Supply chain management and analytics digitization: Cloud computing and predictive analysis

We are currently living in the era of digitization. From our daily life needs to education to retail to corporate, everything is taking to it pretty fast. Supply chain management too is a sector that has recently started integrating its essential business aspects with technology. The main reason for that is how easy it makes the process of collecting big data and silo breakdown in the supply chain. 

This is why it might be the best time to pursue a course on supply chain management if you are thinking of career prospects for a better future. As always, Imarticus Learnings has come through with its Supply Chain Management online course. It will not only provide you with amazing exposure but also put you, leagues, beyond your peers. 

Here, in this article, we are going to elaborate further on what is digitalization in supply chain management? What is predictive supply chain analytics? and how it can reinvent the whole process for a better outcome. Please go through the whole article to learn more.

How cloud computing and predictive analytics can change the supply change management process?

Service and product life cycles are getting shorter and shorter. This is what basically is pushing businesses towards cloud computing and predictive analytical solutions across industries. Cloud computing provides immediate insights and better responsiveness and predictive analytics helps to predict future issues and any changes that can come into the supply or demand cycle. This essentially answers, what is digitalization in supply chain management and what is predictive supply chain analytics. Now, we take a look at the benefits that come with it.

The benefits of cloud computing

There are obviously some major benefits to this process which as of now are not found anywhere else. The benefits are, as follows:

  • Cloud computing provides immediate insights into all the issues that can affect the chain directly. This, in turn, helps the managers to address the issues quickly and effectively. It also improves collaboration across all the sectors that come into play in the process. 
  • Once all the aspects are tied in the loop, predictive analytics will help the decision-makers with quick insights. With the help of ML and advanced statistical algorithms, it can help process big data to predict a likely outcome for the future. This includes all kinds of variables that have been gathered from different sources. 
  • Cloud computing also enables you to seamlessly add in different parties as needed at a moment’s notice. You can also adjust the provisions in this system immediately to fit the constant changes in demands or market trends. 
  • Digitalization will help to plan and execute things faster. This is supported by in-memory computing and big data analytics. This way you can improve your responsiveness to things that need immediate attention.

Best suited supply chain processes for cloud computing

There are some supply chain processes that are best suited for the cloud format, such as:

  • Sourcing and procurement: SaaS tools are best suited to create a collaborative format. 
  • Logistics: from inventory management, replenishment planning to world trade compliance, every process here can easily be turned cloud-based.
  • Manufacturing: this again can be cloudified very easily.
  • Planning and Forecasting: predictive analytics is evolving to offer better planning and forecasting options.

Conclusion

Supply chain management is evolving constantly. This is why it has opened a new horizon of job prospects. Check out Imarticus Learnings’ Supply Chain Management online course to gear up for it and give your career the spark it needs.

What is Business Analytics All About?

Business Analytics Definition

The importance of Business Analytics stems from the fact that it is the method by which firms analyze historical data using statistical methods and techniques to generate new insights and enhance tactical decision-making.

Since data-driven firms see their data as a business asset and actively seek methods to transform it into a competitive advantage, an increasing number of employees are taking data analytics and machine learning courses and acquiring a business analytics certification.

Data quality, trained analysts who understand the technology and the business, and a dedication to leveraging data to uncover insights that influence business choices are all essential components of business analytics success.

What is Business Analytics?

Business analytics is a data managing solution and a subset of business intelligence that involves analyzing and transforming data into valuable information, identifying and predicting outcomes and trends, and making better, data-driven business choices using methodologies such as data mining, predictive analytics, and statistical analysis.

The key elements of a conventional business analytics dash are as follows:

  • Data Visualization: For easy and rapid data analysis, visual representations such as charts and graphs are offered.
  • Optimization: after identifying patterns and making forecasts, firms may use simulation tools to test best-case scenarios.
  • Predictive Analytics: predicting business analytics uses a number of statistical approaches to building predictive models that extract data from datasets, discover trends, and offer a score for a variety of organizational results.
  • Forecasting: examines historical data from a given time period to make educated predictions about future occurrences or behaviors.
  • Association and Sequence Identification: identifying predictable behaviors that are conducted concurrently or sequentially with other acts
  • Text Mining: examines and organizes huge, unstructured text collections for quantitative and qualitative analysis 
  • Data mining for business analytics: data mining for business analytics sifts through large datasets to uncover patterns and connections using databases, statistics, and machine learning.
  • Data Aggregation: data must be acquired, structured, and filtered before being analyzed, whether through provided transactional records or data.

Why is business analytics important?

Business analytics has a lot of moving pieces, but it’s not always evident why it’s vital to your company. To begin with, business analytics is the instrument that your company requires in order to make informed judgments. These decisions are likely to have an impact throughout your whole organization, assisting you in increasing profitability, market share, and possible shareholder returns.

There’s no doubting that technology has an influence on many enterprises, but when utilized appropriately, BA may have a beneficial impact on your business by giving you a competitive advantage in a variety of ways.

While some firms are unclear what to do with vast volumes of data, business analytics combines data with actionable insights to help you make better business decisions.

Furthermore, because this data may be provided in any manner, your organization’s decision-makers will be well-informed in a way that suits them and the objectives you set at the start of the process.

Conclusion

If you are aware of the importance of Business Analytics and are interested in obtaining a business analytics certification, then you should subscribe to our data analytics and machine learning course given at Imarticus.  

Related Article:

https://imarticus.org/what-are-the-benefits-of-business-analytics/

An introduction to neural networks: AI/ML for beginners

The field of AI and machine learning is overgrowing, with new advancements in algorithms happening nearly every day. One area with a lot of growth recently is neural networks, which are artificially intelligent systems built on an architecture inspired by the human brain. In this post, we will explore what precisely neural networks are and how they work so you can get started today!

What is a neural network?

Neural networks are machine learning algorithms that you can use to recognize objects in pictures or understand human speech. 

For example, imagine you wish to teach a convolutional neural network how to recognize pictures of cats. You might show the computer thousands of examples of what cats look like and let it learn from that data. Then, when somebody shows the computer a picture that isn’t a cat, it could determine whether or not this is an image of something else using its knowledge of cats.

A step-by-step tutorial on how to train the convolutional neural network and make predictions:

 

  • Choose your dataset:

 

The first step is choosing a dataset to train your neural network. It could be a data set of images, text, or anything else you want to predict.

 

  • Preprocess the data:

 

Before starting training your neural network, you need to preprocess the data. It includes cleaning and formatting the data to be ready to be used by the deep neural network.

 

  • Choose your model:

 

The next step is to choose a model for your neural network. There are many different models, so you need to choose one that will work best for your dataset.

 

  • Train the model:

 

Now it’s time to train the network. It is where you will feed in your data and let the neural network learn from it.

The future of AI/ML:

AI/ML is becoming more widely used today. AI/ML has many benefits for the world around us. Machine learning help diagnose diseases, drive cars and even write music!

  • Websites like Amazon use AI/ML to recommend products you may like based on what you have bought in the past.
  • Facebook uses AI/ML to determine which posts or status to show first in your newsfeed.
  • Google uses AI/ML to generate search results.

The possibilities are endless, and the future of AI/ML is inspiring!

Discover Artificial intelligence and machine learning course with Imarticus Learning

This Artificial intelligence and machine learning course is by industry specialists to assist students in learning real-world applications from the ground up and building sophisticated models to offer helpful business insights and forecasts. This AIML course is for recent graduates and early-career professionals (0-5 years) who want to further their careers in Data Science and Analytics, the most in-demand job skill.

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.

Which languages should you learn for data analytics?

Data science is a fascinating topic to work in since it combines high statistical and mathematical abilities with practical programming experience. There are a variety of programming languages in which a prospective data scientist might specialize.

In this article, we will tell you how by learning machine learning and taking a python course you can obtain a Data analytics Certification

big data analytics courseWhile there is no one-size-fits-all solution, there are various factors to consider. Many factors will determine your performance as a data scientist, including:

  • Specificity: When it comes to sophisticated data science, re-inventing the wheel each time can only get you so far. Master the numerous packages and modules available in the language of your choice. The extent to which this is feasible is determined by the domain-specific packages that are initially accessible to you! 
  • Generality: A smart data scientist will be able to program in a variety of languages and will be able to crunch statistics. Much of data science’s day-to-day job is locating and processing raw data, sometimes known as ‘data cleaning.’ No amount of clever machine learning software can assist with this. 
  • Productivity: In the fast-paced world of commercial data science, getting the work done quickly has a lot of appeal. This, however, is what allows technical debt to accumulate, and only rational procedures may help to reduce it.
  • Performance: In some circumstances, especially when working with enormous amounts of mission-critical data, it’s crucial to maximize the performance of your code. Compile-time languages are often substantially quicker than interpreted languages and statically typed languages are far more reliable than dynamically typed languages. The clear trade-off is between efficiency and productivity.

These can be viewed as a pair of axes to some extent (Generality-Specificity, Performance-Productivity). Each of the languages listed below can be found on one of these spectra. 

Let’s look at some of the more popular data science languages with these key ideas in mind. What follows is based on research as well as personal experience from myself, friends, and coworkers – but it is by no means exhaustive! Here they are, roughly in order of popularity:

    • R: R is a sophisticated language that excels in a wide range of statistical and data visualization applications, and it’s open-source, which means it has a vibrant community of contributors. Its current popularity is a reflection of how effective it is at what it accomplishes. 
    • Python: Python is a fantastic language for data research, and not only for beginners. The ETL process is at the heart of most of the data science processes (extraction-transformation-loading). Python’s generality is appropriate for this task. Python is a tremendously interesting language to work with for machine learning, thanks to libraries like Google’s Tensorflow.
    • SQL: SQL is best used as a data processing language rather than as a sophisticated analytical tool. Yet ETL is critical to so much of the data science process, and SQL’s endurance and efficiency demonstrate that it is a valuable language for the current data scientist to grasp. 
    • Java: There are several advantages to studying Java as a primary data science language. Many businesses will value the ability to easily incorporate data science production code into their existing codebase, and Java’s performance and type safety will be significant benefits. However, you won’t have access to the stats-specific packages that other languages provide. That said, it’s worth thinking about, especially if you’re already familiar with R and/or Python.

 

  • Scala: When it comes to working with Big Data using cluster computing, Scala + Spark are wonderful options. Scala’s characteristics will appeal to anybody who has worked with Java or other statically typed languages. However, if your application doesn’t deal with large amounts of data, you’ll likely discover that adopting alternative languages like R or Python will increase your productivity significantly.

 

Conclusion

At Imarticus we commit to giving the best quality education, so if you are interested in getting a data analytics certification, taking a python course, and learning machine learning come and visit us! 

Related Article:

https://imarticus.org/what-are-top-15-data-analyst-interview-questions-and-answers/

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

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

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

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

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

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

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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