#linux for data science - with lots of python

[ranjitha78]

The Complete Manual for Understanding Ethical Hacking

In order to evaluate an organization's defenses, ethical hacking—also referred to as penetration testing or white-hat hacking—involves breaking into computers and other devices lawfully. You've come to the correct spot if you're interested in finding out more about ethical hacking. Here's a quick start tutorial to get you going.

1. "Getting Started with the Basics"

Networking and computer science principles must be thoroughly understood before getting into ethical hacking. Here are some crucial aspects to pay attention to: Operating Systems: Acquire knowledge of several operating systems, with a focus on Linux and Windows. Learning Linux is essential because a lot of hacking tools are made to run on it. Networking: It's essential to comprehend how networks operate. Find out more about

protocols include HTTP, HTTPS, DNS, TCP/IP, and others. Understanding data flow across networks facilitates vulnerability detection. Programming: It's crucial to know at least a little bit of a language like Python, JavaScript, or C++. Writing scripts and deciphering the code of pre-existing tools are made possible by having programming expertise.

2. Making Use of Internet Resources To learn more about ethical hacking, there are a ton of internet resources available. Here are a few of the top ones: Online Education: Online learning environments such as Pluralsight, Coursera, and Udemy provide in-depth instruction in ethical hacking. "Penetration Testing and Ethical Hacking" on Pluralsight and "The Complete Ethical Hacking Course: Beginner to Advanced" on Udemy are two recommended courses. Channels on YouTube: HackerSploit, The Cyber Mentor, and LiveOverflow are just a few of the channels that offer helpful tutorials and walkthroughs on a variety of hacking tactics.

3. Exercising and Acquiring Knowledge The secret to being a skilled ethical hacker is experience. Here are some strategies to obtain practical experience:

Capture the Flag (CTF) Tournaments: Applying your abilities in CTF tournaments is a great idea. CTF challenges are available on websites like CTFtime and OverTheWire, with difficulty levels ranging from novice to expert. Virtual Labs: It is essential to set up your virtual lab environment. You can construct isolated environments to practice hacking without worrying about the law thanks to programs like VMware and VirtualBox. Bug Bounty Programs: Websites such as HackerOne and Bugcrowd link corporations seeking to find and address security holes in their systems with ethical hackers. Engaging in these initiatives can yield practical experience and financial benefits.

Dedication and ongoing education are necessary to learn ethical hacking. You can become a skilled ethical hacker by learning the fundamentals, using internet resources, and acquiring real-world experience. Always remember to hack wisely and ethically. Cheers to your hacking! I appreciate your precious time, and I hope you have an amazing day.

[tccicomputercoaching]

Introduction to C Programming

What is C?

The C language is a high-level language developed by Dennis M. Ritchie. It was implemented in 1972, but the first publicly available description of C was developed and produced by Brian Kernighan and Dennis Ritchie in 1978.

Let us see some key points:

Firstly, the B language was introduced. So, C is a successor of B.

The popular UNIX was written in C.

C Language has been used to develop operating systems, databases, editors, etc.

C language code is generally written in a text file with the extension .c

Why Learn C?

The C Language is still preferred since it is simple and reliable to use. A lot of discussions and comparisons have been made about C, C++, and Java.

Let us learn why C should be learned before learning C++ and Java:

Always start with the basics. Learn the basic programming concepts in C before switching to C++ and Java concepts like classes, objects, abstract classes, exception handling, inheritance, etc. Basics will make your learning simpler for other languages.

In recent years, the concept of Java and C++ has definitely been boosted. But still, C is being used since the core C language elements are still required.

Some operating systems, tools, and software are still written in C. The device drivers, which help in solving your software issues with hardware, are still written in C for interaction with hardware. It is still the first choice because the execution is fast and hassle-free.

C Features

Structured Language: C is a structured language since it can be easily split into multiple blocks of execution, which are known as functions. This makes the code easier to understand.

Execution Speed: The efficient and fast execution of C programs still makes it the number one choice of programmers. The compilation and execution of a C program are quite fast.

Built-in functions: C language built-in functions help in writing complex programs with ease.

Code Reusability: Code reusability eliminates the need to write the same code again and again and leads to fewer lines of code and faster execution. Recursion occurs when a function calls itself, reducing the need to write code again and again.

Extend: The C language extends itself and takes up new features.

C Application

The following are the real-life applications of C language:

Many Python libraries for Data Science and Machine Learning are written in C, such as, NumPy.

The Operating Systems, Windows and Mac, are written in C and C++.

Linux, developed by Linus Torvalds, is written in C.

Visual Studio Code, developed by Microsoft, is written in not only C# but also C.

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[idiosys1]

Python For Web Application Development

Web development is a dynamic and competitive field that requires choosing the right programming language for your project. Among the many options available, Python stands out as one of the best and most popular languages for web development. Python is a powerful, versatile, and easy-to-use language that can help you create amazing web applications. In this blog post, we will explore the reasons why Python is the ultimate choice for web development.

Why Choose Python for Web Application Development?

Python has many features and advantages that make it ideal for web development. Here are some of the main reasons why you should choose Python for your web development project:

Simple and readable: Python has a clear and concise syntax that makes it easy to read and write code. Python uses indentation to structure code blocks, which improves readability and reduces errors. Python also has a rich set of built-in data types and functions that simplify coding tasks. Python code is like a story that you can easily follow and understand.

Powerful frameworks: Python has a wide range of frameworks that can help you with web development. Frameworks are collections of libraries and tools that provide common functionalities and features for web applications. Python has frameworks for different purposes and needs, such as Django, Flask, Pyramid, and more. Django is a high-level framework that offers a complete package of features, such as ORM, admin interface, and security. Flask is a microframework that gives you more flexibility and control over your web application. You can choose the framework that suits your project best and save time and effort.

Fast and productive: Python is a high-level language that allows you to write less code and do more. Python has features such as multiple assignment, list comprehension, and lambda functions that enable you to write concise and expressive code. Python also has a large number of third-party libraries that you can use to add functionality and features to your web application. For example, you can use requests to handle HTTP requests, Beautiful Soup to parse HTML, or NumPy to perform numerical computations. Python helps you to develop web applications faster and more efficiently.

Scalable and reliable: Python is a language that can handle large and complex web applications. Python has a modular and object-oriented design that makes it easy to organize and reuse code. Python also has frameworks that support scalability, such as Django, which allows you to use features such as database sharding, caching, and load balancing. Python is a language that can grow with your web application and ensure its performance and reliability.

Supportive community: Python has a large and active community of developers and users who support and contribute to the language. Python has a wealth of resources, such as documentation, tutorials, books, courses, blogs, podcasts, and more. Python also has a lot of events, such as conferences, meetups, hackathons, and workshops, where you can learn, network, and collaborate with other Python enthusiasts. Python has a community that can help you with your web development project and keep you updated with the latest trends and developments.

Cross-platform and compatible: Python is a language that can run on different platforms and operating systems, such as Windows, Linux, Mac, and more. Python also has tools that can help you to create web applications that are compatible with different browsers and devices, such as web2py, Pyjs, and Kivy. Python is a language that can reach a wide and diverse audience and ensure a consistent and smooth user experience.

Versatile and innovative: Python is a language that can do more than web development. Python can also be used for other domains and applications, such as data science, machine learning, automation, scripting, and more. Python has libraries and frameworks that can help you with these tasks, such as pandas, scikit-learn, TensorFlow, Selenium, and more. Python is a language that can help you to explore new possibilities and create innovative solutions.

Python is a language that can help you to create amazing web applications. Python is simple, powerful, fast, scalable, supportive, compatible, and versatile. Python has everything you need for web development and more. Choosing Python is not just a decision; it’s a smart and rewarding choice that can help you to achieve your web development goals.

If you want to create a web application using Python, you need the help of a professional web development company. We, Idiosys Technologies is a leading web development company in Kolkata, We can help you to design, develop, and deploy a web application that meets your requirements and expectations.

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[amitsaini012]

Top 10 Reasons Why Everyone Should Learn Python Programming

In the ever-evolving world of technology, coding has become an indispensable skill, and among the various programming languages, Python has emerged as a powerhouse. This versatile language has captured the attention of professionals and enthusiasts alike due to its simplicity, versatility, and widespread adoption. Whether you're a student, a professional, or someone looking to embark on a new career path, learning Python can open up a world of growth. In this blog post, we'll explore the top 10 reasons why everyone should consider learning Python programming, from its user-friendly syntax to its vast array of applications.

Note: If you are struggling with python homework, then you can get the best python homework help from our experts. 

1. Simplicity and Readability

Python is known for its clean and straightforward syntax, which makes it incredibly easy to learn, especially for beginners. Its code is designed to be human-readable, making it easier to understand and maintain, even for those with no prior programming experience.

2. Versatility

Python is a general-purpose computer language that can be used for many things, such as building websites, analyzing data, automating tasks, machine learning, and more. Because it is so flexible, writers can use it for a wide range of projects. This makes it an important tool for any coder.

3. Vast Libraries and Frameworks

Python has a huge library of tools and frameworks that can be used for a lot of different tasks, from working with data and showing it in different ways to building websites and doing science computing. With so many tools and frameworks available, it's easier to make complicated apps without having to start from scratch.

4. Data Analysis and Scientific Computing

Because it has strong tools like NumPy, Pandas, and SciPy, Python has become a popular language for science computing and data analysis. These packages make it easy to work with, analyze, and display data, which makes Python a useful tool for researchers, data scientists, and analysts.

5. Web Development

Python is very flexible, and it can also be used for web creation. Frameworks like Django and Flask make it easy for developers to make web apps that are strong and scalable. These frameworks support both server-side and client-side code.

6. Automation and Scripting

Python is great for programming and coding because it is easy to read and understand. Python scripts can greatly increase productivity and efficiency by handling chores that are done over and over again or by managing the system.

7. Machine Learning and Artificial Intelligence

Tools like TensorFlow, Scikit-learn, and PyTorch have made Python a popular language for projects that use AI and machine learning. AI is a field that is growing quickly and can benefit from Python because it has libraries that let you make and use advanced models.

8. Community and Support

A lot of coders work on Python and are actively contributing to its growth and progress. This group has a lot of literature, boards, and other tools that make it easier for new people to learn and solve their problems.

9. Cross-Platform Compatibility

Code written in Python can run on different operating systems, like Windows, macOS, and Linux, without needing major changes because Python is a cross-platform language. Python is a good choice for writers who work in a variety of settings because it can be used in those settings.

10. Career Opportunities

There is a growing need for Python writers in many fields, so learning Python can lead to many job possibilities. Companies in many fields are constantly looking for people who know Python. These fields include banking, healthcare, technology, and more.

Conclusion 

In conclusion, the reasons why everyone should learn Python programming are numerous and compelling. From its simplicity and readability to its versatility and vast applications, Python offers a wealth of advantages for developers of all levels. Whether you're a beginner looking to enter the world of programming or an experienced professional seeking to expand your skillset, Python is an excellent choice.

Its extensive libraries and frameworks enable efficient development across various domains, including web development, data analysis, machine learning, and automation. Additionally, Python's vibrant community and cross-platform compatibility make it a versatile and accessible language for developers worldwide.

The need for skilled Python coders is only going to grow as technology keeps getting better. Not only will learning Python give you a useful skill, but it will also help you get great job chances in many different fields.

[esytes-encyclopedia]

Top 5 Programming Languages in 2023

Here are the top 5 programming languages that every programmer should know in 2023: - Python- Python is a general-purpose programming language that is known for its simplicity, readability, and versatility. It is one of the most popular programming languages in the world. Advantages: Web development: Python is a popular choice for web development, as it is easy to learn and use, and there are a number of popular frameworks available, such as Django and Flask. Data science: Python is a powerful language for data science, as it has a number of libraries and tools that make it easy to work with data, such as NumPy, Pandas, and Scikit-Learn. Machine learning: Python is also a popular language for machine learning, as it has a number of libraries and tools that make it easy to build and train machine learning models, such as  Tensorflow and Keras. Software development: Python is a versatile language that can be used for a wide variety of software development tasks, such as building web applications, desktop applications, and command-line tools. Easy to learn and use: Python has a simple syntax that is easy to read and understand, even for beginners. This makes it a great language for people who are new to programming. Versatile: Python can be used for a wide variety of tasks, from web development to data science to machine learning. This makes it a valuable skill to have in the tech industry. Free and open source: Python is a free and open source language, which means that it is available to everyone to use and modify. This makes it a great choice for projects where you need to be able to share your code with others. Large and active community: Python has a large and active community of developers who are constantly creating new libraries and tools. This means that there is always help available if you get stuck. Portable: Python code can be run on a variety of platforms, including Windows, macOS, and Linux. This makes it a great choice for projects that need to be portable. Overall, Python is a powerful and versatile language that is easy to learn and use. It is a great choice for a variety of projects, and has a large and active community of developers who are constantly creating new libraries and tools. Disadvantages: Slow speed: Python is not as fast as some other programming languages, such as C++ or Java. This can be a problem for some applications that require high performance. Heavy memory usage: Python can use a lot of memory, which can be a problem for applications that are memory-intensive. Lack of support for mobile environments: Python does not have as much support for mobile environments as some other programming languages. This can be a problem for applications that need to be run on mobile devices. Dynamic typing: Python is a dynamically typed language, which means that the type of a variable is not known until runtime. This can make it difficult to track down errors in Python code. Not as good for GUI development: Python is not as good for GUI development as some other programming languages, such as Java or C#. This is because Python does not have a built-in GUI toolkit. Overall, Python is a powerful and versatile language with a number of advantages. However, it is important to be aware of the disadvantages of Python before choosing it for a project.   - JavaScript- JavaScript is the most popular programming language on the web. It is used to create interactive web pages and applications. JavaScript is also a versatile language that can be used for a variety of other tasks, such as game development and data visualization. Advantages: Easy to learn: JavaScript has a simple syntax that is easy to read and understand, even for beginners. This makes it a great language for people who are new to programming. Client-side execution: JavaScript is executed on the client side, which means that it does not need to communicate with the server every time a user interacts with a web page. This makes JavaScript applications more responsive and faster. Versatility: JavaScript can be used for a wide variety of tasks, including: Adding interactivity to web pages: JavaScript can be used to add interactive elements to web pages, such as dropdown menus, accordions, and sliders. Building web applications: JavaScript can be used to build full-stack web applications, including both the front-end and back-end code. Creating mobile apps: JavaScript can be used to create mobile apps using frameworks like React Native and Ionic. Large community: JavaScript has a large and active community of developers who are constantly creating new libraries and tools. This means that there is always help available if you get stuck. Cross-platform compatibility: JavaScript code can be run on a variety of platforms, including Windows, macOS, and Linux. This makes it a great choice for projects that need to be portable. Overall, JavaScript is a powerful and versatile language that is easy to learn and use. It is a great choice for a variety of projects, and has a large and active community of developers who are constantly creating new libraries and tools. Disadvantages: Security: JavaScript can be used to create malicious code, which can be used to steal user data or damage a web page. This is because JavaScript code is executed on the client side, which means that it can be accessed by anyone who visits a web page. Performance: JavaScript is not as fast as some other programming languages, such as C or C++. This can be a problem for some applications that require high performance. Complexity: JavaScript can be a complex language, especially when you start using frameworks and libraries. This can make it difficult to learn and use. Browser compatibility: JavaScript is not always compatible with all browsers. This can be a problem if you want your JavaScript code to work on all browsers. Error handling: JavaScript does not have a built-in error handling system. This can make it difficult to track down and fix errors in JavaScript code. Overall, JavaScript is a powerful and versatile language with a number of advantages. However, it is important to be aware of the disadvantages of JavaScript before choosing it for a project.   - Java- Java is a general-purpose language that is known for its reliability and performance. It is popular for enterprise development, mobile development, and large-scale applications. Java has a large and active community of developers, and there are many resources available to help you learn the language. Advantages: Object-oriented: Java is an object-oriented programming language, which means that it is designed to be easy to manage complex code. Platform-independent: Java code can be run on any platform that has a Java Virtual Machine (JVM). This makes Java a great choice for developing cross-platform applications. Secure: Java is a secure language, as it has built-in security features such as bytecode verification and sandboxing. Robust: Java is a robust language, as it is designed to handle errors gracefully. Extensible: Java is an extensible language, as it allows you to add new features and functionality. Portable: Java code can be run on a variety of platforms, including Windows, macOS, and Linux. Fast: Java is a fast language, as it is compiled to bytecode that can be executed by the JVM Popular: Java is a popular language, as it is used by many large companies and organisations. Well-documented: Java is a well-documented language, which means that there are many resources available to help you learn and use Java. Overall, Java is a powerful and versatile language with a number of advantages. It is a great choice for a variety of projects, and has a large and active community of developers who are constantly creating new libraries and tools. Disadvantages:  Complexity: Java can be a complex language, especially when you start using frameworks and libraries. This can make it difficult to learn and use. Performance: Java is not as fast as some other programming languages, such as C or C++. This can be a problem for some applications that require high performance. Memory usage: Java can use a lot of memory, which can be a problem for applications that are memory-intensive. Garbage collection: Java uses automatic garbage collection, which can sometimes be inefficient. This can lead to performance problems in some cases. Lack of low-level control: Java does not give you as much control over the operating system as some other languages, such as C or C++. This can be a problem for some applications that require low-level access. Not as good for embedded systems: Java is not as good for embedded systems as some other programming languages, such as C or C++. This is because Java is not as efficient in terms of memory and CPU usage. Not as good for real-time applications: Java is not as good for real-time applications as some other programming languages, such as C or C++. This is because Java is not as deterministic in terms of its execution time. Overall, Java is a powerful and versatile language with a number of advantages. However, it is important to be aware of the disadvantages of Java before choosing it for a project.   - C/C++: C/C++ are powerful languages that are used for low-level programming. They are popular for system programming, embedded systems, and game development. C/C++ are difficult languages to learn, but they offer a high degree of control over the hardware. Advantages: Performance: C/C++ are very fast languages, as they are compiled to machine code. This makes them ideal for applications that require high performance, such as games or real-time systems. Control: C/C++ give you a lot of control over the operating system and hardware. This is useful for applications that need to access low-level features, such as embedded systems or device drivers. Portability: C/C++ code can be ported to different platforms with relatively few changes. This makes them a good choice for developing cross-platform applications. Efficiency: C/C++ code is typically very efficient in terms of memory usage and CPU usage. This makes them a good choice for applications that need to be as efficient as possible. Popularity: C/C++ are very popular languages, and there are many resources available to help you learn and use them. Maturity: C/C++ have been around for many years, and they are very well-developed languages. This means that there are many libraries and tools available for C/C++, and they are well-supported by the development community. Customization: C/C++ allow you to customise the language to your specific needs. This can be useful for applications that require special features or functionality. Security: C/C++ can be used to write secure applications. This is because C/C++ gives you a lot of control over the memory management and security features of your application. Flexibility: C/C++ are very flexible languages. This means that they can be used to write a wide variety of applications, from simple command-line tools to complex operating systems. Overall, C/C++ are powerful and versatile languages with a number of advantages. They are a good choice for a variety of projects, and they are well-suited for applications that require high performance, control, or portability. Disadvantages: Complexity: C/C++ can be a complex language, especially for beginners. This can make it difficult to learn and use. Error-prone: C/C++ code can be error-prone, as it is easy to make mistakes that can lead to crashes or security vulnerabilities. Memory management: C/C++ requires manual memory management, which can be a source of errors. Not as good for web development: C/C++ are not as good for web development as some other languages, such as JavaScript or PHP. This is because C/C++ are not as well-suited for dynamic web pages. Lack of built-in features: C/C++ does not have as many built-in features as some other languages, such as Java or Python. This means that you may need to write your own code to implement certain features. Not as portable as some other languages: C/C++ code can be more difficult to port to different platforms than code written in some other languages. Overall, C/C++ are powerful and versatile languages with a number of advantages. However, it is important to be aware of the disadvantages of C/C++ before choosing them for a project.   - C#: C# is a general-purpose language that is similar to Java. It is popular for web development, desktop applications, and mobile development. C# is a relatively easy language to learn, and it has a large and active community of developers. Advantages: Object-oriented: C# is an object-oriented programming language, which means that it is designed to be easy to manage complex code. Platform-independent: C# code can be run on any platform that has the .NET Framework installed. This makes C# a great choice for developing cross-platform applications. Scure: C# is a secure language, as it has built-in security features such as code access security and memory safety. Robust: C# is a robust language, as it is designed to handle errors gracefully. Extensible: C# is an extensible language, as it allows you to add new features and functionality. Portable: C# code can be run on a variety of platforms, including Windows, macOS, and Linux. Fast: C# code can be compiled to native machine code, which makes it fast. Popular: C# is a popular language, as it is used by many large companies and organisations. Well-documented: C# is a well-documented language, which means that there are many resources available to help you learn and use C#. Memory management: C# uses automatic garbage collection, which means that you do not need to worry about managing memory manually. This can make C# code easier to write and maintain. Multithreading: C# supports multithreading, which means that you can run multiple tasks at the same time. This can make C# code more efficient and responsive. Strong typing: C# is a strongly typed language, which means that the type of a variable is known at compile time. This can help to prevent errors and make C# code more reliable. Overall, C# is a powerful and versatile language with a number of advantages. It is a good choice for a variety of projects, and has a large and active community of developers who are constantly creating new libraries and tools. Disadvantages: Complexity: C# can be a complex language, especially when you start using frameworks and libraries. This can make it difficult to learn and use. Performance: C# is not as fast as some other programming languages, such as C or C++. This can be a problem for some applications that require high performance. Memory usage: C# can use a lot of memory, which can be a problem for applications that are memory-intensive. Garbage collection: C# uses automatic garbage collection, which can sometimes be inefficient. This can lead to performance problems in some cases. Lack of low-level control: C# does not give you as much control over the operating system as some other languages, such as C or C++. This can be a problem for some applications that require low-level access. Not as good for embedded systems: C# is not as good for embedded systems as some other programming languages, such as C or C++. This is because C# is not as efficient in terms of memory and CPU usage. Not as good for real-time applications: C# is not as good for real-time applications as some other programming languages, such as C or C++. This is because C# is not as deterministic in terms of its execution time. Overall, C# is a powerful and versatile language with a number of advantages. However, it is important to be aware of the disadvantages of C# before choosing it for a project. Read the full article

[catbirdlinux]

Clear your mind and great content will come in...

[alltruedreamersdied]

Important tips if you want to study Astronomy / Astrophysics, from a Grad Student

dont be afraid to ask questions: even if it is after class or per mail, always ask if you dont understand something, or if you think there might be a connection somewhere!

learn working in groups: science is not a 'everyone for themself' kind of thing, you will need to learn how to work in groups and work anf study with other people. trust me, you wont make undergrad anyways if you only work alone

learn python: programming is a MUST in astrophysics, and python is one of the most used (free) languages out there! if you have the funds, IDL is also good, but pricey

learn LaTeX: speaking of programming, you should also learn LaTeX. technically it is not programming, but it feels like it. basically you programm what your MS word document would look like. used for writing papers and thesis, and usually lab reports!

if you can, install Linux: find a pc you can install linux on or try a virtual machine. there are A LOT of important programs that only work on linux, and you will have to use them. learn how to use the linux terminal and the commands! linux is the most widely used system in STEM, it is important to know your way around that, even if you personally prefere something else

dont waste your money on text books: there are usually the most basic fundations that sum up everything in one book, which will be there for you for the rest of your journey, but in general, try to get those in libraries. after that, read scientific papers (all free here: https://arxiv.org/ ) where you can find review papers that talk about general topics!

maths different: waste your money on GOOD maths books, they will always be there for you

maths is THE language: speaking of maths, if you are not interested (i dont mean if you are good or bad, i am talking about interest) in maths, dont study astronomy. seriously! maths is the language you will use to describe the universe! i was not the best at maths in school, but i was always interested by it. that is what is important. you dont need to immediately get it all, but you need to have the ability to invest so much time in it, that you will understand it at one point (going back to a previous point, here it is again important to work and study in groups. you may not get linear algebra, but your friend might)

be open minded, but critical: just because something is written in a published paper does not mean it is 100% accurate. new results can lead to theories to be disproven. discuss and make up your mind with your knowledge, and learn to be critical of new findings, in an educated way, not based on what you think is 'true', but through assassing the method and interpreting the data yourself.

learn english: if (like me) your mother language is not english, learn it! learn mathematical and scientific terms, read literature in english etc. it is the language everything is in, and as astronomy is an international field, it will be the language you will communicate in.

[winsple]

HOW TO DEVELOP A MACHINE LEARNING CAREER

Every year these is a laser sharp increase in demand for professionals with skills in machine learning. Jobs related to machine learning are growing in a faster pace a lot than ever. Machine learning is part of artificial intelligence which automates analytical model building and data analysis. In simple terms machine learning is defined as the capability of a machine to imitate human behavior. One of the best example of machine learning is image recognition. Machine learning is the booming career option which is shaping the job market. You’ll apply machine learning and/or data science techniques sort of modelling and relevance problem involving users, their relationship, their tweets, and their interests. Though the work relies on cutting edge problems it is not a research position. Some prerequisites for a machine learning career are: ·     Machine learning basics ·     Hands on experience with projects ·     Linear algebra ·     Coding ·     Data structure and calculus A machine learning engineer automates ML processes and make models work in a production environment. ML engineer uses tools like python, pytorch, TensorFlow and cloud services. To develop a career, one must possess good problem-solving skills, data science skills, proficiency in deep learning frameworks, knowledge of math and solid programming skills. The main responsibility of a MI engineer is to write programs and develop algorithms to extract meaningful information from large amounts of data provided. It is essential to be familiar with machine learning algorithms, knowledge of ML programming languages such as python and C++ To become a ML engineer, one must own  bachelor’s degree in business informatics, CS, math, or related engineering sciences. According to data a junior ML engineer will be payed approximately $75,000 and senior ML engineer around $153,000. 5 steps to become a successful machine learning professional. ·     Apply and test your learning ·     Learn algorithms ·     Good online courses ·     Refer good books ·     Understanding the fundamentals. To develop a lifetime career out of it one must have excellent interpersonal and collaboration skills, demonstrated ability to quickly learn and modify large, existing code bases. There should be deep learning of ASR, NLP, ROBOTICS, computer vision, multimodal fusion etc. Experience in operating Linux based system is as important as knowledge of core CS concept. Mid-level ML engineer is an expert in deep learning architectures, excellent python and SQL programming skills, PhD in computer science, electrical engineering, statistics, and mathematics. To excel in such a high value booming course consistency and good online courses are crucial.

We at winsple provide AI and machine learning courses. We provide lifetime access to class recordings and 24/7 assistance. Certified machine learning master course helps you out with all the details of the recent tools and technologies that can be used in machine learning. Start learning through our innovative learning sessions and validate your machine learning skills and broaden up your career choices!!

[eightlivs]

24 August 2020

I’m getting the hang of that CAD program. Fucking command line bullshit. Give me macros instead and I’ll be happy.

Anyway, now that I can actually do some PROPER work, we’re getting busy checking and re-checking things before we order or machine the more customized parts, which means a lot of simulation testing and triple-checking our work.

That’s not the most important thing right now, though.

I just finished something that I’ve been working on for a while, and right as my beam time request got put through at Brookhaven, no less. (I put in a beam time request for another collider to run an experiment for me. Ended up not going with SLAC. Turns out I had some strings left to pull at Brookhaven’s laboratory, and hey, all the better, because their collider is better suited for this kind of work.)

Oh, it feels good to be running experiments again. Even if they are a pain in the ass sometimes.

Anyway, a quick rundown, since this IS technically a report log: I’m simulating my OWN collider with a computer program called an event generator, while comparing the simulations to real results. Check this out:

That’s a graph showing how many photons were found at a given energy for the first simulation.

But you know what I’m realizing? I’m going to need a monumental amount of testing to get anywhere, and - well, I’ve got to do it all by myself.

This is... um. This is going to take a long time. Last time, setting up and analyzing runs enough to get new information wasn’t even something we’d gotten to yet, and that’s with an entire COMPANY at my disposal. Unless I’m ridiculously lucky, this may take years even AFTER THE COLLIDER IS BUILT.

I don’t want that. Fuck. Fuck. I’m tired of this. It’s one fucking thing after another, isn’t it. Windows and Linux workarounds, C++, MC formats not working, more MC formats not working, more workarounds, python not working, compilers not working, incorrect variables, run after run after run of incorrect programming, fucking VERTICAL DATA instead of HORIZONTAL like FORMAT YOUR SHIT RIGHT PLEASE, and collider bullshit, and collider setbacks and - I JUST - FUCK.

...okay.

Okay. Hmm.

It’s fine, all right? I’ve just been programming for too long. My eyeballs feel like they’re going to roll out of my skull. The amount of work that it takes just to program, analyze, troubleshoot, and reprogram a single event generator run is not small, okay? We’re talking hours or days PER RUN. And, I mean, it kind of sucks that I can’t tell anybody about this - they’d think I was bonkers. It makes you feel kind of weird, knowing you’ve only known anybody on the whole planet for a period of less than two years.

But, you know, back to the grind. I’ve got more work to do. Catch you all later.

Oh, before I sign off, I’m updating my experiment record - it’s that little link at the right, under the OFFLINE status. The one I just finished working on is tests 12A-D - yes, 12, it’s taken me twelve groups of runs to get this fucking thing right.

I’m going home.

Footnotes: Oh wow. Okay. It may not look like it, but I’ve been working on this since APRIL. That graph up there? That’s not something I found or made up. It’s an actual graph of results from a real simulation run with an actual collision simulator.

One of my fave things about this blog is that it’s based on REAL science, and it’s challenged me to learn new things. I knew Liv was going to have to start doing collision experiments at some point, and I naively ignored the fact that she’d be getting, you know, RESULTS - until I had the idea to simulate them myself. So I installed the program over the course of a week or two - it’s not very user-friendly in a non-programmer type of way - and I’ve been experimenting with it on and off since April. I ran Liv’s final test results last night, which is what you see up there. One thing: I cannot claim any of these results are actually sensible; I am, after all, literally self-taught, and I am not actually a physicist!

Pretty cool, huh. The program I’m using is called PYTHIA - it’s what is called a Monte Carlo event generator. Each particle interaction result is called an “event,” and Monte Carlo refers to a mathematical method of using algorithms and random sampling to get results. It’s really useful in this instance because there’s a level of probability-guessing that has to be done when figuring out what a particle - or particles - will do. Particles are very all-over-the-place little things, so randomness helps simulate that.

Brookhaven National Laboratory is a real thing! They’re based in NY and they have a collider called the Relativistic Heavy Ion Collider, or RHIC. RHIC results in some really GORGEOUS photos, like the one here.

[system76]

Archiving Satellite Imagery: A Chat About the Lemur Pro with NSIDC

The National Snow and Ice Data Center at CU Boulder is one of 12 data centers across the country in charge of archiving NASA’s satellite imagery. This week, we had a nice chat with Chris Torrence (Software Development Manager) and Matt Fisher (Software Developer) on their work and their experience with the System76 Lemur Pro.

Tell us about what goes on at the NSIDC.

Chris: NSIDC is part of an organization called CIRES, which stands for the Cooperative Institute for Research in Environmental Sciences. Our team is kind of a suborganization within that, which is a software development group made up of 14 people. Most of our funding comes from NASA. Our primary mission is to archive all of NASA’s satellite imagery for the Arctic and the Antarctic.

Matt: Part of our mandate is we don’t just have to store the data, we have to make it available openly and freely to our users. Some of our data is a little more restrictive because it’s provisional and we’re still working on it, but it’s important that we service anybody who wants to use our data. So to do that, we wrote a JavaScript application that allows you to go to our website and select exactly what data you’re interested in—without having to write code to filter out the rest.

What kinds of users do you generally see on the site?

Matt: Our User Services office deals with a wide variety of use cases. Even developers like us at NSIDC will go to our User Services office and ask them questions for help using our own data. So they’re servicing developers, scientists, students, teachers, politicians, newspapers—we make products for newspapers and the military as well.

What sort of software does your work entail?

Chris: There’s two main aspects of what we do: Data storage and data distribution. Our web development team builds web applications so that scientists and the general public can come to our website to browse, download, and analyze our data. A lot of our applications are a combination of a front end, which would typically be written in JavaScript, and a back end written in Python which ties the front end into our database. We do also have some legacy code which we have to maintain as well, but those are the two main languages we use. We house most of our data on CU’s campus, though now we’re starting to move some of our data up into the cloud. That’s our next big project. 

Matt: To add to that, we’re also building tools for generating visualizations on our website. ASINA (Arctic Sea Ice News and Analysis) provides recent news on what’s happening in the Arctic and the Antarctic. Then there’s IceBridge, which shows flights of aircraft that have flown over the Arctic and taken photographs and measurements of the ice. You can scroll through all the data that’s available, choose what you want, zoom into areas, look at the thumbnails of the images, and then download the data.

Matt: I’m also working on a project called QGreenland. We’re building a data package for commercial off-the-shelf QGIS, which is an open source tool for visualizing geographic data. The data package is focused on the geographic region around Greenland, so people who are going out there to do field work can take along this pre-downloaded data package that covers all kinds of disciplines, including atmospheric data, oceanographic data, human activity data, human health data, and animal migration data. 

Matt: Another data package we built contains different categories of data focusing on Greenland. It shows scientific data such as vegetation biomass, ice streams, glacial termini positions, ice sheet velocity, bathymetric data, and locations of bird colonies. It’s aimed at all kinds of scientists, as opposed to our other work which is focused on cryospheric science.

How did you first hear about System76?

Matt: We had just started buying Linux laptops when I first started at NSIDC. I’ve always been a Linux user, and there were other Linux users here who had been asking for this for a while. I was part of the first test group that did that. We had laptops from another vendor at first that we were having trouble with, and we felt that we needed somebody to provide us laptops that had Linux expertise. That was one of the primary reasons we looked at System76. It also helped that you were local to us and that your systems had Ethernet ports for fast Internet access.

What led you to decide on the Lemur Pro?

Matt: Battery life was one of the largest reasons we chose the Lemur Pro. It had a very modern CPU in it, and we liked that they had a very high memory limit that you could configure it with. I personally appreciate the repairability of this style of laptop. Being able to just remove the bottom plate and replace the RAM is a great thing to be able to do without having to send it in or have a technician show up on-site.

Chris: We had some cases where people were installing Linux on a generic laptop, which was taking a fair amount of effort for them to maintain and keep up to date. That was another benefit of having Ubuntu come pre-installed, so you’re 75 percent of the way to a system that’s ready to go instead of starting from 0.

What distro are you using?

Matt: A few of us are on Ubuntu 20.04. One of us got their laptop a little earlier with Pop!_OS 18.04 installed. He’s since upgraded, and as far as I can tell he really likes it. At this point my experience with Ubuntu 20.04 has me wishing I went with Pop!_OS 20.04 as well because of snaps. I don’t really like snaps, so I had to go through a good amount of effort to disable them and block them from my system.

How has System76 improved your workplace? Contact myriah@system76.com for more information on how to get your company featured in our next case study!

[suyashgorde375-blog]

Python

Introduction

The information technology industry is daily upgraded that’s ever-changing. And so that’s why you hear about new technologies and programming languages. In present-day, more and more websites are being created with the use of Python programming language. It’s designed by Guido Van Rossum, and initially was released in 1990. Let’s find the reason for, why Python is the top programming language in 2020.

Why Python?

Python is one of the top programming languages in the world. As compare to other languages it is so easy to learn and with any of the high end Frameworks like Machine learning, big data, Analytics. Python is Open source language (cost saving). Every new development concepts comes early in Python like Raspberry Pi, Hadoop interface and etc. This language runs on platforms such as Windows, UNIX, and Linux etc. There are three main reasons to behind your question, “why specifically learn Python language”.

·         Learnability

·         Increased Productivity

·         High Paying salaries

What Python can do?

Python can be used on a server to create Web application and to handle big data and perform complex mathematics. It can connect to the database system, also read and modify files. This language is used for production-ready software development.

What is Python:  Python is High-level programming language, designed easy to read and simple to implement. It is free to use and open source language.  Some of the popular applications and simple that have employed during Python development include such as YouTube, Google App engine, Eve online.

Features of Python:                                                                                            

Easy: When say its “easy” that means it in different contexts.

Easy to Code and Easy to Read:

As compared to other programming languages like Java, C++, C, .Net, Python is easy to code. It’s a programmer friendly. Python is high-level programming language. Its code quite like English dynamically typed and aids readability.

Expressive:  Python provides us with a lot of constructs that help us focus on the solution rather than on the syntax. So it is one of best feature about the python why should you learn Python language.

Free and Open-source language: it is freely available, so you can easily download it from Python website. Python is open-source; its source code is available in public. So that’s why you can use it, download it, and distribute it.

High-level: it is high-level programming language. It means programmer doesn’t need to remember the system architecture and also not need to manage the memory. It is more programmers friendly.

Portable: You can take one code and run it on any machine or platform; don’t required to make changes to it for the same, not necessary to write different code for different machine.

  Interpreted: if you know about the other languages like C, C++, Java; first you can compile the code and then run it. But in Python, there is not necessary to compile it. It’s internally working as, source code is converted into an immediate form that is called as Bytecode.

Object-Oriented: Python language supports to both Procedure-oriented and Object-oriented programming. This language focuses on objects and its combination of data and functions. Also supports to multiple inheritance, unlike Java. Class is a nothing but blueprint for such an object.

Extensible: This language is an extensible; it means that can be extended to other languages.

Embeddable:  We can put code in other language in our Python source code. It is also feasible to put our Python code in a source code in some different languages like C++.

Applications of Python:

Web Development: Python is best idea for rapid development of web applications. It is allows to you build web apps with high speed, with many frameworks like Django, Flask, Pyramid. This is used on the server-side of web development, it can be interacting with database and build rest API services.

Data Science: The data science people are totally dependent on Python because of its advantages like great community, simplicity. There most popular frameworks and libraries are using in data science field like Pandas (Data Analyst), Tensor-flow (Machin and deep learning), Matplotlib (Data Visualization).

Artificial Intelligence: The near future will be the time of artificial intelligence. Machines and computers were used to perform mathematical calculations at very high speeds but now many big researchers and organizations are workings on building intelligent system that can perform tasks as a human would. Some python libraries are used for building intelligent systems are CV- open source computer vision library, NLTK- Natural language toolkit for Natural language processing.

Automation: There are many organizational and business tasks that are repetitive. It can easily access and read all types of files. The library and frameworks used for automation are like Selenium Python, Robot framework, PyUnit, PyTest .

With the increasing demand of coding and programming in today’s world. It is even a best way to start learning experience of coding. Python is attractive powerful and solid language. If you already know about the Python programming then without wasting any time to go toward your career growth.  So I suggest, there is a one of best place available for you that are “Guidance Point Institute in Pune”. Thanks for giving me such superb platform.

[wumblr]

I noticed you mentioning rasbian and I know you do DataSci stuff, so I figured I’d ask if you have any recommendations on setting up Conda on a Raspberri Pi 4 (if you use Conda)... seems like the miniconda distro designed for the Pi’s processor is wildly deprecated, and I’m wondering what else I should use... thanks in advance for any advice!

i do have a recommendation! don’t use linux 

i’m half joking but like, i’ve gotten a lot more mileage out of opening browser-based scratchpads like repl.it or kaggle or github pages or jupyter notebooks and trying out some code. that, to me, is what “learning data science” is -- everything else is “setting up an environment,” and i’ve lost countless hours trying to do that, when i could have just opened up a scratchpad and tried the code idea i had and been done with it... if you’re asking about package managers because you can’t even open a scratchpad, sort of like me, who can’t even figure out how to play audio so i can watch a youtube video telling me where to solder the wires on my laptop power cord so i can get the hell off linux, then, well, reread paragraph one lmao sorry

afaik, conda is a package manager, and anaconda and miniconda are distros. miniconda is basically empty, so, perhaps counterintuitively, anaconda, the full version, should be easier to use... however, evidence would suggest, on my pi, i use a different package manager, called pip. why i made this decision or how it works, i don’t know. it would seem the tradeoff is that pip neglects non-python libraries, and anaconda more broadly focused, but that works for me because i’m mostly using python. (or does it? i still have no audio)

[ourneelworld-blog]

Everything You Ever Wanted to Know About Machine Learning With Python

We are living in the ‘age of data’ that is enriched with better computational power and more storage resources. This data or information is increasing day by day, but the real challenge is to make sense of all the data. Businesses & organizations are trying to deal with it by building intelligent systems using the concepts and methodologies from Data science, Data Mining and Machine learning. Among them, machine learning is the most exciting field of computer science. It would not be wrong if we call machine learning the application and science of algorithms that provides sense to the data.

Before going inside of Article let's first understand what python is and what Machine Learning is ?

Python

Python is a widely used high-level programming language for general-purpose programming

Python is an interpreted, object-oriented programming language that has gained popularity because of its clear syntax and readability. Python was created by Guido van Rossum.  This means that it is a programming language which can be run on any system (ex. Linux). The main component of this language is called an object.

Machine Learning

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.  Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. We use algorithms and models to achieve this.

 Clearly, Machine Learning with Python it is a part of Artificial Intelligence. Using it, we can enable a machine to learn how to do a task on its own without explicitly telling it what to do and when. In other words we program it to think for itself instead of just programming it to act.

Python is one of the programming languages used to create Artificial Intelligence. Python community has developed many modules to help programmers implement machine learning. There are various ML algorithms, techniques and methods that can be used to build models for solving real-life problems by using data.

Apart from enjoying huge popularity in different areas of software development, Python has obtained a leading position in the machine learning domain today. The combination of simplicity, shorter development time, and consistent syntax make Python well-suited for projects in the field of machine learning. Anaconda is the version of Python that is supported by all commonly used OSs like Windows, Linux etc. It offers a complete package for machine learning that includes scikit-learn, matplotlib and NumPy. If you don’t have any prior knowledge of programming, there’re lots of online resources, books etc that can help you obtain the fundamental knowledge.

And for more information you can go on to the given link ML training learning in Noida.

[cgknowhow]

GSoC ‘19 Final Report

GSoC is coming to an end. I’m glad to say that I’ve met all the goals as planned in the beginning. You can find out about it in my previous blog posts.

This post is going to be a very brief report of all the things implemented, goals accomplished, some stuff that’s left to do and future plans.

My project was to implement a Data Visualization library in Swift (similar to matplotlib). Before this project, no plotting framework had been written in Swift that could work on Linux, macOS, iOS, or the the other platforms Swift can target. A truly cross-platform Swift plotting framework was needed to fill a gap in the Swift data science ecosystem.

GitHub link to project: https://github.com/KarthikRIyer/swiftplot

What work was done?

The following features were implemented:

1. LineChart #4

2. Plotting functions in LineChart #11

3. SVG Renderer and AGG Renderer as backends. #3

4. Secondary axis in LineChart. #16

5. Bar Chart (both vertical and horizontal, with hatching). #21 #23

6. Scatter Plot (with various scatter patterns). #25

7. Histogram #30

8. Improved aesthetics and text rendering using FreeType. #33

9. A set of examples, to make sure addition of code doesn’t break the existing code. (initiated in #7 with many updates in subsequent PRs).

10. A pure Swift library to generate Jupyter display messages. It is used to display base64 images in a Jupyter notebook. #67

What’s left to do?

Although I’ve completed all the goals as planned, I have started work on a CoreGraphics rendering backend for the iOS and macOS platforms. The PR is currently under review, and little work is left before it is ready to be merged. #35

Also the updated documentation is yet to be merged. This PR has also been submitted for review. #36

Also, the latest work doesn’t work wih Jupyter Notebooks becuase it isn’t able to find the FreeType install on the system when in Jupyter. I haven’t been able to find a fix for this yet.

Future Work

These are some things I plan to work on, post GSoC:

1. Implement an OpenGL Rendering backend.

2. Work on adding support for real-time plotting. For example updating the plot while a machine learning model is training.

3. Implement more complex plot types, such as Contours and Fields.

Highlights and Challenges

A flexible architecture: This approach was slightly new to me. I had to implement an architecture that kept the rendering of primitives as far away from the plotting logic as possible. This provided the necessary flexibility to add more rendering backends when required.

Implementing a pure Swift display library for swift-jupyter: It took me some time to understand how Jupyter worked. I hadn’t ever used Jupyter notebook before, let alone write a library for it. Once I understood how Jupyter worked it was relatively easy to get it to work. But I still faced a few hurdles which were a little tricky to debug. Like, when I was sending a serialized c-string to the Jupyter kernel, it had a next line delimiter at the end. This caused problems with the Jupyter kernel. I required help from my mentors to solve this issue. I also got to explore SHA256 hashing while working on this. It was necessary for message signing. I used IBM’s BlueCryptor library for SHA256.

Allowing more input data types: Deciding which method to use to allow more input data types was time consuming. In the end we settled on generics. Implementing this also took some time, and it was a bit tricky to get the FloatConvertible protocol to work. FloatConvertible was a Swift protocol I wrote which took Numeric input which could be converted into Float. This isn’t perfect yet. It still has some trouble working with the Int Data type.

Working with FreeType: Working with FreeType was a little tricky. I needed a method to draw custom fonts to improve the aesthetics of the plot. Although AGG provides methods that uses FreeType to draw text, I needed a way to use FreeType with Swift Package Manager. One way was to include FreeType with SwiftPlot, but I couldn’t get it to build with Swift Package Manager. FreeType had it’s own build configuration and also had some Python files. The other option was to make Swift Package Manager find the FreeType install on the system, which came with the necessary headers and the compiled binaries. After a lot of legwork I found a way to do that. The next hurdle here was to test it on the macOS platform. I didn’t have a Mac and when I got access to one I wasn’t able to install FreeType on it. After some more legwork I was able to build FreeType on my own and install it from there. This section is also not perfect yet. It isn’t able to find the FreeType install on the system when in a Jupyter Notebook.

Conclusion

I got to learn a lot of things during the summers. I learnt a new language (Swift) and its conventions, approach to building flexible frameworks, how Jupyter works, and many more things. In other words I’ve had a great and productive summer, working under the guidance of the best mentors possible. Brad and Marc (my mentors) were very motivating and their solutions to my problems always worked! 

It was a great feeling to see my project take shape from scratch and even greater to see the community showing interest in it right from the start. I will definitely keep working on this project post GSoC and would like to see a community growing around it, and people contributing to it.

I hope that this project comes to the next GSoC too so that it keeps on maturing and its reach increases more. I would be more than willing to help people get started.

I’ll keep posting updates whenever there are any developments. So stay tuned!!

[techdata1]

How to start a career in data science in Mumbai and Pune

A data scientist is one of the topmost ranking professionals in any analytics organization. A data scientist is ranked on the topmost positions for the best jobs in the world. In today’s date, data scientists are scarce which is why they are highly in demand and because of that, there are excellent growth prospects in this field. The role of a data scientist is to understand the business problem, outline a data analyst strategy, gather and format the required information, apply techniques or algorithms employing the accurate tools, and then make suggestions backed up by data. For doing so, one needs to take professional training for understanding the subject and performing to the best of their capabilities.

For launching a career in data steps you need to perform certain steps such as:

1.Find out what you require to learn

Data science can be an overwhelming career as a lot of people will often tell you that you need to master certain concepts for stepping into this field, but that’s not exactly true. Data science is a procedure of asking exciting questions and then answering it employing data.

A data science workflow follows:

Asking a question

Collecting data for answering the question

Cleaning the data

Analyzing, exploring, and visualizing the data

Evaluating and building a machine learning model

Interacting results

The workflow doesn’t require advanced maths but a basic understanding of the subject along with programming language.

2.Getting comfortable with Python

Python and R are both great choices where R is more popular in academics and python in industry. You don’t require learning both but you should emphasize on understanding one language with its ecosystem of data science package. Most people prefer python today due to its benefits and user-friendly interface. If you are preferring Python then install Anaconda distribution, as it will simplify your process of package management, and installation on OSX, Windows and, Linux.

3.Understanding data manipulation, analyzation, and visualization with Pandas

For operating with data in Python, get acquainted with the Pandas library. Pandas are known to provide a high-performance data structure called data frames that are comfortable for tabular data with columns of a distinct type that is similar to a SQL table or an excel spreadsheet. Understanding pandas will increase your efficiency while working with data. Though pandas consist of an overwhelming amount of functionality and offer several ways to accomplish the task. These characteristics can make it quite challenging to learn pandas and figure out the best practices.

4.Learn Machine Learning with Scikit-learn

Building ML models for extracting insights from data or predicting the future is the best part of data science. SciKit is one of the most popular libraries for ML in python as:

It provides a consistent and clean interface to several distinct models

It provides many tuning parameters for every model and also selects sensible default

The documentation of Scikit is exceptional and helps in knowing the models and ways to employ it properly

Though machine learning is still a rapid and highly complex evolving area has Scikit has a steep learning curve. For getting a grasp of Scikit for machine learning, one needs to go for proper training which will help in gaining an insight into Scikit-learn, equipping them better in the subject, workflow and machine learning fundamentals.

5.Learning machine learning in-depth

Machine learning is a complicated field, though Scikit offers the tools required for performing effective ML, it doesn’t answer certain questions directly like:

How to know while machine learning model will work best with which dataset

How to evaluate if the model will generalize to future data

Ways to interpret the results of the model

How to select which feature is suitable with the model

If you want to be comfortable and equip machine learning then you need to learn how to answer those questions and for that experience and further study both are required and for that data science training in Mumbai and data science training in Pune are the best options to go for.

6.Keep practicing and learning

One of the best pieces of advice for improving your data science skill is to discover the thing that motivates you to practice things that you have learned and perform better and then do that thing. That could be Kaggle competitions, personal data science projects, reading books, taking online courses, attending conferences or meetups, or any other thing.

Granting to open source projects will help in practicing collaboration with others.

Kaggle competitions are an excellent way to practice data science without coming with problems. You don’t need to be concerned about high your place but emphasize learning something new with each practice.

Attend PyCon US if you are willing to have an actual experience with the Python community. There might also be other conferences but PyCon US is one of the best. You can also consider attending your nearest PyData and SciPy conference for assisting you better.

Create your own data science projects and share it with others to get insights and views of how you can do better and also to inform them that you can do reproducible data science

Data scientists are professionals skilled in this field as they are well versed as to how to work on

huge amounts of data for tapping the areas facing difficulties and solve them with ease so that the company can grow. A rise in the demand for data scientists has led to huge growth in the profile of people working in this field. Data science is one of the most solid career choices for freshers as well as equipped professionals. These are some of the simple steps that you can go through for starting your career as a data scientist and walking through the path of success.

[learnetutorials]

Why Learning Python is Essential: Programs You Can Create with This Powerful Language

Python is a high-level, interpreted programming language that is used for a wide range of applications. It is widely considered one of the most popular and important programming languages in the world, and is used by developers, data scientists, and analysts worldwide. In this blog post, we will explore the importance of learning Python and some of the programs that can be created using this powerful language.

Why Learn Python?

Easy to Learn: Python is a beginner-friendly language that is easy to learn and understand. The syntax of the language is simple and straightforward, making it an ideal language for beginners.

Versatility: Python is a versatile language that can be used for a wide range of applications. It can be used for web development, game development, scientific computing, and more.

Popularity: Python is one of the most popular programming languages in the world, and is used by developers, data scientists, and analysts worldwide. By learning Python, you open up a world of opportunities and job prospects.

Community: Python has a large and active community of developers who are always working to improve the language and create new libraries and tools. This means that there is a wealth of resources available for learning and development.

Flexibility: Python can be used in both object-oriented and functional programming paradigms. This flexibility makes it easier to write code that is easy to read and maintain.

Programs That Can be Created Using Python:

Web Development: Python can be used to develop web applications using frameworks like Django and Flask. These frameworks make it easy to create complex web applications quickly and efficiently.

Data Science: Python is widely used in data science for data analysis, machine learning, and artificial intelligence. Libraries like NumPy, Pandas, and Scikit-learn make it easy to work with data and perform statistical analysis.

Automation: Python can be used for automating tasks such as file manipulation, web scraping, and database management. This can save a lot of time and effort, especially for repetitive tasks.

Game Development: Python can be used to develop games using popular libraries like Pygame. This makes it easy to create 2D and 3D games quickly and efficiently.

Desktop Applications: Python can be used to create desktop applications using frameworks like PyQt and Tkinter. This makes it easy to create cross-platform desktop applications that can run on Windows, Mac, and Linux.

In conclusion, Python is a versatile and powerful programming language that is widely used in the industry. By learning Python, you open up a world of opportunities and job prospects. Whether you want to develop web applications, work with data, automate tasks, develop games, or create desktop applications, Python is the language to learn. With its ease of use, flexibility, and versatility, Python is the perfect language for both beginners and experienced developers alike. If you are interested in learning ython Programming, refer learnetutorials.com which is a completely free online learning platform.