#matplotib

[incegna]

The three best and most important Python libraries for data science are NumPy, Pandas, and Matplotlib. NumPy and Pandas are great for exploring and playing with data. Live training : 08.11.19 @9pm. Interested people can share me your contact me. Inbox me your details and confirm your slot. #python,#machinelearning,#datascience,#numpy,#matplotib,#pandas,#coding,#programs,#data,#analyst,#libraries,#artificial,#intelligence,#AI,#ML,#DS,#graphs,#googlesheets,#syntax,#keywords,#loops,#datatypes,#deeplearning Check our Info : www.incegna.com Reg Link for Programs : http://www.incegna.com/contact-us Follow us on Facebook : www.facebook.com/INCEGNA/? Follow us on Instagram : https://www.instagram.com/_incegna/ For Queries : enquiry@incegna.com https://www.instagram.com/p/B4jqQIIgE4x/?igshid=1lw92dth8xm42

[sharwareeshinde11-blog]

Future Of Data Science

What is data science?

Data science is a field that uses scientific method, process, algorithms and system to extract knowledge from data in various forms, structural, unstructured it is similar to data mining.

Data science is a concept to unify statistic, data analysis, machine learning and their related method in order to understand actual phenomena with data .It employs theory and techniques drawn from many field within the context of mathematics, statistics, information science and computer science

How to learn data science

Data science is a very practical field, and so it’s so important to apply theoretical knowledge that you have gathered. For example, when you learn starts, don’t just sit and read about it .see how it can be done with a programming language such as python, which a very popular language for beginners, find a data set and apply these concept on the data. Including other language like

1 linear algebra and calculus –for these refer to the book called advanced engineering mathematics by kreyszig. The various topics that you need to brush up your understanding in are hyper parameters, regularization function, cluster analyses are few topics under this domain and which is important for machine learning

2 vector calculus

3 Statistics—go on learning statistics on various sites like udacity, khan academy, courser. They provide detailed overview on this topic which is a vital topic under data science.

4 programming language---The most widely preferred open source statistical tools are ‘R’ and ‘Python’.

FOR R--start learning libraries like dplyr, tidyr, data table for data manipulation and ggplot2 for visualization which has the same syntax as R.in fact the visualization in ggplot2 is way better than that of matplotib

   Go to kaggle and download data sets so that you can practice the concept that you have studied and build project and put up on your Git profile  

How To Prepare for the Future of Data Science

There are many ways companies can and should prepare for the future of data science. These include creating a culture for using machine learning models and their output, standardize and digitize processes, experimenting with a cloud infrastructure solution, have an agile approach to data science projects and creating dedicated data science units. Being able to execute on some of these points will increase the likelihood of succeeding in a highly digitized world.

A Data Science Unit

In my previous role I was working as a data scientist for an insurance company. One of the smart moves they made was to create an analytics unit, which worked across company verticals.

This made it easier for us to reuse our skills and models on a variety of data sets. It was also a signal to the rest of the company that we had a focus on data science and that this was a prioritized issue. If a company has a certain size, creating a dedicated data science unit is definitely the right move to make.

Standardization

Standardization of processes is also important. This will make it easier to digitize and perhaps automate these processes in the future. Automation is a key driver for growth, making it much easier to scale. An added bonus is that the data collected from automated processes is usually a lot less messy and less error prone than data collected from manual processes. Since an important enabler of data science models is access to good data, this will help make the models better.

Adoption of Data Science                                  

There should also be a culture in the company for adopting the use of machine learning algorithms and using their output in business decisions. This is of course often easier said than done since many employees might fear that the algorithms are making them obsolete.

It is therefore critical that there be a strong focus on how employees can use their existing skill set alongside algorithms to make more high-level and tactical business decisions, as this combination of human and machine is likely to be the future of work in many occupations. It will probably be more than a few years before the machine learning algorithms are able to navigate alone and make superhuman decisions in an open world setting, meaning mass unemployment due to the rise of the machines is not a likely scenario in the near future.

Always Experiment

With new data being generated from IoT sources, it is important to explore new data sets and see how they can be used to augment your existing models. There is a constant flow of new data waiting to be discovered.

Perhaps including two new variables from an obscure data set into your model will increase the precision of the leads generating model by 5% — and perhaps not. The point is to always experiment and not be afraid to fail. Like all other scientific inquires, failed attempts abound, and the winners are those who keep on trying.

Create an environment that promotes experimentation and that tries to make incremental improvements to existing business processes. This will make it easier for data scientist to introduce new models and will also set the focus on the smaller improvements, which are a lot less risky than the larger grand visions. Remember, data science is still a lot like software development and the more complex the project becomes the more likely it is to fail.

Try building an app that your customers or suppliers can use to interact with your services. This will make it easier to gather relevant data. Create incentives to promote usage of the app which will increase the amount of data being generated. It is also imperative that the UX of the app be appealing and promotes use.

We might need to venture outside of our comfort zones to take on the opportunities and challenges that this digital gold brings. As the amount of data continues to grow, machine learning algorithms get smarter and our computational abilities improve, we will need to adapt. Hopefully, by creating a strong environment for using data science your company will be better prepared for what the future will bring.

Applications / Uses of Data Science

Using data science, companies have become intelligent enough to push & sell products as per customer’s purchasing power & interest. Here’s how they are ruling our hearts and minds:

Internet Search

When we speak of search, we think ‘Google’. Right? But there are many other search engines like Yahoo, Bing, Ask, AOL, Duckduckgo etc. All these search engines (including Google) make use of data science algorithms to deliver the best result for our searched query in fraction of seconds. Considering the fact that, Google processes more than 20 petabytes of data every day. Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.

Digital Advertisements (Targeted Advertising and re-targeting)

If you thought Search would have been the biggest application of data science and machine learning, here is a challenger – the entire digital marketing spectrum. Starting from the display banners on various websites to the digital bill boards at the airports – almost all of them are decided by using data science algorithms.

This is the reason why digital ads have been able to get a lot higher CTR than traditional advertisements. They can be targeted based on users past behavior. 

Speech Recognition

Some of the best example of speech recognition products are Google Voice, Siri, Cortana etc. Using speech recognition feature, even if you are not in position to type a message, your life wouldn’t stop. Simply speak out the message and it will be converted to text. However, at times, you would realize, speech recognition doesn’t perform accurately.

Image Recognition

You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses face recognition algorithm. Similarly, while using whats app web, you scan a bar code in your web browser using your mobile phone. In addition, Google provides you the option to search for images by uploading them. It uses image recognition and provides related search results.

Gaming

EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have led gaming experience to the next level using data science. Games are now designed using machine learning algorithms which improve / upgrade themselves as the player moves up to a higher level. In motion gaming also, your opponent (computer) analyzes your previous moves and accordingly shapes up its game.

Fraud and Risk Detection

 One of the first applications of data science originated from Finance discipline. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paper work while sanctioning loans. They decided to bring in data science practices in order to rescue them out of losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power

Airline Route Planning

Airline Industry across the world is known to bear heavy losses. Except a few airline service providers, companies are struggling to maintain their occupancy ratio and operating profits. With high rise in air fuel prices and need to offer heavy discounts to customers has further made the situation worse. It wasn’t for long when airlines companies started using data science to identify the strategic areas of improvements. Now using data science, the airline companies can:

1.   Predict flight delay

2.   Decide which class of airplanes to buy

3.   Whether to directly land at the destination, or take a halt in between (For example: A flight can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt in any country.)

4.   Effectively drive customer loyalty programs

Southwest Airlines, Alaska Airlines are among the top companies who’ve embraced data science to bring changes in their way of working

Coming Up In Future

Though, not much has been reveled about them except the prototypes, and neither I know when they would be available for a common man’s disposal. Hence,  We need to wait and watch how far Google can become successful in their self driving cars project. 

Self Driving Cars

[nearmesblog]

Python is the most widely used and growing app development language mainly in a startup environment where time and budget are usually limited. As a web development language, it is aspect-oriented which means there are modules with various functionality. So first the software development programmers, have to create the modules and after, based on the ‘if-then action’, depending on the user’s action, the algorithm initiations a particular block and brings the outcome. The Python developers are usually working with back-end components, app developers connection with third-party web services, and giving support to flutter developers in web applications.

8 skills that are required for a python app developers

Expertise in core python.

Good grasp of web development frameworks.

Object-relational mappers.

Road to data science.

Machine learning and AI.

Deep learning.

Good grasp of multi-process app development architecture.

Analytical skills.

Expertise in core python

Before learning any flutter development framework, it is essential to learn the core concepts of any app development programming language. You should have a good solid grasp on different software development foundational concepts such as:

Variables & data types.

Data structure.

Exception handling.

File handling.

Object-oriented app development programming.

and more.

If you don’t know where to begin, you can find some good and useful flutter development resources on the internet. You can even use the python reference books as they do an amazing job of covering these topics in their depth.

Good grasp of web frameworks

Flask and Django are the most famous Python web developers framework. A programmer should have an outstanding grasp of either of these web development frameworks. Flask is a Python micro web developers framework that is widely used. Django is a high-level python web framework and functional design.

Object-relational mappers

An object-oriented app development programming language is used to convert data between two incompatible type systems, and that technique is called object-relational mapper. App developers also use many customizable ORM tools.

Road to data science

In the data science field, python web developers can grow very much by having a good understanding of the popular aspect of data science like:

Great knowledge of SQL.

Knowledge to use python packages as Scikit learn, MatPlotib, NumPy, etc.

Cleaning up of app developers data.

Data wrangling.

Analysis of software development data.

Visualization of app development data.

Mathematical knowledge.

Knowledge of probability.

Knowledge of statistics.

Machine learning and AI

A flutter developers in data science should have good knowledge about AI and machine learning as it comes under data science. From the beginner’s point of view, and also be well aware of machine learning algorithms and one must have a good grasp of neural networks, produce insight from given app development data, visualization of data, data analysis, and collection of data from a different source.

Deep learning

Deep learning is one of the fastest-growing fields that come under data science. One is expected to have a good grasp of deep learning and neural network architecture. This is after covering all the aspects of AI, machine learning, and the basics of data science.

Other aspects of deep learning, such as:

Audio processing.

Video processing.

NLP.

Understand multi-process architecture

A flutter developers must know how codes are used in the working of release and deployment. a Web designers engineer does this, but a app developers needs to know the basic understanding. Python web developers should learn about MVT and MVC architecture. A software developers would resolve the issues in the core app development framework after knowing the multi-process architecture.

Analytical skills

To become good python web developers in the future, one should have good analytical skills in the field of data science. The analytical skills may need a good understanding of developing useful sites for web development, visualizing datasets for data science in a better way, writing clean code that is not redundant, able to write optimized algorithms, etc.

Conclusion

Anyone can learn to write code but the below points are gives you an upper hand over the competition, are the tactics you take, your code’s efficiency, your expertise with the numerous packages and flutter development tools. A skilled app developers who possesses these skills stands out from the rest. If you are also inspired by this write-up and want to improve yourself by adopting the skills mentioned here, we strongly suggest you do. These skills help you improve as a Python web developers and also help you land a better job.

[nearlearnbangalore]

What Skills are Necessary for Python Developers?

Python is a popular programming language favored by many companies. 

Being a good Python developer requires many skills. In this article, we'll discuss what skills Python developers should possess to be more successful.

Exactly what is Python?

It is an object-oriented programming language used by programmers to design desktop apps, to build systems, websites, and platforms. 

There is no doubt that this language is the first choice for freshers and beginners in the tech world, even though it is not the most popular language.

How do Python developers work?

Using the Python programming language, Python developers create, design, test, deploy, and debug computer applications and programs. 

As well as this, a Python developer is also responsible for finding errors within Python projects.

Does Python have a high demand?

Top companies like Google, Spotify, Netflix, and Pinterest must use Python for their back-end services for a good reason. 

These are some of the features Python has over its counterparts.

Ruby is known for its complexity, while Python is known for its simplicity.

Python is more dynamic than Java

Machine learning projects work better in Python

The flexibility and readability of Python are greater than those of PHP.

Python is favored by startups because of its low development costs

Unlike C, Python hasn't had a strong historical foundation but is on the verge of dominating the field.

The best Python institute that is highly recommended is Near Learn if you are looking for the best Python training with 100% placement.

Here are the top 10 skills needed to become a Python developer:

1. Expertise in Python Core

Core Python knowledge is necessary for the developer. It includes: 

Concepts of OOPs

Data types and variables

Concepts related to file handling

Handling exceptions

Generating systems

The iterator

2. Object Relational Mapping

Object Relational Mapper (ORM) is an object-oriented programming language that is used to convert data between two incompatible systems. 

Object Relational Mappers can create virtual object databases from any programming language.

3. The Data Science Roadmap

Python developers can gain a great deal of knowledge in Data Science by becoming familiar with popular aspects like:

Good SQL knowledge

Data science knowledge of Python packages such as Scikit learns, MatPlotib, NumPy, etc.

Cleanup of Data

Wrangling data

Data Analysis

Data Visualization

Knowledge of mathematics

Probability knowledge

Statistics knowledge

You will be better prepared for your dream job if you find the best data science course.

4. Artificial Intelligence and Machine Learning

AI and Machine Learning are two parts of Data Science. 

From a beginner's perspective, you should be familiar with all aspects of Data Science, along with Machine Learning algorithms. 

In order to produce insights from data, one must understand how to acquire data from datasets, analyze data, visualize data.

Summary

This article is most likely directed towards those who want to become Python developers. As a Python developer, you need to have the following skills. I hope you found this article useful.

Check out the best Python training program if you're interested in learning Python and getting your hands dirty on a variety of tools and libraries.

[educationtech]

Use of Python language in a Engineering Career - Arya College

Python language is one of the most popular programming languages that shows an incredible growth and popularity every year. Students of top engineering colleges in Jaipur calculates that python could beat all other programming languages. As it has become the fastest-growing programming language across the globe. Also, it is considered one of the best programming languages for machine learning. Some of the reasons of its popularity includes the following:

1. Easy to Learn and Use

Python language is very easy and simple to use and learn for new beginners and newcomers. The language is one of the most accessible programming languages available to the students of Computer Science Engineering Colleges in Rajasthan because it has simplified syntax which provides more emphasis on natural language. Due to the easy learning and usage, python codes can be easily written and executed much faster when compared to other programming languages.

One of the main reasons for the popularity of python would be its simplicity in syntax. So that it could be easily read and understood even by the young developers. Also, one can quickly experiment by changing the code base of python because it is an interpreted language which makes it even more popular among different kinds of developers.

2. Mature and Supportive Python Community

Python was created more than 30 years ago, and consumes lots of time for any community of programming language to grow and mature adequately to support developers ranging from beginner to expert levels. There are plenty of documentation, guides and Video Tutorials for Python language that are available to the learners and developers of Best Engineering College in Jaipur of any skill level or ages can use and receive the support required to enhance their knowledge in python programming language.

Many graduates get introduced to computer science only through Python language. Which is the same language used for in-depth research projects. If any programming language lacks developer support or documentation, then they do not expand too much. But python has no such kind of problems because it has been there for a very long time. The python developer community is one of the most incredibly active programming language communities today.

3. Support from Renowned Corporate Sponsors

Programming languages grows faster when a corporate sponsor works on it. For instance, PHP is backed by Facebook, Visual Basic & C# by Microsoft, Java by Oracle and Sun. Python Programming language is mainly backed by Facebook, Amazon Web Services, and especially Google.

Google adopted python language in the year 2006 and have used it for many applications and platforms since then. Lots of Institutional effort and money have been devoted to the training and success of the python language developed by Google. They have even created a dedicated portal for python. The list of support documentation and tools keeps on growing for python language in the developers’ world.

4. Hundreds of Python Libraries and Frameworks

Due to its corporate sponsorship and big supportive community of python, python has excellent libraries that students of computer science engineering colleges can use to select and save both your time and effort on the initial cycle of development. Also, there are various cloud media services that offer cross-platform support through library-like tools, which can be extremely beneficial.

Libraries with particular focus are also available like nltk for natural language processing or scikit-learn for machine learning applications. There are multiple frameworks and libraries that are available for python language like matplotib for plotting charts and graphs, SciPy for engineering applications, science, and mathematics, Beautiful Soup for HTML parsing and XML, NumPy for scientific computing, and Django for server-side web development.

5. Versatility, Efficiency, Reliability, and Speed

The python language is reliable, efficient, and much faster than most modern languages. Python can be used in nearly any kind of environment by the students of top BTech colleges, and one will not face any kind of performance loss issue irrespective of the platform one is working.

Python language can be used in different environments like mobile applications, web development, desktop applications, hardware programming, and many more. The versatility of python makes it more attractive to use due to its various applications.

6. Big data, Machine Learning and Cloud Computing

Machine Learning, Cloud Computing, and Big Data are some of the hottest trends in the computer science world right now, which helps lots of organizations to transform and improve their processes and workflows.

Python language is the second most popular used tool after R language for both data science and analytics. Lots of many data processing workloads in the organization are powered by python language by the students of top private engineering colleges in Jaipur. Most of the research and development consider python language due to its many applications like ease of analyzing and organizing the usable data. Hundreds of python libraries are being used in thousands of machine learning projects every day, like TensorFlow for neural networks and OpenCV for computer vision, etc.

7. First-choice Language

Python language considered as the top choice for many programmers and students due to the main reason for python being in high demand in the development market. Developers and Students always look forward to learning a language that is in high demand. Now, Python is undoubtedly the hottest cake in the market.

Many programmers and data science students of top BTech college in Jaipur are using python language for their development projects. Learning python is one of the most essential section in data science certification courses. In this way, the python language can provide various fantastic career opportunities for students. Due to the variety of applications of python, one can pursue different career options.

8. The Flexibility of Language

The python is flexible that it provides the developer the chance to try something new. The person who is an expert in this language can build similar kinds of things. And, you can go on to try to make something different than before.

Python does not restrict developers from developing any sort of application. This kind of freedom and flexibility by just learning one language is not available in different programming languages.

9. Use of python in academics

Python treated as the core programming language in colleges and schools due to its countless uses in Deep Learning, Artificial Intelligence, Data Science, etc. Now, it has become a fundamental part of the development world that schools and colleges cannot afford not to teach python language. In this way, it expands more python Developers and Programmers and further expands its growth and popularity.

10. Automation

Python language can help a lot in automation of tasks with lots of tools and modules available. Which makes things much more comfortable. It is incredible to know that one can reach an advanced level of automation easily by using necessary python codes. Python is the best performance booster mainly in the automation of software testing. One will be amazed at how much less time and few numbers of lines must write codes for automation tools.

[techcybersblog]

8 REASONS WHY PYTHON IS BECOMING MORE POPULAR THAN JAVA AND C

The popularity and growth of Python continue to be astounding year after year. The Stack Overflow site predicted that Python would be the fastest-growing language in the world by 2020, meaning it would eliminate all other programming languages. Aside from being one of the most popular languages for machine learning, it is also known for its ease of use. Python is very popular, so why Python is important ? Here's what you need to know:

Easy to use and to learn

Beginners and newcomers can easily get to grips with Python as it is very easy to learn and use. Python has simplified syntax, which makes it one of the easiest to learn. Its simple nature emphasizes natural language more than programmers' syntax. In addition to being generally easier to use and learn than other programming languages, Python is much faster to write and execute than most other languages.

A mature, supportive community for Python

It's been over 30 years since Python was created, which is a long time for any community of programming languages to grow and mature adequately to support programmers at all levels, including beginners. A developer of all levels, from beginner to expert, can offer help when someone is having problems with the Python programming language. When it comes to a project's development, timing is crucial, as delays could cause the project's development to stall.

Corporate sponsorship from internationally known companies

When a corporate sponsor supports a programming language, its growth is faster. The PHP programming language is supported by Facebook, while Java is supported by Oracle and Sun. A large number of companies, including Facebook, Amazon Web Services, and most importantly Google, back Python as a programming language.

Since 2006, Google has been using Python for many applications as well as platforms. The training and success of Python by Google has involved a great deal of institutional effort and money. Python even has its own portal. Developers have access to a growing number of Python tools and documentation.

Language of first preference

Due to the high demand for the Python language in the development market, many programmers and students opt for the Python language. Languages that are in high demand are always attractive to students and developers. In the current market, Python is undeniably the hottest software.

Python is flexible

A programmer can explore new possibilities thanks to the flexible nature of Python. Any kind of application can be developed with Python. There are no other programming languages that offer such freedom and flexibility.

Python in academic research

As a result of its many uses in Artificial Intelligence, Deep Learning, Data Science, etc., Python is now taught as the code-base language in schools and colleges. Thousands of schools and colleges around the world teach Python as it has become so fundamental to the development world.

Automatisation

The performance of automated software testing is best enhanced by Python. Writing coding code is easy with automation tools. You will be amazed at how few lines of code are needed and how much less time it takes to do it.

Python libraries and frameworks

Besides the excellent libraries, python has a generous corporate sponsorship and a supportive community that makes python a good choice for selecting and saving your time and effort on the initial cycle. Cloud media services also offer cross-platform support via library-powered tools, which can be highly beneficial. Python language offers many frameworks and libraries, such as:

The graphing tool matplotib

Science, math, and engineering applications in SciPy

For HTML and XML parsing, BeautifulSoup

Statistical computing with NumPy

Development of web applications with Django.

Get more tips follow the link: https://www.itscybertech.com/2021/07/8-reasons-why-python-is-becoming-more.html

[gayatrisc]

Top 13 Skills to become a good python developer

Python is one of the most popular and most used high-level programming languages in the software field. If you want to become a good Python developer, you must need to learn some essential skills.

In this article, we will discuss top Python developer skills which are necessary to be a better python developer.

1. Core Python Knowledge

Python developer must have an excellent understanding of core python knowledge. One must know the concepts of core python such as:

concepts of File handling

Iterators

Generators

Exception Handling

Data Structures

Data types and variables

OOPs Concepts

Also Read: How to become a Master in Python Programming?

2. Good understanding of Python web Frameworks

Django is the most popular Python web framework, and a Python developer must have an excellent knowledge of these web frameworks. After Django, Flask is the most important and useful Python micro web framework. Django is a high-level Python web framework offers a practical, clean and functional design. Good knowledge of JavaScript, CSS, and HTML is also helpful to become a great Python Developer.

3. ORM

ORM is basically used for the conversion of data between two incompatible systems, and that technique is what we call Object Relational Mapper (ORM). A virtual object database is created by Object Relational Mappers from any programming language.customizable ORM tools are also available which are used by programmers.

4. Data Science Concepts

A Python developer can also apply knowledge in Data science field and grow very much in that field also. For this, one should have knowledge of things such as:

understanding of SQL

Knowledge of data science-related Python libraries such as Scikit learn, MatPlotib, NumPy, etc.

Data Cleaning and wrangling

Data Analysis and Visualization

Mathematics and Statistics Knowledge

Knowledge of Probability

5. AI and Machine Learning Skill

If you are working in field of Data Science, then you must have sound knowledge of Artificial Intelligence and Machine Learning as this both technologies comes under Data Science. Developer should have strong hold on Machine Learning algorithms. Also, one should have a good knowledge of Artificial Neural Networks, Data analysis, collection of data from various sources, insight from data and visualization of data.

6. Knowledge of Deep Learning

Deep Learning is also a field related to Data Science. To become a good Python Developer, One should have a good understanding of the basic concepts in deep learning and Neural network architecture. There are so many important concepts of Deep Learning, such as:

Audio and Video Processing

Natural Language Processing

7. Good Understanding of Multi-Process Architecture

If you are developer, then you should know all the codes that are used in the process of deployment and release. The design engineer usually work on this task, but being a developer one must have a good understanding of this things. You should learn about Model View Template and Model View Controller Architecture if you want to become a Python developer because these are the main and important concepts. A good developer should be able to solve the problem that comes in the core framework.

8. Analytical Skills

if you want to be the best Python Developer in future, you must gain excellent analytical skills in the field of Data Science. The analytical skills can be gained through building useful websites for web development, by optimization of algorithms while coding, writing clean code etc.

9. Design Skills

Becoming a good developer means one should have the skills of a good design which are important while designing products. A python developer can make use of popular python frameworks such as Flask or Django while creating a website. From this, we can say Python is able to work at both server and client-side programming.

10. Communication Skills

Not only for the developer, but Communication skill is one of the most essential skills one should have in any profession.communication skill make an impact in every field and bring success to the profession and organization. Being a professional, one should avoid communication gap or confusion in understanding the concepts otherwise it can lead to the misunderstanding. Communications skill is very important while working with a team, you should express your thoughts and ideas to other team members in efficient way so that others can also understand them easily. Sometimes Communication is important while solving any problem regarding work.

11. Understanding of Front-End Technologies

A Python developer need to work and coordinate with both back end as well as front end team to match the client-side with the server-side. For this reason, the developer should have a good understanding of front-end technologies to get an idea about the appearance of the application, limitations, and working of the front end, and possibilities with which front end team can work.

12. Server-Side Templating Language

A python developer must have knowledge of some modeling languages at the server-side like Make and Jinja 2

13. Multiple Delivery Platforms Understanding

There are many platforms where a developer can deliver the output. It can be a desktop or mobile. Python developers should have good knowledge and experience about the different platforms. one must able to track and optimize the output according to the target platform and the requirements.

Here we have discussed all the important skillset to become a good Python developer. Other than this, you can also start taking some basic projects related to python so that you can increase your knowledge gradually. Also, you can learn these things from any good online Python Course.

NearLearn offers an Online python course with 50% discounts. Along with python, we provide online training for Machine Learning and AI, Blockchain, React JS, React Native, data Science, and many other courses. Offer valid for a limited time period only. For registration, Call: +91-80-41700110