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Category: Analytics

Why digitizing supply chain management courses will yield greater efficiency in the new normal?

Posted on by Imarticus Learning

Why digitizing supply chain management courses will yield greater efficiency in the new normal?

Digitizing the supply chain management system has helped the companies reduce their operational cost and have managed to save more time. This drastic change has increased their overall efficiency. This has become the new normal almost everywhere across the world. 

Does this strategy of digitalization benefit the supply chain management courses as well? Are the SCM online courses as efficient as earlier? Let’s find out!

Online Supply Chain Management courses

Digitization has helped automate the various operations across the various levels of a supply chain. It is also only fitting to digitalize the management courses of such operations and business. The various SCM courses available online provide a curriculum that includes the latest technology in this field. 

For example, the supply chain management online course offered here at Imarticus Learning is created in collaboration with the IIT Roorkee. The course curriculum includes the metrics of the supply chain along with areas such as demand and supply, distribution network, sales and operations, optimal product level, etc. 

The concepts of all these areas are provided by experts in the industry through live interactive sessions and practical sessions so that there is no compromise on the quality of education. There will be continued assessments and assignments to keep track of your progress.

 Online or on-campus? 

Being an online course doesn’t mean that it is similar to on-campus courses, In fact, the Professional Certification In Supply Chain Management & Analytics with IIT, Roorkee offers this wonderful opportunity to have an on-campus experience for the students.  

Moreover, the Capstone project at the end of the courses allows you to work at the campus either in Delhi or Noida. It will be ideal to work with professional mentors and understand more about the industry to have a wonderful start in your supply chain management career. 

So it doesn’t make any major difference between the offline or online courses as both of them cover a similar curriculum. The project will be working with real-life issues inspired by leading companies. The result will be that you are ready with all the necessary job skills required to do well in the supply chain industry. 

How do online courses prepare you for SCM?

Online courses for SCM and other related topics are widely available. The biggest advantage of online courses is that they are available with the latest technologies and changes related to what is happening in the industry. 

They can be quickly tailor-made to accommodate all sorts of job profiles depending upon their demand in the market. Such tailor-made courses help the candidates be more suitable to deal with big data, automation, and other technologies. 

What helps is to prepare aspirants for the particular jobs with the right kind of knowledge to help them get a better edge over others in the same pool. Physical courses do not have this advantage as it can take longer for them to come up with the necessary tools. 

Conclusion

Due to the ongoing pandemic, digitization is the new normal in almost all industries. It has become a part of our lives. The availability of SCM online courses has increased the availability and efficiency of the various aspirants and candidates for the different job opportunities in this industry.

From the supply chain management training perspective, it doesn’t matter how or where the candidate was trained. All it matters is whether or not they are qualified. 

What is Supervised learning?

Posted on by Imarticus Learning

Supervised Learning is a machine learning method that makes predictions based on input data. It’s one of the most popular methods for predictive analytics because you can use it to make accurate predictions and analyze trends in the data. This blog post will discuss supervised Learning and how it can help you improve your business!

What do you mean by supervised Learning?

In simple terms, it is a standard machine learning algorithm that uses labeled training data to predict the output. Supervised Learning applies predictive modeling techniques on large datasets/data streams to find patterns and relationships between features, which you can use for building accurate models.

Supervised learning algorithms are a common way to make predictions when there is data on both the input and output sides. The algorithm will learn to map the input variables to the desired output variable by using a training set of example data. You can use supervised learning algorithms in various industries and applications. 

How does it work?

Supervised Learning is an algorithm that can learn from data with answers labeled correctly. The algorithm consists of training data with several input values (x) and the corresponding desired output value (y). It then predicts the output for new inputs.

You can use supervised learning algorithms for a wide range of tasks, such as:

  • Classification: Determining the type of object an image contains, such as a cat or a dog.
  • Regression: Predicting a value, such as the price of a house or the number of calories in food.
  • Clustering: Grouping data into clusters based on similarities.

There are many different supervised learning algorithms, each with strengths and weaknesses. Popular ones include linear regression, logistic regression, support vector machines, and neural networks. Choosing the correct algorithm for your task is essential for achieving good results.

Why should you use supervised Learning to train your models?

Supervised Learning is a machine-learning method that enables us to obtain the parameters of an algorithm from labeled training data. We have a set of input and output pairs with known labels. The goal is to learn from these examples to correctly map new inputs onto their correct outputs when given previously unseen instances.

The most common example of a supervised learning problem is the classification task that labels our data with more classes. In this case, samples typically get drawn from labeled training sets, and each label corresponds to a class (or multiple disjoint classes). The critical point is that tags associated with different inputs must be read-only (immutable).

Discover AIML certification with Imarticus Learning

This Machine learning course will give students a solid grounding in the practical applications of data science by teaching them how to use these skills to solve real-world problems. This program is for graduates and early-career professionals who want to further their careers in Data Science and Analytics, the most in-demand job skill.

Course Benefit For Learner: 

  • Learn machine learning skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • This machine learning course will provide students with a strong understanding of data analytics and machine learning fundamentals and introduce some popular tools used by professionals today. 
  • Impress employers & showcase skills with AIML course recognized by India’s prestigious academic collaborations.

Top 7 career options in data analytics

Posted on by Imarticus Learning

The world of data analytics is constantly growing and changing. With the help of new technologies, we can do more with data than ever before. The data analyst field has seen massive growth in recent years. Data analysts use their skills and knowledge to analyze large data sets and turn them into meaningful information.

Companies or organizations can use it for business purposes such as making decisions on product lines or marketing campaigns or personal reasons like choosing a career path.

The job markets for data analytics are flourishing, and the number of jobs is growing. Data is everywhere, and a career in data analysis has never been more straightforward or promising. 

Data Analytics Careers: The Top Seven Choices

Data analytics is a booming industry, and the job market shows no sign of slowing down. Data Analytics jobs are in high demand across all sectors at every career level, from entry-level to executive management. There are numerous possibilities while choosing your career as a data analyst! 

Here are seven popular choices for entering the world of data analysis:

Data analyst: This is the most common role in data analytics and refers to a professional who extracts insights from data using various techniques, such as statistical analysis and machine learning.

Data engineer: Data engineers are in charge of designing, building, and maintaining the architecture and infrastructure for collecting, processing, and storing data.

Data architect: Data architects work with large quantities of complex data to design high-level structures that inform how they should get stored in a database or file system. This role is especially relevant in big data projects where you need an experienced professional dealing with terabytes of data.

Data scientist: A data scientist is a statistician who analyzes patterns in large sets of complex datasets to extract meaning and information that can be used for decision-making or reporting the findings back to the business stakeholders.

Business analyst: This role involves working with company executives, project managers, marketing teams, and other business professionals to identify and define business problems addressed with data analytics.

Data visualizer: Data visualization is the process of transforming data into graphical representations that are easy to understand, communicate and share. As a data visualizer, you’ll be responsible for designing and creating effective charts, graphs, and other information graphics to help others visualize the data.

Data manager:  Data Manager is responsible for designing and maintaining an enterprise-wide database and overseeing compliance with records management policies.

Learn Data Analytics online with Imarticus Learning

Learn the fundamentals of data science and critical analytics technologies, including Python, R, SAS, Hadoop, and Tableau, as well as nine real-world projects. This data analytics certification course helps students get in-demand future abilities and begins their career as data analysts.

What students draw from this course:

  • Students can participate in fascinating hackathons to solve real-world business challenges in a competitive scenario.
  • Impress employers & showcase skills with data analytics certification courses recognized by India’s prestigious academic collaborations.
  • World-Class Academic Professors to learn from discussions and live online sessions.

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

2022 trends in AI: Learn python from scratch

Posted on by Imarticus Learning

Artificial intelligence (AI) is a tool that allows individuals to reconsider how we combine information, analyze data, and apply the ensuing insights to better decision making—and it is already affecting every aspect of life.

Artificial intelligence (AI) is a technology that is revolutionizing every aspect of existence. It is a versatile tool that allows individuals to reconsider how we combine information, evaluate data, and apply the ensuing insights to make better decisions. Given the human ability for deliberation, judgment, and purpose, AI is widely understood to refer to computers that respond to stimuli in ways that are compatible with conventional human responses.

Algorithms for artificial intelligence are trained to make decisions based on real-time information. They are not the same as passive machines, which could only operate in programmed ways. They integrate information from many sources using sensors, digital data, or remote inputs, instantaneously evaluate the content, and act on the knowledge acquired from those data.

 AI Trends that will rule in 2022:

  1. Hyper-automation will be a primary force in digital transformation, with the goal of streamlining operations to increase efficiency, accuracy, and productivity. As the world continues to embrace digital technologies as drivers of innovation and growth, RPA will be critical in laying the groundwork for accelerating corporate operations in the coming years.
  1. As more of the globe pivots to remote working platforms, 2022 will see AI being employed for precautionary cybersecurity activities. This is largely due to the expansion of workers’ personal laptops and PCs being used to access organizations’ servers that put firms susceptible to cyber-attacks.

    Regretfully, fraudsters are employing AI technology to circumvent traditional IT security mechanisms in order to breach systems containing important commercial and personal data. However, AI also aids in the battle against cyber threats, and we anticipate that more organizations will invest in safeguarding their data through the employment of advanced cybersecurity solutions this year.

  1. AI-powered by quantum computing might eventually be used by modern enterprises to address complicated business challenges faster than classical AI. Quantum AI allows for quicker and more precise data analysis and trend prediction. As a result, it supports diverse firms in detecting unexpected issues and developing appropriate solutions. As a result, quantum AI will transform several industries.
  1. The use of AIOps has become the new norm. They were used to enhance essential processes and duties of an organization’s IT operations through improved analysis of massive quantities and categories of acquired data as complicated IT ecosystems and technologies continue to evolve.

    Organizations gain from more accurate estimates that lead to improved decision-making chances by leveraging Machine Learning’s (ML) capacity to acquire and synthesize massive data volumes. Furthermore, through data correlation, this technology enables cross-team cooperation, opening the path for end-to-end digital experiences through whole corporations.

  2. AI-on-5G integrated computing framework delivers a high-performance and secure connection network for integrating sensors, computing systems, and AI applications.

 Conclusion:

 Python is the most popular web development programming language. Scalability and machine learning are two important characteristics that contribute to the language’s attractiveness. It can handle millions of users and create a powerful web application.

The AI trends that are listed above will be largely dependent on Python. Hence, getting a hang of this programming language might help you kick-start your dream career. The Artificial intelligence course in E&ICT Academy, IIT, will teach you all the necessary skills you need to excel in the field of AI. It will teach you Python from scratch along with other programming languages necessary and give you an AI certification for a bright career in the field of AI.  

10 top strategies to better sustainable supply chain management and analytics

Posted on by Imarticus Learning

The supply chain management training will help you become a professional in this industry in the form of a Supply Planning Analyst or a Logistics Manager. At the same time, they will also give you a clear idea about all the processes and operations pertaining to the daily operations of this industry.

So you will know that sustainability is important for the supply chain industry but the process can be complex. 

Here are the top 10 strategies that can help achieve sustainability. 

  • Commitment in all levels 

Ensure commitment from all the stakeholders from top to bottom such as the partners, employees, third-party entities, customers, etc. Only the complete backing of all parties involved will bring success. 

  • Create a proper plan

Whether it is the business case to start the business or understanding the challenges that are likely to appear, companies must have a clear-cut plan for their progress and expectations. It is also important to communicate those expectations to everyone concerned. 

  • Establish a code of conduct

Supply Chain Management career is one of those areas that give importance to regulations and conduct to win the trust of customers and the public. The core of sustainability will have values as its chief driving force so it is important to ensure that a code of conduct is set. Sometimes a new set is not necessary, one could also make use of the already available regulations. 

  • Accept criticism

Objective criticism is a crucial building block for a sustainable supply chain. Accept them well enough and also seek advice from NGOs or other organizations to understand the initiatives that can achieve your goal in sustainability. 

  • Utilize the metrics

In the Supply chain management course with analytics such as the Professional Certification In Supply Chain Management & Analytics here at Imarticus, you will learn how to use the metrics and data from the various resources. Use them well to fine-tune the operations and performance. 

  • Assess the third-parties

Since accountability is an important asset in the supply chain, assess and measure all the third parties to judge their performance and can choose to replace them if they don’t meet the standard.  

  • Cooperation with other companies

Leverage the relationships with other companies that fit the existing regulations and framework of the industry. 

  • Use capacity-building programs

Giving proper training to the employees will be fruitful and improve their performance. Also include some programs that help them achieve their goals and work smoother. 

  • Performance assessment

Constant performance assessment of the third parties is equally important as that of the employees. A bad decision or a mistake from one level can affect all the related levels as well. 

  • Adopt transparency

The most important factor that affects the success of a supply chain and its ability to achieve sustainability is its transparency. 

How can supply chain management and analytics courses help?

It may seem unrelated but the supply chain is a collaboration of different stakeholders at various levels. People at each level must have a clear understanding of the operations involved here. Those who are on the medium to higher levels may also need to work with data and these SCM certifications courses help them understand the nuances very well. 

The bottom Line

The supply chain is a complex industry that needs the trust and support of its customers. The best way to gain their trust is sustainability. Sustainability shows their commitment towards customers. That will help them gain their social license and maintain it too. 

Top 10 Hacks to speed up your data analysis

Posted on by Imarticus Learning

Data analysis can be a tedious task. Sometimes it feels like there is so much data and not enough time to analyze it all. But some simple tricks will save you a ton of time! In this blog post, we will share 10 top hacks to speed up your data analysis process. You’ll learn to quickly find insights in data without wasting precious hours waiting for programs to run or crunch numbers.

Ten hacks to speed up data analysis

  1. Use hash tables instead of unsorted arrays:
  • An unsorted array is an ordered collection of objects accessible by numerical index, where the index indicates the sequence of its element’s appearance in the variety.
  • A hash table is an associative array, map, lookup table, and dictionary (in programming languages with a limited vocabulary, as Python), a data structure that associates keys to values. 
  1. Store data in row-major order:
  • Use row-major order when storing data, which is faster to load into memory. Row major storage orders memory by rows.
  • Row major storage orders memory by rows instead of ordering memory by columns (called column-major storage).
  1. Group like items in buffers:
  • To speed up processing, store data in the most efficient order. 
  • For example, focus on grouping items in separate buffers instead of creating a different pad for every item.
  1. Store many data sets in memory:
  • If your data sets can fit into the RAM, many data sets into memory by using a hash table to map from keys to their corresponding data sets.
  1. Use persistent objects to pass data between function calls:
  • Endless things are less expensive to construct and maintain than ephemeral objects.
  • For example, instead of passing data from one function call to another, give object references and update the thing as needed.
  1. Use a meta-object system to add behavior to data:
  • A meta-object system is a software framework that provides ways to add behavior to objects.
  • Use a meta-object system to add behavior to data so that you don’t have to write the same code for every data set.
  1. Avoid garbage collection overhead:
  • Avoid using a garbage collector to reclaim unused memory if you can avoid it because the garbage collector has overhead that slows down the program.
  1. Reuse objects instead of allocating new ones:
  • To reuse objects, maintain a cache of things that get frequently used.
  • Enable garbage collection only after the cache has filled up since garbage collection is less expensive if the stock is entire.
  1. Create only the objects you need:
  • Create only the objects you need to reduce memory allocations and garbage collection overhead.
  1. Use language-specific techniques:
  • If possible, use language-specific techniques to avoid memory allocations that you can prevent in languages with control over memory allocation.

Explore and Learn with Imarticus Learning

Industry specialists created this postgraduate program to help you understand real-world Data Science applications from the ground up and construct strong models to deliver relevant business insights and forecasts. This program is for recent graduates who want to further their careers in Data Analytics course online, the most in-demand job skill. With this program’s job assurance guarantee, you may take a considerable step forward in your career. 

Some course USP:

  • These data analytics courses in India to aid the students in learning job-relevant skills.
  • Impress employers & showcase skills with the certification in data analytics endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions.

Contact us through the chat support system or visit Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon, training centers.

2022- The Wake-Up Call for Strong Logistics Management?

Posted on by Imarticus Learning

The global pandemic was a downward spiral for several industries but the logistics industry handled it as a wake-up call. Even though there are several areas where they incurred loss, the overall outcome of the pandemic proved to be positive for it. Increased use of AI-driven technologies has helped this industry map a new strategy for the upcoming years. 

The last two years have been a learning curve for the logistics industry so, in the year 2022, the industry needs to strategize its moves for the upcoming years. With the help of the latest technologies, those who have completed a Logistics and supply chain management course could extend their services to change the overall face of this industry. 

Future of logistics industry in 2022

The COVID-19 pandemic forced the supply chain system to shift from the low-cost management system to an agile framework of data-driven technologies. A major percentage of companies in this industry have already invested in such technologies. It helped the management to make decisions based on the data. The future of this industry will be seen in some key areas. The future trends in these areas are mentioned here. 

  • Better visibility

One of the strong positive outcomes of the pandemic is higher visibility of the supply chain system. The real-time tracking of the consignments and instant feedback from the customers etc will enable the companies to have a re-evaluation of themselves. They can use the data to make appropriate decisions and make their service more impactful. 

  • Optimized network

A logistics company may be provisioning shipments internationally, nationally, and in local areas. They must have a quality optimized network to have customized changes according to the region and the behavior of the customers. 

  • Changing customer expectations

When the supply chain system has improved its functioning the expectations of the customers have also increased many folds. They expect lower costs with faster shipping and a simplified return policy. They are even ready to pay for the same-day delivery so the companies must comply with such demands and make the necessary changes. 

  • Better risk mitigation

Utilizing centralized data to come up with reliable forecasting plays a huge role in preventing losses. The major steps that can prevent loss or risk are automation and optimization of the various processes. It helps minimize the possible errors. This is one of the strong points that you can learn from a supply chain management online course. 

  • Stronger relationship management

A resilient industry requires stronger relationships between the various departments and the personnel involved. A larger company may find it difficult to comply with this but they can share the data among all the departments and bring them all to the same page. This will eliminate the issue of lack of visibility in the lower levels of the chain. 

Conclusion

The best approach for logistics companies is to hire professionals who are trained to achieve these goals. They can understand the changes much better and can also come up with successful strategies that have a positive impact and reduce risks. 

One of the best and most popular choices for the aspirants is to choose the Certification in Supply Chain Management and Analytics course from Imarticus. The course is in collaboration with IIT Roorkee. 

This course will be ideal to prepare you for the job description of a Supply Planning Analyst, Supply And Operations Planner, or Logistics Manager with the right knowledge in the key operational areas. Upskilling with the latest technologies and methods will be essential to understand future trends so that you can make appropriate decisions. 

Here’s why music created by AI is better than you think

Posted on by Imarticus Learning

Artificial Intelligence or AI is capable of carrying out tasks that are much more advanced than just arranging words to generate lyrics. AI already has the ability to offer an immersive listening experience by adapting to a user’s preference. As seen in Spotify and Apple Music for a long time, AI systems understand the user’s preference and recommend songs that the user will enjoy.

AI has gone a step further and now is also able to compose completely personalized music for users. AI can understand certain benchmarks such as harmony, structure, and balance, using which, AI models can generate songs or background music based on the input provided by the user.

Is AI Capable of Creating Better Music Than Humans?

If AI is able to compose music without human supervision, people who need background tracks or copyright-free songs might not need music producers or artists as much as they currently do. Purchasing AI-created music also is easier as there are no royalties while the music generation process would be faster and available on demand.

Yes, with vast amounts of data and training, AI can help in creating a very capable autonomous music generation system, however, it will still be relying on historic data and other pieces of music in order to generate future songs. But, due to the vast amount of data available, the probabilities are limitless and if taught to truly identify good music, AI can become capable of generating hit songs one after another using the very same data.

Even coming up with new songs are just mathematical likelihoods for AI and by analyzing enough combinations, AI is bound to come up with good music. Similarly, meaningful lyrics can also be generated with Natural Language Processing or NLP. However, it will take a while till AI systems become as sensitive to the context of lyrics and innovative in using musical notes.

How AI is Helping in Creating Music?

Even though completely AI-generated music has not reached the Billboards Top 10 yet, services such as AIVA uses AI and Deep Learning models for composing soundtracks and music for users. This helps both small content creators and mainstream celebrities generate music for YouTube, Tik Tok, Twitch or Instagram. This is a cheaper alternative as well. Amper is another great online tool for content creators and non-musicians to make royalty-free music based on their own preferences and parameters. Amper has been created by the music composers who are behind creating the soundtrack for movies such as ‘The Dark Knight’. 

Alex the Kid is a UK-based Grammy-nominated music producer who has used ‘heartbreak’ as a theme and with the help of Machine Learning (ML) and Analytics, has created the hit song ‘Not Easy’. The song even features celebrity music artists such as Wiz Khalifa, Sam Harris, and Elle King.

The hit song had reached the 4th rank in iTunes’ ‘Hot Tracks’ chart within 2 days of its release. Alex used IBM Watson for analyzing billboard songs of the last 5 years as well as cultural and socially relevant content, scripts, or artifacts in order for including references to these elements within the song. Then, the producer used Watson BEAT, the ML-driven music generation algorithm powering the cognitive system for coming up with various musical backgrounds till he found the most suitable combination. 

Conclusion

Artificial intelligence and Machine learning courses can definitely help one learn AI topics for getting involved in interesting projects such as those mentioned above. A Machine Learning and Artificial Intelligence course, such as one offered by Imarticus, are essential for building AI systems such as soundtrack generators or lyrics generators. 

What competitive advantages can be achieved through integration, automation and big data in supply chain management

Posted on by Imarticus Learning

The need for Supply Chain Management (SCM) integration, automation, and big data are increasing rapidly. In order to stay competitive in the marketplace, it’s imperative that organizations have a strong SCM strategy in place. The benefits of integrating these three technologies are enormous – from efficiency gains to cost reductions to improved customer satisfaction.

With a single view across all supply chain activities, teams can make informed decisions about capacity utilization and inventory levels. They can also identify new opportunities for growth while identifying potential risks ahead of time.  

The benefits of integrating the three technologies go beyond SCM

  • Integration reduces complexity and downtime in manufacturing plants by applying automation to key business processes such as Supply Chain planning and scheduling. This enables organizations to achieve higher levels of performance with less human intervention – leading to greater efficiency throughout the entire enterprise. 
  • Automated systems can reduce errors and improve productivity while also freeing up employees’ time for more valuable activities like research and development or customer service initiatives. 
  • Big data offers a treasure trove of opportunities that enterprises are just beginning to tap into today. In addition to providing new insights on sales trends from across the supply chain network, big data analytics lets companies gain critical knowledge about their customers, which they can then use towards improving product development and marketing strategies.

Big Data Analytics, Supply Chain Integration, and Automation are three technologies that can’t be ignored in today’s competitive marketplace. By integrating these technologies together within a single platform or solution to achieve seamless supply chain collaboration across an enterprise, people not only realize numerous benefits of the Supply chain Management course – but they’re able to create new business opportunities that will set them apart from their competitors now and into the future.

Some Other Benefits Listed!

– More efficient utilization of resources (inventory levels) within organizations throughout the value chain network.

– Better understanding customers’ needs which allows for improved product development/marketing strategies as well as better service delivery through channels like social media etc…

– Competitive advantages via increased efficiency along with shorter lead times resulting in greater customer satisfaction.

– Ability to identify new opportunities for growth which help organizations stay ahead of the competition now and into the future.

In addition, integrating automation within supply chain management systems allows companies to realize operational benefits such as:

– Improved throughput/performance levels through reduced error rates due to lowest cost staffing models that provide minimal human intervention across all processes.

– Reduced downtime resulting in continuous operations with increased capacity utilization along with lower fixed costs per unit produced etc…

Finally, big data analytics provides a treasure trove of business insights by giving enterprises access to a wide variety of customer information from throughout their value chains. This data can then be mined for critical knowledge about existing customers and prospects who may not have been considered or targeted before now, which can then be used to improve product development and marketing strategies.

Boost Your Supply Chain Management Career with Imarticus Learning

At Imarticus Learning, supply chain aspirants can make their supply chain management career with the 6-months program that is uniquely designed by IIT faculty and industry leaders. With the ever-increasing trend of e-commerce, the amount of movement of goods has been ever-increasing. There has been a top-heavy jump in the number of jobs for SCM across industries.

Course USPs:

  • Cutting-edge curriculum and certification from e-learning Centre, IIT Roorkee
  • Experiential learning & impressive project portfolio
  • SCM and analytics – a power couple
  • Learn through real-life industry projects.

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

Understanding the basics of data visualization with python

Posted on by Imarticus Learning

Data visualization has become an increasingly important part of the data analysis process in recent years. Many analysts have found that a picture is worth a thousand words, and in this case, it just might be true. You could say that good data visualization can save even more than 1,000 words–it can save lives! Let’s explore some basics of making compelling visualizations with Python.

What is Data Visualization?

Data visualization represents data in a visual form. You can use visualizations to help people understand data more efficiently, ranging from simple graphs to complex infographics. Data visualization is an increasingly popular field with many practical applications. For example, you can use it for business intelligence gathering and analysis or education purposes. Some experts consider data visualization to be a vital part of the expanding field of big data.

Data types and how they get visualized?

There are many types of data, including categorical, univariate, multivariate normal, and so on. Data visualization methods vary depending on the type of data represented. For example, there are several other ways to express categorical data than with graphs.

Univariate data is usually best displayed in a simple bar graph or line graph. Categorical information is often best represented by a pie chart. Multivariate data can be shown in a radar graph or spider chart, while multivariate average data get visualized with a scatter plot.

How to use Python for data visualization?

Python is an easy-to-use programming language that You can use for data visualization. Many libraries, including matplotlib, make it possible to create visualizations without much technical knowledge.

You can even create interactive online visualizations using Python. For example, you can use Python to create visualizations for the Vega-Lite specification, which allows you to create interactive online data visualization. Due to its flexibility and ease of use, it has become one of the most popular languages for data science. It is perfect for working with large amounts of data because it can easily handle large lists or arrays.

Python-based data visualization libraries are beneficial because they typically allow for rapid prototyping of visualizations. It makes them an excellent choice for exploratory data analysis because you can quickly try out different algorithms and processes. The downside is that they can sometimes be challenging to use for more complex projects.

Explore and Learn Python with Imarticus Learning

Industry specialists created this postgraduate program to help the student understand real-world Data Science applications from the ground up and construct strong models to deliver relevant business insights and forecasts. This python tutorial 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.

Some course USP:

This Python for data science course for students is with placement assurance aid the students to learn job-relevant skills.

 

Impress employers & showcase skills with the certification in Python endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions.