#Linux for data science

[codeexpertinsights]

Harnessing Linux for Data Science: Integrating R, Python, and Machine Learning

Linux is the most appropriate OS for data research since it is portable, expansible and complies with many different open-source software. In this respect, machine learning frameworks can be easily linked to the powerful computation languages such as R and Python in Linux to let data scientists fully optimise the resourceful processing efficiency and model construction. It is now time to consider how these technologies improve the productivity of data science processes. They offer a less rigorous process when clients begin to employ Linux as their foundation, coupling the utility of Python with the statistician might of R. They also improve the understanding, in the process of raising production.

[gnome-de-official]

Hey stats or linguistics nerds!

(Explanation below)

Explanation:

Countable and singular means you hear “data” and think there’s exactly one thing in discussion. You would use it like “this data is an outlier, whereas that one is not.” Examples of this form: a cat, an artwork

Countable and plural means you hear “data” and think there’s more than one thing being talked about. You would use it like “these data show a positive trend, whereas those do not.” When discussing just one, you would use the term “datum”. Examples: cars, planets

Uncountable means you hear “data” and think that there’s some quantity of it, but it’s not necessarily a numbered amount, in a similar way to a fluid. You would use it like “this data doesn’t prove my hypothesis, but some of that data does.” Examples: water, air

Bias alert:

My father is a database engineer and has always used the word “data” as uncountable. I believe the “correct” way to use it is countable plural with the singular form being “datum”, though there’s no real answer because it’s linguistics and languages shift with use. That’s why I’m asking the people, in order to know how the term is used

[learnandbuild]

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[aarvyedutech]

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Data Science and Machine Learning: Skills in data analysis, machine learning, and artificial intelligence are highly sought after in various industries.

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[highskyit]

Fix Deployment Fast with a Docker Course in Ahmedabad

Are you tired of hearing or saying, "It works on my machine"? That phrase is an indicator of disruptively broken deployment processes: when code works fine locally but breaks on staging and production.

From the perspective of developers and DevOps teams, it is exasperating, and quite frankly, it drains resources. The solution to this issue is Containerisation. The local Docker Course Ahmedabadpromises you the quickest way to master it.

The Benefits of Docker for Developers

Docker is a solution to the problem of the numerous inconsistent environments; it is not only a trendy term. Docker technology, which utilises Docker containers, is capable of providing a reliable solution to these issues. Docker is the tool of choice for a highly containerised world. It allows you to take your application and every single one of its components and pack it thus in a container that can execute anywhere in the world. Because of this feature, “works on my machine” can be completely disregarded.

Using exercises tailored to the local area, a Docker Course Ahmedabad teaches you how to create docker files, manage your containers, and push your images to Docker Hub. This course gives you the chance to build, deploy, and scale containerised apps.

Combining DevOps with Classroom Training and Classes in Ahmedabad Makes for Seamless Deployment Mastery

Reducing the chances of error in using docker is made much easier using DevOps, the layer that takes it to the next level. Unlike other courses that give a broad overview of containers, DevOps Classes and Training in Ahmedabad dive into automation, the establishment of CI/CD pipelines, monitoring, and with advanced tools such as Kubernetes and Jenkins, orchestration.

Docker skills combined with DevOps practices mean that you’re no longer simply coding but rather deploying with greater speed while reducing errors. Companies, especially those with siloed systems, appreciate this multifaceted skill set.

Real-World Impact: What You’ll Gain

Speed: Thus, up to 80% of deployment time is saved.

Reliability: Thus, your application will remain seamless across dev, test, and production environments.

Confidence: For end-users, the deployment problems have already been resolved well before they have the chance to exist.

Achieving these skills will exponentially propel your career.

Conclusion: Transform Every DevOps Weakness into a Strategic Advantage

Fewer bugs and faster release cadence are a universal team goal. Putting confidence in every deployment is every developer’s dream. A comprehensive Docker Course in Ahmedabador DevOps Classes and Training in Ahmedabadcan help achieve both together. Don’t be limited by impediments. Highsky IT Solutions transforms deployment challenges into success with strategic help through practical training focused on boosting your career with Docker and DevOps.

[computer-knowledge27]

what is Data Science and Machine learning ?

Data Science and Machine Learning are two closely related fields within the broader domain of artificial intelligence and data analytics. Here's an overview of each:

Data Science:

Data Science is an interdisciplinary field that deals with extracting insights and knowledge from structured and unstructured data.

It involves various techniques, including data mining, statistics, machine learning, and visualization, to analyze large volumes of data and derive actionable insights.

Data Science encompasses the entire data lifecycle, including data collection, cleaning, preprocessing, analysis, interpretation, and visualization.

It is widely used across various industries for tasks such as predictive analytics, pattern recognition, customer segmentation, fraud detection, and decision-making.

Machine Learning:

Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

It involves training algorithms on labeled or unlabeled data to recognize patterns, infer relationships, and make predictions or decisions based on new input data.

Machine Learning algorithms can be categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, depending on the type of data and the learning approach.

Applications of Machine Learning include image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, medical diagnosis, and financial forecasting.

Relationship between Data Science and Machine Learning:

Data Science often incorporates Machine Learning techniques as part of its toolkit for analyzing and interpreting data. Machine Learning algorithms are used to uncover patterns, trends, and insights from large datasets, which can then be utilized for making data-driven decisions.

Machine Learning is a key component of many Data Science projects, particularly in tasks such as predictive modeling, classification, clustering, and anomaly detection.

Data Scientists leverage Machine Learning algorithms to build predictive models and analytical solutions that address specific business or research objectives, thereby extracting actionable insights from data.

In summary, Data Science focuses on extracting knowledge from data using various techniques, while Machine Learning specifically deals with developing algorithms that enable computers to learn from data and make predictions or decisions. Together, these fields play a crucial role in leveraging the power of data to drive innovation, solve complex problems, and improve decision-making across various domains

machine learning and data science.

[coreai-5]

Visit here learn these Tools - Online or Offline

[errorid-mt7lt7k7unfr9ttfua5]

me when companies try to force you to use their proprietary software

anyway

Layperson resources:

firefox is an open source browser by Mozilla that makes privacy and software independence much easier. it is very easy to transfer all your chrome data to Firefox

ublock origin is The highest quality adblock atm. it is a free browser extension, and though last i checked it is available on Chrome google is trying very hard to crack down on its use

Thunderbird mail is an open source email client also by mozilla and shares many of the same advantages as firefox (it has some other cool features as well)

libreOffice is an open source office suite similar to microsoft office or Google Suite, simple enough

Risky:

VPNs (virtual private networks) essentially do a number of things, but most commonly they are used to prevent people from tracking your IP address. i would suggest doing more research. i use proton vpn, as it has a decent free version, and the paid version is powerful

note: some applications, websites, and other entities do not tolerate the use of VPNs. you may not be able to access certain secure sites while using a VPN, and logging into your personal account with some services while using a vpn *may* get you PERMANENTLY BLACKLISTED from the service on that account, ymmv

IF YOU HAVE A DECENT VPN, ANTIVIRUS, AND ADBLOCK, you can start learning about piracy, though i will not be providing any resources, as Loose Lips Sink Ships. if you want to be very safe, start with streaming sites and never download any files, though you Can learn how to discern between safe, unsafe, and risky content.

note: DO NOT SHARE LINKS TO OR NAMES OF PIRACY SITES IN PUBLIC PLACES, ESPECIALLY SOCAL MEDIA

the only time you should share these things are either in person or in (preferably peer-to-peer encrypted) PRIVATE messages

when pirated media becomes well-known and circulated on the wider, public internet, it gets taken down, because it is illegal to distribute pirated media and software

if you need an antivirus i like bitdefender. it has a free version, and is very good, though if youre using windows, windows defender is also very good and it comes with the OS

Advanced:

linux is great if you REALLY know what you're doing. you have to know a decent amount of computer science and be comfortable using the Terminal/Command Prompt to get/use linux. "Linux" refers to a large array of related open source Operating Systems. do research and pick one that suits your needs. im still experimenting with various dispos, but im leaning towards either Ubuntu Cinnamon or Debian.

[foone]

So Warframe added a "Pom-2" Alternate 1999 computer (that's needed for weird void magic future science wizardry). Thoughts?

Only thing I have that's a sort of question mark is that I don't know of many setups that would have needed a 5 1/4" floppy in 99 (or why it has both the tower and the under monitor unit)

ugh. OKAY, so... the tower and desktop combination is just weird. I have, on one occasion, run a "server" that was two towers, and the original PC supported a DUAL-DESKTOP mode, but both types together? nonsense.

dual monitor was rare but possible in 1999 (win98 added native support), so I think the best interpretation here is that this is actually two computers. maybe the one on the left is missing the keyboard and mouse because it's being used as some kind of server for the other computer? I used a little case like that to run my first linux server, which was also acting as a router for my internal network.

The OS is weird. The icons above the menu-bar look like win98, the dialog box is windows 3.x, the menu-bar icons on the bottom are pure os X (although they remind me of like a web-TV kinda system, like hotkeys for email/internet/etc), but the greyscale is very classic mac system. Actually it kinda reminds me of C64's GEOS, but GEOS was very classic-mac.

Like most CRT-filters, they turned the scanlines up WAY TOO HIGH. No CRT I've ever seen looked that fucking terrible. The monitor buttons are a bit odd: You didn't get monitors with buttons on the front until long after they were all color... but maybe it's a color monitor that's showing a monochrome OS?

as for the floppies: yeah. There are multiple mistakes here.

5.25" in 1999 is just silly. If you still had 5.25" disk drives in 1999, you were intentionally doing some retrocomputing stuff. For reference, around 2001 my PC repair job would specifically ask me to copy data off 5.25" disks, because they didn't have any 5.25" drives anymore, and I was their only tech who did.

The other mistake is that they have THREE floppy drives. so the PC doesn't really support that, natively? You can do some tricks and make it work (The youtuber Tech Tangents did a video on how it could be done), but realistically two was the normal max.

The final mistake is that all the drive activity lights are on. Those are only supposed to be on while the drive is reading or writing... and I don't see any disks in those drives! Let alone a situation that would involve turning all three on at once (I don't think that's even possible on most floppy controllers!)

In fact, the main time you'd end up with the drive lights stuck on like that is when you've installed the drive cable upside down. That ends up with them getting stuck on and non-functional. So this computer looks, to me, like it was put together incorrectly and no one noticed.

I don't believe that font would be on a black & white retro computer. Nope. Too smooth and too big.

There's also a USB icon on that OS: I don't think there's ever been a monochrome OS that supported OS, and looking at that computer case I don't believe that it has USB. Maybe the tower would, but the desktop? no.

That keyboard is off a Gateway 2000 computer. Something like this:

[merjashourov]

Tech Skill For Computer Science Students

Technical Skills for Computer Science Students

Software Development

MERN Stack

Python-Django Stack

Ruby on Rails ( RoR )

LAMP ( Linux, Apache Server, MySql, PHP )

.Net Stack

Flutter Stack ( For mobile app )

React Native Stack ( Cross Platform mobile app development )

Java Enterprise Edition

Serverless stack - "Cloud computing service"

Blockchain Developer

Cyber Security

DevOps

MLOps

AL Engineer

Data Science

[cyberstudious]

what's it like studying CS?? im pretty confused if i should choose CS as my major xx

hi there!

first, two "misconceptions" or maybe somewhat surprising things that I think are worth mentioning:

there really isn't that much "math" in the calculus/arithmetic sense*. I mostly remember doing lots of proofs. don't let not being a math wiz stop you from majoring in CS if you like CS

you can get by with surprisingly little programming - yeah you'll have programming assignments, but a degree program will teach you the theory and concepts for the most part (this is where universities will differ on the scale of theory vs. practice, but you'll always get a mix of both and it's important to learn both!)

*: there are some sub-fields where you actually do a Lot of math - machine learning and graphics programming will have you doing a lot of linear algebra, and I'm sure that there are plenty more that I don't remember at the moment. the point is that 1) if you're a bit afraid of math that's fine, you can still thrive in a CS degree but 2) if you love math or are willing to be brave there are a lot of cool things you can do!

I think the best way to get a good sense of what a major is like is to check out a sample degree plan from a university you're considering! here are some of the basic kinds of classes you'd be taking:

basic programming courses: you'll knock these out in your first year - once you know how to code and you have an in-depth understanding of the concepts, you now have a mental framework for the rest of your degree. and also once you learn one programming language, it's pretty easy to pick up another one, and you'll probably work in a handful of different languages throughout your degree.

discrete math/math for computer science courses: more courses that you'll take early on - this is mostly logic and learning to write proofs, and towards the end it just kind of becomes a bunch of semi-related math concepts that are useful in computing & problem solving. oh also I had to take a stats for CS course & a linear algebra course. oh and also calculus but that was mostly a university core requirement thing, I literally never really used it in my CS classes lol

data structures & algorithms: these are the big boys. stacks, queues, linked lists, trees, graphs, sorting algorithms, more complicated algorithms… if you're interviewing for a programming job, they will ask you data structures & algorithms questions. also this is where you learn to write smart, efficient code and solve problems. also this is where you learn which problems are proven to be unsolvable (or at least unsolvable in a reasonable amount of time) so you don't waste your time lol

courses on specific topics: operating systems, Linux/UNIX, circuits, databases, compilers, software engineering/design patterns, automata theory… some of these will be required, and then you'll get to pick some depending on what your interests are! I took cybersecurity-related courses but there really are so many different options!

In general I think CS is a really cool major that you can do a lot with. I realize this was pretty vague, so if you have any more questions feel free to send them my way! also I'm happy to talk more about specific classes/topics or if you just want an answer to "wtf is automata theory" lol

[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.

[learnandbuild]

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[biopractify]

How to Transition from Biotechnology to Bioinformatics: A Step-by-Step Guide

Biotechnology and bioinformatics are closely linked fields, but shifting from a wet lab environment to a computational approach requires strategic planning. Whether you are a student or a professional looking to make the transition, this guide will provide a step-by-step roadmap to help you navigate the shift from biotechnology to bioinformatics.

Why Transition from Biotechnology to Bioinformatics?

Bioinformatics is revolutionizing life sciences by integrating biological data with computational tools to uncover insights in genomics, proteomics, and drug discovery. The field offers diverse career opportunities in research, pharmaceuticals, healthcare, and AI-driven biological data analysis.

If you are skilled in laboratory techniques but wish to expand your expertise into data-driven biological research, bioinformatics is a rewarding career choice.

Step-by-Step Guide to Transition from Biotechnology to Bioinformatics

Step 1: Understand the Basics of Bioinformatics

Before making the switch, it’s crucial to gain a foundational understanding of bioinformatics. Here are key areas to explore:

Biological Databases – Learn about major databases like GenBank, UniProt, and Ensembl.

Genomics and Proteomics – Understand how computational methods analyze genes and proteins.

Sequence Analysis – Familiarize yourself with tools like BLAST, Clustal Omega, and FASTA.

🔹 Recommended Resources:

Online courses on Coursera, edX, or Khan Academy

Books like Bioinformatics for Dummies or Understanding Bioinformatics

Websites like NCBI, EMBL-EBI, and Expasy

Step 2: Develop Computational and Programming Skills

Bioinformatics heavily relies on coding and data analysis. You should start learning:

Python – Widely used in bioinformatics for data manipulation and analysis.

R – Great for statistical computing and visualization in genomics.

Linux/Unix – Basic command-line skills are essential for working with large datasets.

SQL – Useful for querying biological databases.

🔹 Recommended Online Courses:

Python for Bioinformatics (Udemy, DataCamp)

R for Genomics (HarvardX)

Linux Command Line Basics (Codecademy)

Step 3: Learn Bioinformatics Tools and Software

To become proficient in bioinformatics, you should practice using industry-standard tools:

Bioconductor – R-based tool for genomic data analysis.

Biopython – A powerful Python library for handling biological data.

GROMACS – Molecular dynamics simulation tool.

Rosetta – Protein modeling software.

🔹 How to Learn?

Join open-source projects on GitHub

Take part in hackathons or bioinformatics challenges on Kaggle

Explore free platforms like Galaxy Project for hands-on experience

Step 4: Work on Bioinformatics Projects

Practical experience is key. Start working on small projects such as:

✅ Analyzing gene sequences from NCBI databases ✅ Predicting protein structures using AlphaFold ✅ Visualizing genomic variations using R and Python

You can find datasets on:

NCBI GEO

1000 Genomes Project

TCGA (The Cancer Genome Atlas)

Create a GitHub portfolio to showcase your bioinformatics projects, as employers value practical work over theoretical knowledge.

Step 5: Gain Hands-on Experience with Internships

Many organizations and research institutes offer bioinformatics internships. Check opportunities at:

NCBI, EMBL-EBI, NIH (government research institutes)

Biotech and pharma companies (Roche, Pfizer, Illumina)

Academic research labs (Look for university-funded projects)

💡 Pro Tip: Join online bioinformatics communities like Biostars, Reddit r/bioinformatics, and SEQanswers to network and find opportunities.

Step 6: Earn a Certification or Higher Education

If you want to strengthen your credentials, consider:

🎓 Bioinformatics Certifications:

Coursera – Genomic Data Science (Johns Hopkins University)

edX – Bioinformatics MicroMasters (UMGC)

EMBO – Bioinformatics training courses

🎓 Master’s in Bioinformatics (optional but beneficial)

Top universities include Harvard, Stanford, ETH Zurich, University of Toronto

Step 7: Apply for Bioinformatics Jobs

Once you have gained enough skills and experience, start applying for bioinformatics roles such as:

Bioinformatics Analyst

Computational Biologist

Genomics Data Scientist

Machine Learning Scientist (Biotech)

💡 Where to Find Jobs?

LinkedIn, Indeed, Glassdoor

Biotech job boards (BioSpace, Science Careers)

Company career pages (Illumina, Thermo Fisher)

Final Thoughts

Transitioning from biotechnology to bioinformatics requires effort, but with the right skills and dedication, it is entirely achievable. Start with fundamental knowledge, build computational skills, and work on projects to gain practical experience.

Are you ready to make the switch? 🚀 Start today by exploring free online courses and practicing with real-world datasets!

[highskyit]

Riding the Cloud Wave: Red Hat Training in Ahmedabad

Cloud computing is not only a way to access and use technology services and resources remotely, but the foundation of IT infrastructure relies on it. To enable businesses to move toward automation and containerisation, the Red Hat OpenShift platform facilitates cloud-native software development. Open-source platforms and hands-on practical skills are crucial now more than ever for success in the competitive tech industry.

If you are from Gujarat, an opportunity for career advancement lies in attending the Red Hat Training and Certification in Ahmedabad, which prepares you for the new in-demand positions.

Why Red Hat Training and Certification in Ahmedabad?

With a good reputation in the field of technology, Ahmedabad is also known as an educational hub. Red Hat courses in Ahmedabad are designed for fast-tracked professional development.

Here is what a student can look out for:

IT Industry Accredited Certification – Boost your career as you get properly recognised skills by IT professionals with the guiding documents that will help you gain higher positions.

Contemporary Use of OpenShift – Utilise Red Hat's flagship platform to learn how to set up, deploy, and, thus, manage containers along with Kubernetes.

Post Secondary Learning Experience – Classes are thus structured according to international standards and industry practices and address real-life challenges.

Now, DevOps engineers, system admins, and cloud developers are enabled with the new tools to help them complete their engineering goals. Whatever path they pursue with this training, they can now succeed.

Red Hat Training Ahmedabad: What to Expect

Alongside theory, learners will acquire Red Hat Training Ahmedabad through an immersive approach, including:

Teaching sessions and labs conducted by trainers

Thus, preparation for certification examinations

Learning paths dependent on roles (Like RHCSA, RHCE, OpenShift Admin, et cetera)

Access to Red Hat-endorsed resources

And so much more; with everything said above, you can move from novice to certified in a matter of weeks, which is favourable when considering time and financial investments.

Accelerate Earning Your Certification with Local Access

Even if you are a complete novice to Linux or an experienced system administrator, you will find everything you need, from instructor-led training to exam simulations in Ahmedabad, on Red Hat’s learning paths. Courses are taught using Red Hat training materials, which makes sure that you will be employable from the outset.

Conclusion: Elevate your cloud skillset The need for Red Hat Training Ahmedabad with certified professionals is increasing, and with the adoption of the cloud, there is no better time to jump in. For those wishing to remain a step ahead of others in the industry, look no further than Red Hat Training and Certification in Ahmedabad, which acts as a springboard for a powerful career in IT. Visit Highsky IT Solutions to learn more about Red Hat courses and certification paths.

[python-programming-language]

Pydroid 3 And Much More ...

Post #146: AlternativeTo, 27 Kilobyte AB by Ola & Markus, Crowdsourced Software Recommendations, Pydroid 3 And Alternatives, 2024.