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AI’s Fight Against Coronavirus Continues

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AI’s Fight Against Coronavirus Continues

The world has come to a grinding halt with the spread of the deadly novel coronavirus (COVID-19), one of the most contagious diseases to affect us as a people of late.

In little over three months since the virus was first detected in China’s Wuhan late last year, it has spread to more than 90 countries, infected almost a million people, and taken about48,000 lives as of Thursday, April 2, 2020.

Scientists, governments and health organizations are doing the best they can to contain and fight the disease and they are trying to take all the help they can. Including the help of technology – and Artificial Intelligence – in tracking, diagnosing cases, disinfecting areas and speeding up the hunt for a cure.

Here is a short essay on how artificial intelligence is assisting in the fight against COVID-19.“Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak,” says a report.

Tracking the virus outbreak with machine learning

In December last year, a Canada-based artificial intelligence platform that tracks the spread of infectious diseases around the world, detected a cluster of “unusual pneumonia” cases reported in China’s Wuhan. You can read about the detection of the disease here.

The Toronto-based start-up uses Natural Language Processing or NLP and machine learning algorithms to scour tons of information on infectious diseases from sources like statements from health organizations, commercial flights and live stock health reports. It alerted its clients about the outbreak of the disease almost a week before international health organizations declared the outbreak of COVID-19 in China.

Computer Vision being used to detect coronavirus infection

As of today, authorities the world over are checking temperatures of their citizens at airports and railway stations or other crowded places through thermal guns and manually screening them for signs of fever, cough and breathing difficulties.

However, computer vision algorithms can accelerate this crucial screening process by equipping cameras with computer vision technology.

In China, a tech giant has invented a thermal scanner that uses computer vision and infrared sensors to check people’s temperatures in public places. The system invented can check up to 200 people per minute.

Another tech giant has invented a system that uses AI to detect coronavirus in chest CT scans (in 20 seconds as opposed to the 15 minutes spent by human health workers) with a 96 per cent accuracy rate. The system is reportedly being adopted by 100 hospitals in China.

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AI is speeding up drug research

Developing new drugs and vaccines, in the race to win the fight against coronavirus, is proving long and tedious. Some reports suggest it can take anywhere near 12 years at a cost of billions of dollars.

However, that said, AI will help speed up the process of drug discovery to some extent. “DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19”, a process that could have taken many more months.

“Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.”

To know more about how AI is helping in the fight against the novel coronavirus, do read DexLab Analytics’ previous blog on the topic. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

 


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5 Problems Machine Learning Can Solve

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5 Problems Machine Learning Can Solve

Machine Learning, a subset of Artificial Intelligence, has taken the world by storm. A method of data analysis, it is a system that is equipped with expertise to learn from data, identify patterns and take decisions with minimal human intervention.

From clearing our email inboxes of spam to tagging our friends’ faces in social media photograph uploads, Machine Learning is crucial to all aspects of our lives. Here are some problems that Machine Learning can easily take care of.

Manual Data Entry

The problem of inaccuracy and duplication of data that business houses wish to avoid when automating their processes can be tackled with the help of Machine Learning (ML). A report says, “ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks.”

Moreover, ML knowledge workers can, nowadays spend more time solving problems of higher-value while ML takes care of repetitive work. “Arria, an AI based firm has developed a natural language processing technology which scans texts and determines the relationship between concepts to write reports.”

Detecting Spam

Spam detection, one of the earliest tasks for ML systems, has upgraded.“Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML.”This is because of ‘neural networks’ installed in spam filters, “Google now boasts of 0.1 percent of spam rate.”

Neural Networks fitted in spam filters can teach themselves to learn to recognize junk mail and phishing messages “by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.”

Product Recommendation

Unsupervised learning enables companies to put in place a product based recommendation system. By studying purchase history of a customer and a correspondingly large inventory of products, ML models can identify certain products in which a customer is likely to be interested.

“The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases.”

Medical Diagnosis

Machine Learning in the medical field is touted to improve patients’ health with minimum costs. “Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient.”

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Computer Vision

Computer vision “produces numerical or symbolic information from images and high-dimensional data. It involves machine learning, data mining, database knowledge discovery and pattern recognition.” Potential business applications of image recognition technology can be found in healthcare and automobiles. A tech giant has produced a computer vision powered earpiece that can narrate its interpretation of the outside world to a visually impaired person.

Machine Learning has many applications in industries the world over. For more on this, or a related subject, do peruse the DexLab Analytics website. DexLab Analytics is the best Machine Learning course in Delhi.


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AutoML (Machine Learning) in 2020

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AutoML (Machine Learning) in 2020

AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, is likely to become the most sought after development in computing sciences for more reasons than one.

Data scientists with competent skills who can work on big data, advanced analytics, and predictive models are few and hard to find. However, AutoML programs have made life easier for businesses and organisations by coming to the rescue of lesser skilled professionals.

Bridging the skill gap, AutoML is helping lesser skilled professionals build models using the best diagnostic and predictive analytics tools.

“AutoML packages like auto-sk learn can automatically do the model selection, scoring, and hyperparameter optimisation. Services like Amazon Forecast and Google’s Cloud AutoML also help in determining the algorithm to fit best with the data,” says a report.

With time, the amount of data generated by computer systems will have grown exponentially, and “the world of analytics, AI, machine learning and data science will see a wave of data and training. And, with the increasing amount of data, here’s why AutoML might be the most used technology in 2020.”

Hastening The ML Process

It takes human beings a longer time to build ML models than it takes automatic systems to, and accuracy is not always at par on the part of human beings. It would take less time for AutoML to construct a model and businesses are slowly preferring to use automated machine learning to amplify their predictive power for the need for insights from big data is only growing.

“An ML process typically consists of data pre-processing, feature selection, feature extraction, feature engineering, algorithm selection, and hyperparameter tuning. These take up more time to implement and require considerable expertise; AutoML, on the other hand, removes the trouble of going through some of these tedious processes.”

Addressing The Skills Gap

AutoMLis helping bridge the skills gap, especially in non-tech companies or companies with less data science expertise. “With the launch of Cloud AutoML, based on Neural Architecture Search (NAS) and transfer learning, Google believes that it has the potential to make the existing AI/ML experts more productive along with helping the less skilled engineered to build a powerful AI system.”

AutoML, also, hasmade machine learning a democraticsystem. It has helped “to carry out processes like hyperparameter tuning, selection of algorithms, and finding the appropriate model — as these tasks are tedious and at the same time complex.”

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Bettering Scalability

Machine Learning requires massive amounts of data to work on and training a model takes a long time, especially if the model is big. “AutoML, on the other hand, makes it easy to handle data, train model, evaluate, experiment, and even deploy the model for different use cases as it takes on the task to find the best algorithm for the task to be done.”

To enrol in a course on AutoML, do peruse the DexLab Analytics website today. DexLab Analytcis is a premiere Machine Learning training institute in Delhi and NCR.


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Why Machine Learning Matters

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Why Machine Learning Matters

Machine Learning, a subset of artificial intelligence, is a process of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that computing systems can learn from data, identify patterns in them and make intelligent decisions with minimal human intervention.

Importance of Machine Learning

The growth in volumes of data sets, and cheaper and more powerful computational processing and affordable data storage has triggered resurgence in interest in machine learning.

“All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks,” a report says.

Uses of Machine Learning

Machine Learning has been adopted by several key industries working with large amounts of data. Machine Learning helps businesses grow by gleaning actionable insights from these data sets.

Financial services

Machine Learning has revolutionised the banking sector giving financial institutions and banks the opportunity to “identify important insights in data, and prevent fraud.” The business insights can help companies identify investment opportunities or help investors know when to trade. “Data mining can also identify clients with high-risk profiles, or use cyber-surveillance to pinpoint warning signs of fraud.”

Government

Governmentsown an unimaginable amount of data and they can use this to their advantage. With the help of machine learning, they can mine data sets for insights. “Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.”

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Health care

Machine Learning has helped the healthcare industry evolve thanks to wearable devices and sensors that can use data to assess a patient’s health in real time and improve diagnosis and treatment. 

Retail

Machine Learning helps study and analyse customers’ purchase history and recommends what items a customer is likely to prefer buying. It predicts buying patterns and tastes and choices. It helps retailers offer a personalised experience to shoppers, implement a marketing campaign, optimize prices and plan merchandise supply.

Oil and gas

“Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.”

Transportation

“Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.”

For more on Machine Learning algorithms and artificial intelligence, do checkout the DexLab Analytics blog section. DexLab Analytics is a premiere institute of Machine Learning training in Delhi which trains professionals and students in all aspects of the technological science through both online classes and classes conducted in the National Capital Region.


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How AI and Machine Learning are Helping Fight Coronavirus

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How AI and Machine Learning are Helping Fight Coronavirus

A Toronto based AI-startup detected the outbreak of coronavirus, a large family of viruses which infect the respiratory tract of human beings and animals, hours after the first few cases were diagnosed in Wuhan in December 2019.

More than 100,000 people the world over have been infected by the novel coronavirus since then and more than 4000 people have died, most in China.

The start-up team confirmed their findings and informed their clients about an “unusual pneumonia” in a market place in Wuhan a week before Chinese authorities and international health bodies made formal announcements about the virus and the epidemic. The key to the company’s ability to detect and warn of a possible outbreak of an epidemic is AI and big data.

NLP and Machine Learning

The company uses natural language processing or NLP and machine learning to, says a report, “cull data from hundreds of thousands of sources, including statements from official public health organizations, digital media, global airline ticketing data, livestock health reports and population demographics. It’s able to rapidly process tons of information every 15 minutes, 24 hours a day.”

This information becomes the basis of reports compiled by computer programmers and physicians. Also, they do not just detect the outbreak of a disease but also track its spread and the consequences.

In the case of COVID-19, the company besides sending out an alert, correctly identified the cities that were highly connected to Wuhan using data on global airline ticketing “to help anticipate where the infected might be travelling.”

GDP

“Already, the COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets,” a report says.

The vast amount of X-rays and scans people across the world are undergoing in this outbreak of coronavirus has strained medical resources and systems across the world. That is why AI and machine learning models are being trained to read accurately vast amounts of data tirelessly, and efficiently.

Thermal Scanners

China has already deployed AI-powered thermal scanners at railway stations in major cities to read and record, from a distance through infrared, body temperatures of persons passing to detect a fever. This technology has to a large extant reduced stress on institutions across the country.

But it must be noted that AI is set to become a huge firewall against infectious diseases and pandemics not only by powering diagnostic techniques but by identifying potential vaccines and lines of treatment against the next coronavirus and COVID-19 itself within days.

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Robots

Also, AI and big data are helping revolutionize the medical management system in China. With the outbreak of the pandemic, China hospitals are using robots to reduce the stresses piled on medical staff. Ambulances in the city of Hangzhou are assisted by AI in navigation to help them reach patients and people suspecting an infection faster.

“Robots have even been dispatched to a public plaza in Guangzhou in order to warn passersby who aren’t wearing face-masks…China is also allegedly using drones to ensure residents are staying at home and reducing the risk of the coronavirus spreading further.”

In India, though the virus has been detected in some states, it has not spread as alarmingly as it has in other countries. It is now more than ever important to concentrate on building more robust and competent Artificial Intelligence courses in Delhi and Machine Learning courses in India.


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Why Python is Preferred in AI and Machine Learning?

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Why Python is Preferred in AI and Machine Learning?

Python has become one of the leading coding languages across the globe and for more reasons than one. In this article, we evaluate why Python is beneficial in the use of Machine Learning and Artificial Intelligence applications.

Artificial intelligence and Machine Learning are profoundly shaping the world we live in, with new applications mushrooming by the day. Competent designers are choosing Python as their go-to programming language for designing AI and ML programs.

Artificial Intelligence enables music platforms like Spotify to prescribe melodies to users and streaming platforms like Netflix to understand what shows viewers would like to watch based on their tastes and preferences. The science is widely being used to power organizations with worker efficiency and self-administration. 

Machine-driven intelligence ventures are different from traditional programming languages in that they have innovation stack and the ability to accommodate an AI-based experiment. Python has these features and more. It is a steady programming language, it is adaptable and has accessible instruments.

Here are some features of Python that enable AI engineers to build gainful products.

  • An exemplary library environment 

“An extraordinary selection of libraries is one of the primary reasons Python is the most mainstream programming language utilized for AI”, a report says. Python libraries are very extensive in nature and enable designers to perform useful activities without the need to code them from scratch.

Machine Learning demands incessant information preparation, and Python’s libraries allows you to access, deal with and change information. These are libraries can be used for ML and AI: Pandas, Keras, TensorFlow, Matplotlib, NLTK, Scikit-picture, PyBrain, Caffe, Stats models and in the PyPI storehouse, you can find and look at more Python libraries. 

  • Basic and predictable 

Python has on offer short and decipherable code. Python’s effortless built allows engineers to make and design robust frameworks. Designers can straightway concentrate on tackling an ML issue rather concentrating on the subtleties of the programming language. 

Moreover, Python is easy to learn and therefore being adopted by more and more designers who can easily construct models for AI. Also, many software engineers feel Python is more intuitive than other programming languages.

  • A low entry barrier 

Working in the ML and AI industry means an engineer will have to manage tons of information in a prodigious way. The low section hindrance or low entry barrier allows more information researchers to rapidly understand Python and begin using it for AI advancement without wasting time or energy learning the language.

Moreover, Python programming language is in simple English with a straightforward syntax which makes it very readable and easy to understand.

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Conclusion

Thus, we have seen how advantageous Python is as a programming language which can be used to build AI models with ease and agility. It has a broad choice of AI explicit libraries and its basic grammar and readability make the language accessible to non-developers.

It is being widely adopted by developers across institutions working in the field of AI. It is no surprise then that artificial intelligence courses in Delhi and Machine Learning institutes in Gurgaon are enrolling more and more developers who want to be trained in the science of Python.


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Skills Data Scientists Must Master in 2020

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Skills Data Scientists Must Master in 2020

Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.

Technical Skills

Python and R

Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.

GitHub

Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”

Preparing for Production

Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”

Cloud

Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.

Deep Learning

Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.

Math and Statistics

Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.

SQL

Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.

AutoML

Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.

Data Visualization

Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.

Non-Technical Skills

Ability to solve business problems

It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.

Effective business communication

A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.

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Agility

Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.

Experimentation

The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.


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Applications of Artificial Intelligence: Healthcare

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Applications of Artificial Intelligence: Healthcare

This article, the second part of a series, is on the application of artificial intelligence in the field of healthcare. The first part of the series mapped the applications of AI and deep learning in agriculture, with an emphasis on precision farming.

 AI has been taking the world by storm and its most crucial application is to the two fields mentioned above. Its application to the field of healthcare is slowly expanding, covering fields of practice such as radiology and oncology.

Stroke Prevention

In a study published in Circulation, a researcher from the British Heart Foundation revealed that his team had trained an artificial intelligence model to read MRI scans and detect compromised blood flow to and from the heart.

And an organisation called the Combio Health Care developed a clinical support system to assist doctors in detecting the risk of strokes in incoming patients.

Brain-Computer Interfaces

Neurological conditions or trauma to the nervous system can adversely affect a patient’s motor sensibilities and his or her ability to meaningfully communicate with his or her environment, including the people around.

AI powered Brain-Computer Interfaces can restore these fundamental experiences. This technology can improve lives drastically for the estimated 5,00,000 people affected by spinal injuries annually the world over and also help out patients affected by ALS, strokes or locked-in syndrome.

Radiology

Radiological imagery obtained from x-rays or CT scanners put radiologists in danger of contracting infection through tissue samples which come in through biopsies.  AI is set to assist the next generation of radiologists to completely do away with the need for tissue samples, experts predict.

A report says “(a)rtificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.”

Cancer Treatment

One reason why AI, has made immense advancements in the field of medical oncology is the vast amount of data generated during cancer treatment.

Machine learning algorithms and their ability to study and synthesize highly complex datasets may be able to shed light on new options for targeting therapies to a patient’s unique genetic profile.

Developing countries

Most developing counties suffer from health care systems working on shoe-string budgets with a lack of critical healthcare providers and technicians. AI-powered machines can help plug the deficit of expert professionals.

For example, AI imaging tools can study chest x-rays for signs of diseases like tuberculosis, with an impressive rate of accuracy comparable to human beings. However, algorithm developers must bear in mind the fact that “(t)he course of a disease and population affected by the disease may look very different in India than in the US, for example,” the report says. So an algorithm based on a single ethnic populace might not work for another.

Conclusion

It is no surprise then that developing countries like India are even more enthusiastic about adopting deep learning courses in Delhi and machine learning and artificial intelligence sciences in the healthcare sector. Machine Learning courses in India are coming up everywhere and it is important to note that DexLab Analytics is one of the leading artificial intelligence training institute in Gurgaon. Do visit the website today.


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8 Skills a Python Programmer Should Master

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8 Skills a Python Programmer Should Master

Python has become the lingua franca of the computing world. It has come to become the most sought after programming language for deep learning, machine learning and artificial intelligence. It is a favourite with programmers because it is easy to understand and learn and it achieves a lot more in terms of productivity as compared to other languages.

Python is a dynamic, high-level, general-purpose programming language that is useful for developing desktop, web and mobile applications that can also be used for complex scientific and numeric applications, data science, AI etc. Python focuses a lot on code readability.

From web and game development to machine learning, from AI to scientific computing and academic research, Data science and analysis, python is regarded as the real deal. Python is useful in domains like finance, social media, biotech etc. Developing large software applications in Python is also simpler due to its large amount of available libraries.

The Python developer usually deals with backend components, apps connection with third-party web services and giving support to frontend developers in web applications. Of course, one might create applications with use of different languages but pretty often Python is the language chosen for it – and there are several reasons for that.

In this article, we will walk through a structured approach to top 8 skills required to become a Python Developer. These skills are:

  • Core Python
  • Good grasp of Web Frameworks
  • Front-End Technologies
  • Data Science
  • Machine Learning and AI
  • Python Libraries
  • Multi-Process Architecture
  • Communication Skills

Core Python

This is the foundation of any Python developer. If one wants to achieve success in this career, he/she needs to understand the core python concepts. These include the following:

  • Iterators
  • Data Structures
  • Generators
  • OOPs concepts
  • Exception Handling
  • File handling concepts
  • Variables and data types

However, learning the core language (as mentioned above) is only the first step in mastering this language and becoming a successful Python developer.

Good grasp of Web Frameworks

By automating the implementation of redundant tasks, frameworks cut development time and enable developers to focus greatly on application logic rather than routine elements.

Because it is one of the leading programming languages, there is no scarcity of frameworks for Python. Different frameworks have their own set of advantages and issues. Hence, the selection needs to be made on the basis of project requirements and developer preference. There are primarily three types of Python frameworks, namely full-stack, micro-framework, and asynchronous.

A good Python web developer has incredible honing over either of the two web frameworks Django or Flask or both. Django is a high-level Python Web Framework that encourages a good, clean and pragmatic design and Flask is also widely used Python micro web framework.

Front-End Technologies (JavaScript, CSS3, HTML5)

Sometimes, Python developers must work with the frontend team to match together the server-side and the client-side. This means Python developers need a basic understanding of how the frontend works, what’s possible and what’s not, and how the application will appear.

While there is likely a UX team, SCRUM master, and project or product manager to coordinate the workflow, it’s still good to have a basic understanding of front-end tasks.

Data Science

Data science offers a world of new opportunities. Being a Python developer, there are several prerequisites you need to know starting with things you learn in high school mathematics, such as statistics, probability, etc. Some of the other parts of data science you need to understand, and use include SQL knowledge; the use of Python packages, data wrangling and data cleanup, analysis of data, and visualization of data.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning (as well as Deep Learning) are constantly growing. Python is the perfect programming language which is used in all the frameworks of Machine Learning and Deep Learning. This will be a huge plus for someone if he/she knows about this domain. If someone is into data science, then definitely digging in the Machine Learning topic would be a great idea.

Python Libraries

Python libraries certainly deserve a place in every Python Developer’s toolbox. Python has a massive collection of libraries, both native and third-party libraries. With so many Python libraries out there, though, it’s no surprise that some don’t get all the attention they deserve. Plus, programmers who work exclusively in one domain don’t always know about the goodies available to them for other kinds of work.

Python libraries are extensively used in simplifying everything from file system access, database programming, and working with cloud services to building lightweight web apps, creating GUIs, and working with images, ebooks, and Word files—and much more.

Multiprocessing Architecture

Multiprocessing refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance of the system. As a Python-Developer one should definitely know about the MVC (Model View Controller) and MVT (Model View Template) Architecture. Once you understand the Multi-Processing Architecture you can solve issues related to the core framework etc.

Communication Skills

In best software development firms the teams are made out of amazing programmers which work together to achieve the final goal – no matter if it means to finish the project, to create a new app or maybe to help a startup. However, working in a team means that a developer has to communicate well – not only to get the stuff done but also to keep the documentation clear so others can easily read and follow the thinking path to fully understand the idea.

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Conclusion

In this write-up, we have elaborated on the top skills one needs to have to be a successful Python Developer. One must have a working knowledge of Core Python and a good grasp of Web Frameworks, Front-End Technologies, Data Science, Machine Learning and AI, Python Libraries, Multi-Process Architecture and Communication skills. Though there are a few more skills not listed in this blog, one can achieve success in developing large software applications by mastering all the above skills only.

As delineated in the article, Python is the new rage in the computing world. And it is no surprise then that more and more professionals are opting to take up courses teaching Machine learning using Python and python for data analysis.

 

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