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Category: Management

How to get started in Python: An overview of recent trends

Posted on by Imarticus Learning

Are you very interested in programming? Then you need to know the programming language Python. No, it’s not exactly about pythons and snakes, so you can let your puppy loose.

Why Python, specifically? It’s approachable, simple, and adaptable to a range of situations. And because a growing number of programmers all around the world are using and appreciating it.

In fact, according to a recent rating published by IEEE Spectrum (a prestigious engineering and applied science newspaper), Python will be the most used programming language in 2020, followed by JavaScript, C++, C, and Java.

Python’s popularity has been stable in recent years, and this trend is unlikely to reverse. Python tutorials are the most popular on Google, according to the PYPL portal, and everyone wants to learn Python nowadays.  

This explains why Dropbox, Netflix, Facebook, Pinterest, Instagram, and Google all employ Python in their technical growth. Additionally, NASA is included in this list of “tech celebrities” that use Python. Do you see why it’s important for you to be aware of it?

Python is quite popular, and everyone wants to learn more about it. You, too, would not be reading this article if you weren’t.

Projects and programs made in Python

  • Netflix

Netflix, the platform that had a growth of 16 million subscribers during the first quarter of 2020, also uses Python. Its engineers prefer this programming language mainly because of its available libraries.

  • Instagram

Yes, the app you use to share images frequently uses the Python programming language on its backend (what runs on a server). In other words, Instagram is implemented on the open-source web development framework Django which is written entirely in Python.

  • Google

This is one of the big projects that also use the Python programming language, in addition to C++ and Java.

What are the characteristics of Python?

The Python programming language is known for being simple, quick, and having a short and easy learning curve. It is free to use and share because it was created under an open-source license.

But what does “multi-platform”, “multi-paradigm” and “interpreted” mean, here is the explanation:

– Multi-platform: Python can operate on a variety of platforms, including Windows, Mac OS X, Linux, and Unix.

– Multiparadigm: Because it is a programming language that allows a variety of programming paradigms (development models), programmers are not forced to utilize a particular style. Python supports which programming paradigms? Programming styles include object-oriented, imperative, and functional programming.

– Interpreted: Python “interprets” the programmer’s code, which implies it both interprets and executes it.

Python may also be used as an extension language for applications that require a programmable interface since it is dynamically typed (when a variable can take values of multiple kinds or adapts to what we write).

What is Python and what is it for?

Python is a multi-paradigm, multi-platform interpreted programming language used mostly in Big Data, Artificial Intelligence (AI), Data Science, testing frameworks, and web development. Due to its vast library, which has a wide range of features, it qualifies as a high-level general-purpose language.

In 1989, Guido van Rossum, a Dutch programmer, decided to construct an interpreter for a new scripting language he was developing.

His significant expertise in creating the ABC system – an interactive, structured, high-level programming language – aided his efforts to develop a language that was more intuitive, simpler, more powerful. Python, the successor of the ABC language, was born in 1991 (yep, he is a millennial at 29 years old).

Conclusion

At Imarticus we offer a Data Analytics course where you will learn more about how to get started in Python and you will receive more than an overview of recent trends. Visit our website today and enroll in one of our analytics programs. 

Vectors are over, hashes are the future of artificial intelligence

Posted on by Imarticus Learning

AI (artificial intelligence) aims to have computers capable of thinking independently. We are getting closer to achieving that goal, but there are some obstacles in the way. One problem is how computers understand language and communicate with humans. This blog post will discuss how hashes are the future of Artificial Intelligence.

What are vectors and hashes, and how do they differ?

Vectors are a mathematical structure that represents multiple values as a single entity. You can use Vectors in artificial intelligence for matrix multiplication and deep learning tasks. On the other hand, Hashes are a data structure that can store an object’s key-value pairs. You can use hashes in computer science for caching and data mining tasks.

Vectors are better for tasks that require large amounts of data, while hashes are better for jobs that require a small amount of data. For example, vectors are used in deep learning because they can handle a lot of data. Hashes are used in data mining because they can take a small amount of data.

Why are hashes becoming more popular in the world of AI development?

You can use them to teach computers about the environment around them. It’s easy for machines to see what something looks like, but it is much more difficult for them to understand how that object will act in specific scenarios without prior experience. It means hashes can provide a foundation of knowledge that AI systems can understand.

Most importantly, hashes offer a way to understand how the world works without requiring large amounts of data. It is essential because it takes multiple datasets to train neural networks for AI development, and those can be difficult to obtain in some cases.

How can hashes be used to improve the accuracy and efficiency of AI systems?

One way hashes can improve the accuracy and efficiency of AI systems is by reducing the number of dimensions in a vector space. In other words, hashes can help reduce the complexity of data while still preserving its information content. Additionally, you can use hashes as a form of error detection and correction. Incorporating checksums into hash algorithms makes it possible to detect and correct data errors without recomputing the hash.

It can be beneficial for large datasets that are difficult to process in their entirety. Finally, you can use hashes as a form of compression. By representing data as a series of hashes, it is possible to reduce the size of the data while still retaining its information content.

Explore and Learn AI with Imarticus Learning

The Artificial Intelligence certification program collaborates with the E&ICT Academy, IIT Guwahati, and industry professionals to deliver the most satisfactory learning experience for aspiring Artificial Intelligence and Machine Learning students. This curriculum will prepare students for a data scientist, Data Analyst, Machine Learning Engineer, and AI Engineer.

Course Benefits For Learners:

  • This Artificial Intelligence course will help students improve their Artificial Intelligence basic abilities.
  • Students can now take advantage of an Expert Mentorship program to learn about Artificial Intelligence and Machine Learning in a practical setting.
  • This course will assist students in gaining access to attractive professional prospects in the disciplines of Artificial Intelligence and Machine Learning.

Changing career paths: A developer’s journey towards supply chain management courses

Posted on by Imarticus Learning

To meet the demand of a customer for a product or service, there is a chain of procedures involved such as Sourcing, Warehouse, Distribution, and the like. Effective management of these processes is called Supply Chain Management. Supply Chain Management is crucial in most businesses where professionals are required to manage and handle not only the flow of information but most importantly the flow of their products, as per the behavioural changes in consumers.

Just like changing industrial trends and customer behaviour, career paths are subject to change as well. You may find interest in a supply chain as a career even without any qualification or formal training and even if you are from a different industry and a different sphere. As impossible and risky as it may seem, all it takes is having faith in yourself. Here are some skills you will require to excel in Supply Chain Management.

Mandatory Supply Chain Management Skills:

  1. Creative Mindset: You need to come up with unique ways to improve your current role and also help reduce waste. 
  2. Have Computer Skills And Knowledge of Data Management:  You will need good computer skills to handle, understand, analyze, utilize, and manage data. 
  3. High Learning Aptitude: You will need to learn new concepts on the daily basis and use them to improve the process. 
  4. Working in a Team and Leadership Skills:  Supply Chain Management requires good leadership and management skills to not only manage tasks but also improve the efficiency of the team as well. 
  5. Customer Service Skills: Supply chain managers are required to make client rosters. To build such relationships with the suppliers, excellent customer service skills are a must-have. 
  6. Enterprise resource planning (ERP) mastery: Supply Chain Managers use ERP software to execute their job and you can attain these high-level skills through training on the job and tutorials or a certificate course in supply chain management.

Why Take-up SCM Courses?

For a detailed introduction to the world of Supply Chain Management, it is highly recommended that you take up a Supply chain Management course especially if you are planning to change your career field. It will give you a better understanding of how it works and the various complexities related to it in today’s global networked economy. 

A specialization in Supply Chain Management is made up of four courses. They are in operations, logistics, sourcing, planning, and strategy. A Supply Chain Management course will not only help you develop the aforementioned points, but also will cover the technical aspects required to optimize flow, and also the Lean practices and Six Sigma quality. You will gain vast knowledge on various forecasting techniques, and also how to create reliable and productive relationships with suppliers. 

Great Career Opportunities Supply Chain Management (SCM) Courses Provide

A Qualification in Supply Chain Management (SCM) provides a plethora of opportunities and the job roles are as follows. 

  1. Purchase Manager: A Purchase Manager is required to procure goods and services. 
  2. Strategic Planner: The job role of a Strategic Planner involves planning and coming up with a business strategy. 
  3. Materials Analyst: A Material analyst is required to make sure that the flow of goods and services amongst a company’s distribution chains go seamlessly. 
  4. Supply Chain Manager: Supply chain management is the main man in the manufacturing process. 
  5. Warehousing Manager: Warehouse managers are required to manage things like storage, receipt, and dispatch of the goods. 

Conclusion:

A career in Supply Chain Management ought to take you places. A reliable supply chain management training course is what you will need to kick-start your SCM journey to success. Keeping in mind the points mentioned above. 

The fourth Industrial Revolution: a primer on computer vision tutorial

Posted on by Imarticus Learning

The fourth Industrial Revolution is upon us, and it’s bringing a new wave of technological innovations. This post will explore the basics of computer vision, one of the most exciting technologies to come out in recent years.

It’s a branch of artificial intelligence that understands scenes from images or videos. With computer vision, you can quickly identify objects in pictures and recognize what is happening at different locations by looking at them! You can use it for applications such as face recognition, navigation assistance, and many more!

What is computer vision, and what are its applications?

Computer Vision = Artificial Intelligence + Machine Learning

AI is particularly interested in solving problems by building machines capable of intelligent behavior, learning from data, and taking action based on what they’ve learned. And machine learning is a subset of artificial intelligence concerned with the design and development of algorithms that can access data.

Computer vision tutorial is a field in which computer scientists apply their knowledge of imaging, mathematics, physics, engineering, visual perception, and computing to develop methods and algorithms so computers can visually understand scenes. It involves extracting information from a single image or a sequence of images.

From an engineering perspective, you can apply computer vision tutorials to understand and analyze areas such as video surveillance, medical imaging, document management/image retrieval, automatic facial recognition systems for security, etc. You can use it in autonomous vehicles. A fundamental component enables a car to understand its surroundings and make intelligent navigation decisions.

Applications of computer vision:

Automatic Facial Recognition

One of the most common computer vision applications is automatic facial recognition. An image of a person’s face is captured and then used to identify that person from a database of images. You can use this application for security purposes.

Video Surveillance

You can use computer vision to monitor and capture events occurring in video surveillance automatically. You can find this application at airports, casinos/gambling venues, shopping centers, and other places of interest where security and safety are concerned (e.g., amusement parks).

Automotive

Computer vision is used in automotive applications to help the car avoid obstacles and driver assistance systems. Driving a vehicle without using computer vision would be virtually impossible, given that there are just too many visual variables for a person to take into account at any one time. 

Medical Imaging

Computer vision is used in medical imaging to help doctors diagnose and treat patients. You can use computer vision to automatically identify lesions on the skin or tumors inside the body.

Explore and Learn AI Deep Learning with Imarticus Learning

This intensive course will prepare students for a data scientist, Data Analyst, Machine Learning Engineer, and AI Engineer. Students can now utilize our real-world projects from a variety of sectors. This course will assist students in gaining access to attractive professional prospects in the disciplines of Artificial Intelligence and Machine Learning.

Course Benefit For Learner: 

  • Learn machine learning skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • This AIML course will provide students with a strong understanding of data analytics and machine learning fundamentals and introduce some popular tools used by professionals today. 
  • Students can now take advantage of our Expert Mentorship program to learn about Artificial Intelligence and Machine Learning in a practical setting. 

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.

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