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4-day working week in India?
The Modi government is considering a major change in the labour policy. The government is planning to introduce new rules to give flexibility to companies to have four working days instead of five or six. According to reports, the working hours limit of 48 hours for a week will not be changed.
If someone works for 8 hours a day, then there will be 6 working days per week. If a company opts for 12-hour working per day for its employees, it means four-day working and three holidays.
"If daily working hours are increased, you will have to give the workers similar holidays also. There will be 5 or 4 working days if duty hours are increased. It will now be mutually agreed to by the employees and employers as to what is appropriate for them," Labour Secretary Apurva Chandra said.
Chandra said that if a company offers a 4-days work week, the remaining three days should be paid offs. He said that the issue of flexibility in working days is being discussed.
Chandra said that the Ministry is currently engaged in formulating relevant rules and regulations to implement the four new Labour Codes in the next few weeks to usher in labour reforms. The new labour laws will not compromise the interests of labourers and employees in any way, he said.
Yogita Tulsiani, Director and Co-founder, iXceed Solutions, said that the 4 days work culture can only be implemented according to the functioning and working of different sectors.
"The 4 days work week concept has been in discussion for quite some time now. This debate has started off in Germany that already has the lowest working hours in a week at 34.2 hours. Several companies have experimented with it. But I suppose a 5-day work culture is more effective and contributes to better productivity at the workplace. Since the reduction in working days will reflect in the number of increased working hours, it will act as more burden for the employees. The increased timeline of work will hassle the employees stress level per day decreasing the work productivity.
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Data Science: A Good Career Choice
Data Science has become a revolutionary technology that everyone seems to talk about. Hailed as the ‘sexiest job of the 21st century’, Data Science is a buzzword with very few people knowing about the technology in its true sense. While many people wish to become Data Scientists, it is essential to weigh the pros and cons of data science and give out a real picture. In this article, we will discuss these points in detail and provide you with the necessary insights about Data Science.
Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option With the help of data science, providers now can decide the class of airplanes to buy, decide whether to reach the destination directly or halt in between, predict flight delay, design customer loyalty programs effectively etc.
Here are some of the leading data science careers-
-Data Scientist.
-Machine Learning Engineer.
-Machine Learning Scientist.
-Applications Architect.
-Enterprise Architect.
-Data Architect.
-Infrastructure Architect.
Why Python Is So Amazing?
Python is a really fascinating programming language. Developers can think that they have done everything they want, but then it offers more. Businesses now realize how much they would profit if they build their application in Python. Here’s what makes Python so amazing:-
Clear syntax:- Python has a clear and clean syntax which is easily readable. It allows even beginners to work with complex software development projects as the team can coordinate easily on the coding front. The simple coding syntax facilitates test-driven development for all applications of Python.
Scalable:- Companies love Python for its scalability. Some of the companies implementing the uses of Python language include Google, Spotify, Netflix, Instagram, and many more that want scalable applications. It allows handling a massive amount of traffic with ease.
Versatile:- Unlike most programming languages, the practical uses for Python are not limited to just web or mobile development. It is a popular choice for building web apps, gaming applications, enterprise-grade apps, e-commerce applications, ML and AI applications, and much more.
The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code -- not in reams of trivial code that bores the reader to death.
Why do you think Python has recently overtaken R in popularity among data scientists?
There are a couple of reasons I think Python has taken off. Python is a general purpose language, used by data scientists and developers, which makes it easy to collaborate across your organization through its simple syntax. People choose to use Python so that they can communicate with other people. The other reason is rooted in academic research and statistical models. I would say that R has better statistical packages than Python, but Python has deep learning, structured ways to do machine learning, and can deal with larger amounts of data. As people shift more to deep learning, the bias has been shifting toward Python.
Python is a truly wonderful language. When somebody comes up with a good idea it takes about 1 minute and five lines to program something that almost does what you want. Then it takes only an hour to extend the script to 300 lines, after which it still does almost what you want.
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Design Philosophy And Features Of Python
Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of its features support functional programming and aspect-oriented programming (including by metaprogramming and metaobjects (magic methods)). Many other paradigms are supported via extensions, including design by contract and logic programming.
Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. It also features dynamic name resolution (late binding), which binds method and variable names during program execution.
Python's design offers some support for functional programming in the Lisp tradition. It has filter, map and reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML.
The language's core philosophy is summarized in the document The Zen of Python (PEP 20), which includes aphorisms such as:
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Readability counts.
Rather than having all of its functionality built into its core, Python was designed to be highly extensible (with modules). This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and easily extensible interpreter stemmed from his frustrations with ABC, which espoused the opposite approach.
Python strives for a simpler, less-cluttered syntax and grammar while giving developers a choice in their coding methodology. In contrast to Perl's "there is more than one way to do it" motto, Python embraces a "there should be one— and preferably only one —obvious way to do it" design philosophy. Alex Martelli, a Fellow at the Python Software Foundation and Python book author, writes that "To describe something as 'clever' is not considered a compliment in the Python culture."
Python's developers strive to avoid premature optimization, and reject patches to non-critical parts of the CPython reference implementation that would offer marginal increases in speed at the cost of clarity. When speed is important, a Python programmer can move time-critical functions to extension modules written in languages such as C, or use PyPy, a just-in-time compiler. Cython is also available, which translates a Python script into C and makes direct C-level API calls into the Python interpreter.
An important goal of Python's developers is keeping it fun to use. This is reflected in the language's name—a tribute to the British comedy group Monty Python—and in occasionally playful approaches to tutorials and reference materials, such as examples that refer to spam and eggs (from a famous Monty Python sketch) instead of the standard foo and bar.
A common neologism in the Python community is pythonic, which can have a wide range of meanings related to program style. To say that code is pythonic is to say that it uses Python idioms well, that it is natural or shows fluency in the language, that it conforms with Python's minimalist philosophy and emphasis on readability. In contrast, code that is difficult to understand or reads like a rough transcription from another programming language is called unpythonic.
Users and admirers of Python, especially those considered knowledgeable or experienced, are often referred to as Pythonistas.
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Present Trends for Python-
Python is used by hundreds of thousands of programmers and is used in many places. Sometimes only Python code is used for a program, but most of the time it is used to do simple jobs while another programming language is used to do more complicated tasks.
Its standard library is made up of many functions that come with Python when it is installed. On the Internet there are many other libraries available that make it possible for the Python language to do more things. These libraries make it a powerful language; it can do many different things.
Some things that Python is often used for are:
Web development
Scientific programming
Data science
Machine learning
Numerical analysis
Statistics
Desktop GUIs applications
Network programming
Game programming.
Complex algorithms creation
Automation scripts
Machine learning and artificial intelligence
Audio and video applications
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