. To categorize the MNIST dataset, improved NN models are built. He uses extremely complicated business algorithms that would take years to master. This is commendable, yet it is insufficient to achieve results.

A strong product and impact are required to become a successful data scientist. Products represent values that your users will appreciate. It’s a sign that your abilities had an influence on society. Finally, when you walk in for a data science interview, you must show that you can solve issues and provide value to the table.

As a result, outstanding data scientists utilize their dashboards to create prediction formulae that will halt CoronaVirus from spreading to millions of individuals. To safeguard millions of people from being hijacked, a big data scientist uses his NN model to identify phishing assaults. A big data scientist’s portfolio includes audiences, products, and impacts by definition.

What should be included in a data analytics portfolio?

There are numerous approaches to analyzing and diagnosing a client portfolio. Using 4-5 axes of analysis, which might be utilized in this manner to generate a “snapshot” of the client portfolio, is a simple and rapid technique to acquire a “snapshot.”

Customer segments are the first axis. This study will be carried out by classifying customers into categories based on their value, from highest to lowest. Basic data should be available for each client category, allowing their contribution to turnover to be easily understood. The number of clients, as well as the contribution to turnover, are examples of preliminary statistics (turnover, margin, visits, contracts, etc.). Starting with this axis allows us to adjust the offer, resource allocation, marketing, and so on. It also helps us to determine our client’s wallet share in relation to the market.

The second axis is customer status. A customer’s status indicates his or her life cycle. While segmentation gives us a “snapshot of the client portfolio,” this axis reveals how each section has evolved over time. It also helps us to evaluate client acquisition efforts, customer loyalty program performance, and so forth. In general, there are four stages in which a client can be:

  • Registered: when a consumer completes their first transaction with us.
  • Active: once they’ve made their first purchase.
  • Sleeping: after a period of “x” months with no purchase.
  • Low: After a period of “y” months without making a transaction.

The third axis is Customer Acquisition/Acquisition Reasons: Specific campaigns might stimulate a customer’s activation; the key in this axis is to understand why consumers are having their first experience (in the case of a new customer) or why they are “activated.” We will be able to determine the reasons for the growth in the value of the client base using this axis. Customer surveys, ideas, activation efforts, and customer support workers may all provide this sort of data. We may see an example of motives for acquisition-related campaigns in the diagram below.

Axis four is Non-renewal/unsubscription reasons: The reasons for churn or non-renewal allow us to determine the impact of customer non-renewal on turnover (turnover, margin, etc.). With this axis, we can see the reasons for the portfolio’s decline in value. Customer surveys, social media, and customer service workers may all provide this sort of data.

Axis 5 is Level of Recommendation: The level of recommendation allows General Management to “remain” with one number, just one, and if we have an NPS (Net Promoter Score) evaluation of the moments of truth, we will be able to identify which touchpoints have produced memorable experiences.

Conclusion

Here at Imarticus, we can offer you a data analytics course with placement to boost your career and to help you in the first steps of obtaining a data analytics certification.

How to start a supply chain management and analytics career after college

Posted on by Imarticus Learning

An efficient supply chain management system is an asset to any business. It can completely transform a business by eliminating latencies and speeding up the delivery process. It also saves time and money for the customers. 

The key areas of Supply Chain Management are operations, and finances, which requires thorough knowledge. A Supply Chain Management (SCM) Analyst can go through the data to create predictions regarding customer demand in the future, thus creating a career in this field. If you are thinking along the lines of, how do I become an SCM analyst, we have the process explained to you. 

What is Supply Chain Management?

Supply Chain Management is the internal system of business companies that manage the flow of goods. The processes start from the raw material until it reaches the consumers as the final product. The SCM covers the major phases of planning and execution, such as production, development of the product or services, their marketing, the various operations involved in it, distribution of the materials and products, managing the finance of all the processes involved, and customer service. 

The Supply Chain Management system, which is now digital almost everywhere, generates a huge amount of data. The career as a Supply Chain Analyst is in-demand, as SCM requires analysis of performance or the system, identifying problems and finding solutions, and then developing a successful formula or ideas to improve the business.  

How do I become an SCM analyst?

To start a career in supply chain management and analyst career right out of college, one must enroll for a quality certificate course in Supply Chain Management that provides the basic qualification. Some of the careers in SCM analysis include Supply Planning Analyst, Supply and Operations Planner, Logistics Manager, Quality Assurance Manager, etc. 

The Professional Certification In Supply Chain Management & Analytics is a wonderful option as the course is approved by IIT Roorkee with a hands-on process for learning. It enables you to start a career as a Supply Chain Analyst with an attractive salary. It also provides quality mentoring during the course and beyond. 

Apart from qualifying, one must also develop analytical skills, mathematical skills, interpersonal skills, etc to excel in this career. Having 1-2 years of experience with a Master’s Degree can be highly boosting. It is a challenging career but at the same time highly rewarding to stay at the center of a business. Those who love this career often find it exciting. 

Since SCM requires some experience to have a stronger career, enrolling in courses that can provide placement assistance is key. 

Conclusion

If you are a strategic thinker, the dynamic industry of SCM is most suitable for you. It also comes with an attractive salary range which goes higher as you move higher in this career. A career in SCM can be more than the storage and shipping of goods. It is more about data management that helps find quick solutions to the problems. 

SCM requires a broader knowledge in all the fields related to this system. While it mainly concerns goods management, it also involves people management so having key personal skills are important. 

As attractive as the career looks, it can be pressurizing. So it is important to have an experienced mentor to polish those skills and learn how to face the challenges. A good certificate course in Supply Chain Management will have such facilities. Most importantly, a career in SCM will provide multiple options from a beginner to advanced levels in the supply chain system. 

Tips and tricks in AI/ML with python to avoid data leakage

Posted on by Imarticus Learning

Data science has emerged as an essential field of work and study in recent times. Thus, a machine learning course can help interested candidates learn more and land lucrative jobs. However, it is also essential to protect data to ensure proper automation.

Now, beginner courses in machine learning and artificial intelligence only teach students to split data or feed the relevant training data to the classifier. But Imarticus Learning’s AI/ML program helps gain the necessary in-depth knowledge. 

Best Ways to Avoid Data Leakage when Using AI/ML with Python

A Python certification from a reputable institute can help one gain proper insight and learn the tricks of using AI or ML with Python. This will enable interested candidates to know about real-world data processing and help them prevent data leakage.

Following are some tips that advanced courses like an artificial intelligence course by E&ICT Academy, IIT Guwahati will teach students. 

  • No Data Preprocessing Before Train-Test Split

There will be a preprocessing method fitted on the complete dataset at times. But one should not use it before the train-test split. If this method transforms the train or test data, it can cause some problems. This will happen because the information obtained from the train set will move on to the test set after data preprocessing. 

  • Use Transform on Train and Test Sets

It is essential to understand where one can use Transform and where one needs to use fit_transform. While one can use Transform on both the train set and the test set, fit_transform cannot be used for a test set. Therefore, it is wise to choose to Transform for a test set and fit_transform for a train set. 

  • Use Pickle and Joblib Methods

The Python Pickle module serializes and deserializes an object structure. However, the Pickle module may not work if the structure is extensive with several numpy arrays. This is when one needs to use the Joblib method. The Joblib tools help to implement lightweight pipelining and transparent disk-caching. 

Following are a few more tricks that help in automation and accurate data analytics when using AI/ML with Python.

  • Utilize MAE score when working on any categorical data. It will help determine the algorithms’ efficiency as the most efficient one will have the lowest case score. 
  • Utilize available heat maps to understand which features can lead to leakage. 
  • When using a Support Vector Machine (SVM), it is crucial to scale the data and ensure that the kernel cache size is adequate. One can regularise and use shrinking parameters to avoid extended training times. 
  • With K-Means and K-Nearest Neighbour algorithms, one should use a good search engine and base all data points on similarities. The K-value should be chosen through the Elbow method, and it should be relevant. 

Learn AI/ML with Python 

A Python certification will be beneficial for those who wish to pursue a career in data science and analytics. However, it is best to choose a course that will offer advanced training. Imarticus Learning’s Certification in Artificial Intelligence & Machine Learning includes various recent and relevant topics. Apart from using AI/ML with Python, students will also get to work on business projects and use AI Deep Learning methods.

The course curriculum is industry-oriented and developed by IIT Guwahati and the E&ICT Academy. Students can interact with industry leaders, build their skills in AI and Ml through this machine learning course. This course is ideal for understanding the real-world challenges in data science and how AI/ML with Python can help provide solutions. 

The IIT artificial intelligence course from Imarticus Learning helps students become data scientists who excel in their fields of interest. The course offers holistic education in data science through live lectures and real business projects. It is therefore crucial for a rewarding job in the industry. 

5 Reasons Why Supply Chain Snags Affect Global Economic Growth

Posted on by Imarticus Learning

The global economy depends on supply chains that have networks running across continents. This is why any issue with the supply chain can directly affect economic growth. If you learn SCM, you will notice that most of the time, supply chain snags occur due to issues with the management. Now, you can ensure that they have a positive impact on economic growth. To do this, you will have to opt for a supply chain management course. 

How Can Supply Chain Snags Affect the Global Economy?

Since supply chains are vital for most businesses, any management issue can harm economic growth. Those who opt for a supply chain management career need to be aware of the snags that can affect the global economy. 

 

  • Fluctuations in Customer Demand

 

Supply chains often depend on the demands of the customers. But due to various external factors, these demands can fall. When this happens, excess products go to waste. However, at times, customer demands can increase rapidly. If supply chains are not optimized enough to handle such a spike, there will be utter chaos. This can lead to limited products, improper pricing, and issues with the delivery of goods. If global supply chains are unable to meet demands, the economy is bound to suffer. 

 

  • Shortage of Workforce

 

While you can optimize several aspects of the supply chain, a human workforce is necessary. A shortage of workforce means less production or a slow-moving supply chain. If the supply chain gets held up due to inevitable glitches, many might quit due to extended periods of no work. When the supply chain starts working again, there will be an inadequate workforce to handle the pressure. This snag can adversely affect economic growth, particularly when production slows down or the delivery of goods stops. 

 

  • Increase of Freight Rates

 

Since supply chains are closely related to the global economy, snags may increase freight charges. If shipping routes, particularly for global supply chains, are disrupted, the freight rates will increase rapidly. When businesses cannot pay the costs, that particular link in the supply chain will stop functioning.  

 

  • A Slowdown of Industrial Activity

 

A snag in a supply chain can slow down activity in an entire industry. If one part of the supply chain does not move forward, the rest cannot follow. The economy will suffer as vast quantities of raw material go to waste, and the labour force reduces due to certain issues with managing a specific supply chain. 

 

  • Bottlenecks in Manufacturing and Production

 

Supply chain bottlenecks can occur anytime, primarily if the supply chain is not being appropriately managed. If there is a supply bottleneck, then manufacturing will be affected. While this affects the availability of products, it can also affect the earnings of those involved in the production stages. 

To understand how an aspect of the global economy is dependent on supply chains, you need to gain extensive knowledge. Learning supply chain planning can help you build the required skills. 

Supply Chain Management for Sustained Economic Growth

Supply chains, when appropriately managed, can contribute to economic growth and even maintain it. To learn about sustainable supply chains, you can opt for a supply chain management course. Imarticus Learning offers Professional Certification in Supply Chain Management and Analytics. This program is in collaboration with IIT Roorkee, DoMs, and E-Learning Centre.

The institute also has various industry experts collaborating to create a well-rounded curriculum. Imarticus Learning ensures that students can understand supply chain management from a strategic and operational viewpoint. This will help you establish a rewarding supply chain management career. 

The course offered by Imarticus Learning includes supply chain planning and prepares you for the industry through six projects. These are all based on real situations, and you can develop the experience necessary to become a successful supply chain manager. You can also opt for a job in data science, demand planning, or supply and operations planning.