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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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Applications of Artificial Intelligence: Agriculture

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Applications of Artificial Intelligence: Agriculture

This article, the first part of a series, is on the application of artificial intelligence in agriculture. Popular applications of AI in agriculture can be sectioned off into three aspects – AI powered robots, computer vision and seasonal forecasting.

Robots

Firstly, companies are now gradually adopting AI powered machines to automate agricultural tasks such as harvesting larger volumes of crops faster than human workers. For instance, companies are using robots to remove weeds and unwanted plants from fields.

Computer Vision

Secondly, companies are using computer vision and deep learning algorithms to process and study crop and soil health. For instance, farmers are using unmanned drones to survey their lands in real time to identify problem areas and areas of potential improvement. Farms can be monitored frequently using these machines than they can be with farmers doing so on foot.

Seasonal Forecasting

Thirdly, AI is used to track and predict environmental impacts such as weather changes. “Seasonal forecasting is particularly valuable for small farms in developing countries as their data and knowledge can be limited. Keeping these small farms operational and growing bountiful yields is important as these small farms produce 70% of the world’s crops,” says a report .

The India story

In India, for instance, farmers are gradually working with technology to predict weather patterns and crop yield. Since 2016, Microsoft and a non-profit have together developed an AI sowing application which is used to guide farmers on when to sow seeds based on a study of weather patterns, local crop yield and rainfall.

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In the year 2017, the pilot project was broadened to encompass over 3,000 farmers in Andhra Pradesh and Karnataka and it was found that those farmers who received the AI-sowing app advisory text messages benefitted wherein they reported 10–30% higher yields per hectare.

Chatbots

Moreover, farmers across the world have begun to turn to chatbots for assistance and help, getting answers to a variety of questions and queries regarding specific farm problems.

Precision Farming

Research predicts the precision agriculture market to touch $12.9 billion by 2027. Precision agriculture or farming, also called site-specific crop management or satellite farming, is a concept of farm management that utilizes information technology to ensure optimum health and productivity of crops.

With this increase in the volume of satellite farming, there is bound to be an increase in the demand for sophisticated data-analysis solutions. One such solution has been developed by the University of Illinois. The system developed aims to “efficiently and accurately process precision agricultural data.”

A professor of the University says, “We developed methodology using deep learning to generate yield predictions…”

Conclusion

The application of artificial intelligence to analyze data from precision agriculture is a nascent development, but it is a growing one. Environment vagaries and factors like food security concerns have forced the agricultural industry to search for innovative solutions to protect and improve crop yield. Consequently, AI is steadily emerging as the game changer in the industry’s technological evolution.

It is no surprise then that AI training institutes are mushrooming all across the world, especially in India. For the best artificial intelligence certification in Delhi NCR, do check out the DexLab Analytics site today.


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AI joins the fight against Cancer

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AI joins the fight against Cancer

Cancer is the emperor of all maladies. Finding a cure to it is one of the biggest challenges in the world of medicine. More and more men and women, one in five men and one in six women worldwide likely to be afflicted, are falling prey to the malady. It is something that has spurred on the fight against the disease even more intensely.  AI and machine learning has increased the scope of groundbreaking research in the field and it is worth knowing a little about.

One reason why AI, which has made inroads into numerous sectors of the economy, has made immense advancements in the field of medical oncology is the vast amount of data generated during cancer treatment. With the assistance of AI, say scientists, this vast trove of data can be mined and worked to improve methods of diagnosis and preventive cures and treatments.

Detection of Cancer

Machine learning can lead to early detection and timely treatment in many cases. Because cancer is treated in stages, unlike other diseases, machine learning can come in handy when it comes to detection of precancerous lesions in tissues.

AI utilizing tools can assist radiologists in graphically and visually studying images by revealing suspicious lesions. This process not only reduces the work load of radiologists but it also makes possible the detection of miniscule lesions which could otherwise be overlooked.

Detection of Breast Cancer

“DeepMind and Google Health collaborated to develop a new AI system that helps in detecting breast cancer accurately at a nascent stage. Being the most common cancer in women, breast cancer, has seen an alarming rise over the past few years. Though early detection can improve a patient’s prognosis significantly, mammography, which is the best screening test currently available, is not entirely error-proof”, says a report.

To correct this, researchers at DeepMind and Google Health designed an algorithm on mammogram images and noticed AI systems reduced the recurrence of errors. They discovered that AI systems functioned better than human radiologists. A few startups in India are also laboring in the arena of cancer detection.

Predicting Cancer Evolution

Besides detection, AI is useful in the treatment of cancer as well. It is critical to the survival of patients in that it is used to predict growth and evolution of cancers which could help doctors prepare a treatment plan and save lives.

Identifying Effective Treatments

AI can play a significant role in the overall treatment of the patient, especially precision medicine which is the administering of personalized medicine from a pool of generic medication beneficial to the patient. AI can also be used to design new drugs.

Thus, AI has created a huge potential for changing the mode of treatment of cancer patients. According to the report, Exscientia is the first company, globally, to have overtaken conventional drug designing processes by automating the whole process using AI. Another company is trying to do the same in Bangalore.

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It is no surprise then that AI is being even more widely adopted across sectors of healthcare and medicine. More and more professionals, the world over, are enrolling in courses teaching AI, deep learning and machine learning. For the best such institute in India, or for the best artificial intelligence training institute in Gurgaon, do not forget to visit the DexLab website today.

 

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How AI and Deep Learning Helps In Weather Forecasting

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How AI and Deep Learning Helps In Weather Forecasting

The world’s fight against extreme weather conditions and climate change is at the forefront of all discussions and debates on the environment. In fact, climate change is the biggest concern we are faced with today, and studying the climate has increasingly become the primary preoccupation of scientists and researchers. They have received a shot in the arm with the increase in the scope of artificial intelligence and deep learning in predicting weather patterns.

Take for instance the super cyclone Amphan that has ravaged West Bengal and Orissa. Had it not been for weather forecasting techniques, meteorologists would never had predicted the severity of the cyclone and the precautionary evacuation of thousands of people from coastal areas would not have been taken, leading to massive loss of lives. This is where the importance of weather forecasting lies.

Digitizing the prediction model

Traditionally, weather forecasting depends on a combination of observations of the current state of the weather and data sets from previous observations. Meteorologists prepare weather forecasts collecting a wealth of data and running it through prediction models. These sets of data come from hundreds of observations like temperature, wind speed, and precipitation produced by weather stations and satellites across the globe. Due to the digitization of these weather models, accuracy has improved much more than it was a few decades ago. And with the recent introduction of machine learning, forecasting has become an even more accurate and exact science.

Machine Learning

Machine learning can be utilized to make comparisons between historical weather forecasts and observations in real time. Also, machine learning can be used to make models account for inaccuracies in predictions, like overestimated rainfall.

At weather forecast institutions, prediction models use gradient boosting that is a machine learning technique for building predictive models. This is used to correct any errors that come into play with traditional weather forecasting.

Deep Learning

Machine Learning and Deep Learning are increasingly being used for nowcasting, a model of forecasting in the real time, traditionally within a two-hour time span. It provides precipitation forecasts by the minute. With deep learning, a meteorologist can anywhere in the vicinity of a weather satellite (which runs on deep learning technology) use nowcasting rather than just those who live near radar stations (which are used in traditional forecasting).

Extreme Weather Events

Deep learning is being used not only for predicting usual weather patterns, it is being used to predict extreme weather conditions as well. Rice University engineers have designed a deep learning computer system that has trained itself to predict, in accurate terms, extreme weather conditions like heat waves or cold waves. The computer system can do so up to five days in advance. And the most fascinating part is it uses the least information about current weather conditions to make predictions.

This system could effectively guide NWP (numerical weather prediction) that currently does not have the ability to predict extreme weather conditions like heat waves. And it could be a super cheap way to do so as well.

According to sciencedaily.com, with further development, the system could serve as an early warning system for weather forecasters, and as a tool for learning more about the atmospheric conditions that lead to extreme weather, said Rice’s Pedram Hassanzadeh, co-author of a study about the system published online in the American Geophysical Union’s Journal of Advances in Modeling Earth Systems.

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Thus, it is no surprise then that machine learning and deep learning is being widely adopted the world over. In India, is it being taken up as a form of study and training in metropolitans like Delhi and Gurgaon. For the best Machine Learning course in Delhi and deep learning course in delhi, check out the DexLab Analytics website today.

 

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Budget 2020 Focuses on Artificial Intelligence in a Bid to Build Digital India

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Budget 2020 Focuses on Artificial Intelligence in a Bid to Build Digital India

The Indian technology industry has welcomed the 2020 budget for its outreach to the sector, specially the Rs 8000 crore mission for the next five years on Quantum Computing. The budget has been praised in general for its noteworthy allocation of funds for farm, infrastructure and healthcare to revive growth across sectors in the country.

According to an Economic Times report, Debjani Ghosh, President, NASSCOM, reacting to the budget, said, “Budget 2020 and the finance minister’s speech has well-articulated India’s vision on not just being a leading provider of digital solutions, but one where technology is the bedrock of development and growth’.

Industry insiders lauded the budget for the allocation on Quantum Computing, the policy outline for the private sector to construct data center parks and the abolition of the Dividend Distribution Tax. The abolition of the Tax had been a long standing demand of the industry and the move has been welcomed. The building of data parks will help retain data within the country, industry experts said.

Moreover, while announcing the budget this year, Finance Minister Nirmala Sitharaman spelt out the government’s intentions of utilizing, more intensely, technology, specially artificial intelligence and machine learning.

These will be used for the purposes of monitoring economic data, preventing diseases and facilitating healthcare systems under Ayushman Bharat, guarding intellectual property rights, enhancing and improving agricultural systems and sea ports and delivery of government services.

Governments the world over have been emphasising the deployment of AI for digital governance and research. As per reports, the US government plans and intends to spend nearly 1 billion US dollars on AI-related research and development this year.

The Indian government has also planned to make available digital connectivity to citizens at the gram panchayat level under its ambitious Digital India drive with a focus on carrying forward the benefits and advantages of a digital revolution by utilizing technology to the fullest. One lakh gram panchayats will be covered under the Rs 6000 crore Bharat Net project wherein fibre connectivity will be made available to households.  

“While the government had previously set up a national portal for AI research and development, in the latest announcement, the government has continued to offer its support for tech advancements. We appreciate the government’s emphasis on promoting cutting-edge technologies in India,” Atul Rai, co-founder & CEO of Staqu said in a statement, according to a report by Live Mint.

The Finance Minister also put forward a plan to give a fillip to manufacturing of mobiles, semiconductor packaging and electronic equipment. She iterated that there will be a cost-benefit to electronics manufacturing in India.

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Thus, this article shows how much the government of India is concentrating on artificial intelligence and machine learning with a push towards digital governance. It shows that the government is recognising the need to capitalise on the “new oil” that is data, as the saying goes. So it is no surprise then that more and more professionals are opting for Machine Learning Course in India and artificial intelligence certification in delhi ncr. DexLab Analytics focuses on these technologies to train and skill professionals who want to increase their knowledge base in a digital first economy.

 

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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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