#data science using python

[trainingin2inglobal]

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[tripta-123]

Learn data science using Python in Codesquadz

Learn Data Science with Python to become an in-demand developer with mastery in crucial skills like data manipulation, visualization, and machine learning.

[zora28]

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[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

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[cromacampusinstitute]

Python is widely used in Data Science due to its simplicity, versatility, and extensive ecosystem of libraries and frameworks. It allows data scientists to efficiently handle data manipulation, analysis, and visualization tasks using powerful libraries like Pandas, NumPy, and Matplotlib.

[advanto-software]

Get skilled Today with Advanto Software’s best Data Science Course in Pune

When it comes to the modern and rapidly developing world which is based on the usage of the Internet and digital technologies people tend to find Data Science as one of the most promising professions. Data science skills play an important role in many fields as organizations turn to evidence-based decision-making systems in the growing knowledge economy. There is a highly ranked Data Science Course in Pune provided by Advanto Software that can assist individuals to enhance personal proficiency and get a promotion. Why Advanto Software’s Data Science Course? Comprehensive Curriculum Our Data Science Course at Advanto Software includes several units that are highly informative and useful for aspiring data scientists. Our curriculum includes:

• Introduction to Data Science:

 The article aims to give a clear definition of data science and explain its relevance in the contemporary world.

• Programming for Data Science:

Knowledgeable in programming languages like Python and R which are used mainly in data analysis.

 • Statistics and Probability:

Basic ideas with which data science is built, help in improving data interpretation.

• Data Wrangling:

Methods for extracting, purging, structuring, and reformatting data for easy usage.

• Machine Learning:

Practical experience in supervised as well as unsupervised machine learning algorithms.

• Data Visualization:

Effective methods and approaches to designing and implementing good informative and meaningful visualizations.

• Capstone Project:

Further projects can be conducted in a real environment where learners can practice what they have learned. Expert Instructors

Our course is offered by working professionals who are experienced data scientists and analysts. These instructors also come to the class with experience, and knowledge and pass real-life occurrences in their teachings and hence the students gain a lot of understanding in the matter.

Hands-On Learning

The best approach that we take when it comes to learning is a pragmatic one. As a part of our course, some many projects and assignments enable the students to use the actual problem situations and apply the theories learned. Such an approach to learning makes it possible for the students to be market-ready as soon as they complete the course. State-of-the-Art Facilities

The reason that Advanto Software’s training institute located at Pune is fully endowed with all the modern technologies required for effective training. Computer lab facilities are equipped with superior hardware and the student can get a hold of different software tools that are used in the profession.For whom is this course useful?

Aspiring Data Scientists

If you want to get a job in data science, then this course can be a good beginning. It offers a sound grounding for the fundamental competencies and information that are required in this course of business. Working Professionals

For the following groups of people, this course would be very helpful: People in the course who want to remain marketable and updated in their working industry. Data science skills are likely to improve your employability regardless of whether you are in IT, finance healthcare or any other sector. Students and Graduates

People who may benefit from developing data science skills include; recent employment seekers, computer science and engineering students, mathematics and related field students.Major Applicable Outcomes of the Course

Career Advancement

Big data science is an in-demand career nowadays with numerous firms and companies that exist offering employment to big data scientists. Upon the end of the course, one is well placed for positions like data analyst, data scientist, and machine learning engineer among others.

High Earning Potential

It noted that the remuneration offered to data science specialists is among the highest in the given field. Information science offers an individual so many high returns, that one should consider acquiring the skills.

Versatile Skills

This paper uses basic skills that are learned in this course and applicable in different fields of study. There has been a virtual explosion of high-paying jobs in the fields of finance, healthcare, retail, and technology where specialized data science skills are sought-after assets.

Certification

In conclusion of the course, you will be issued a Certification by Advanto Software only if you pass the course. This certification is your proof of mastery and it makes a good addition to your resume or your LinkedIn profile.

Enrolling in the course

Enrolling in Advanto Software's Data Science Course is simple: Enrolling in Advanto Software's Data Science Course is simple:

1. Visit Our Website: see the course details visit our website then follow the Data Science Course link.

2. Fill out the Registration Form: Fill your information and the registration form.

3. Payment: After making your selection on the payment type, go through the payment process.

4. Start Learning: After registration, you get access to the content of the course and it is always available any time you want to start.

Conclusion

The Importance of Data Science Course in Pune – Advanto Software provides knowledgeable Skills, it is time to take your abilities to the next level with our Data Science Course in Pune. A detailed syllabus, professional tutors, practical approach – all these qualities are gathered in one course to help you become a successful data scientist.

“Join our course and embark on the stepping stone towards a brighter future”

Click here to register: www.advantosoftware.com/

[ayeforscotland]

Ad | Hi folks, I'd previously been getting into the swing of posting Humble Bundle deals and the charities they were helping. With any non-bundle purchases helping to raise money for Endometriosis research and support. Then Humble decided to outsource their partner program to a system called 'Impact' which has honestly been a massive pain to get my head round. Looks like I can't link to bundles directly and they only give me the above link to share.

There is currently:

The TellTale Games Bundle featuring Batman, the Expanse, the Walking Dead, the Wolf Among Us - Currently raising money for Save the Children

The Sid Meier Collection with every Civilization game and DLC I can think of - Raising money for Covenant House and Michael J. Fox Foundation for Parkinson's Research

Math for Programmers Book Bundle which contains a whole bunch of data science, cryptography and Python books - Raising money for Girls Who Code

Learn Unit Game Development Course - From Shaders to 3D to a course on Game Feel - Raising money for Oceana

Super Game Asset Bundle for Unreal, Godot, and Unity. Over 7000 audio, visual and environmental assets - Raising money for Direct Relief.

Not sure if this format is okay, it requires you to visit the link and navigate but hope it helps? Let me know.

[elbiotipo]

fwiw and i have no idea what the artists are doing with it, a lot of the libraries that researchers are currently using to develop deep learning models from scratch are all open source built upon python, i'm sure monsanto has its own proprietary models hand crafted to make life as shitty as possible in the name of profit, but for research there's a lot of available resources library and dataset wise in related fields. It's not my area per se but i've learnt enough to get by in potentially applying it to my field within science, and largely the bottleneck in research is that the servers and graphics cards you need to train your models at a reasonable pace are of a size you can usually only get from google or amazon or facebook (although some rich asshole private universities from the US can actually afford the cost of the kind of server you need. But that's a different issue wrt resource availability in research in the global south. Basically: mas plata para la universidad pública la re puta que los parió)

Yes, one great thing about software development is that for every commercially closed thing there are open source versions that do better.

The possibilities for science are enormous. Gigantic. Much of modern science is based on handling huge amounts of data no human can process at once. Specially trained models can be key to things such as complex genetics, especially simulating proteomes. They already have been used there to incredible effect, but custom models are hard to make, I think AIs that can be reconfigured to particular cases might change things in a lot of fields forever.

I am concerned, however, of the overconsumption of electronics this might lead to when everyone wants their pet ChatGPT on their PC, but this isn't a thing that started with AI, electronic waste and planned obsolescence is already wasting countless resources in chips just to feed fashion items like iphones, this is a matter of consumption and making computers be more modular and longer lasting as the tools they are. I've also read that models recently developed in China consume much, much less resources and could potentially be available in common desktop computers, things might change as quickly as in 2 years.

[williammorrised]

Man goes to the doctor. Says he's frustrated. Says his Python experience seems complicated and confusing. Says he feels there are too many environment and package management system options and he doesn't know what to do.

Doctor says, "Treatment is simple. Just use Poetry + pyenv, which combines the benefits of conda, venv, pip, and virtualenv. But remember, after setting up your environment, you'll need to install build essentials, which aren't included out-of-the-box. So, upgrade pip, setuptools, and wheel immediately. Then, you'll want to manage your dependencies with a pyproject.toml file.

"Of course, Poetry handles dependencies, but you may need to adjust your PATH and activate pyenv every time you start a new session. And don't forget about locking your versions to avoid conflicts! And for data science, you might still need conda for some specific packages.

"Also, make sure to use pipx for installing CLI tools globally, but isolate them from your project's environment. And if you're deploying, Dockerize your app to ensure consistency across different machines. Just be cautious about Docker’s compatibility with M1 chips.

"Oh, and when working with Jupyter Notebooks, remember to install ipykernel within your virtual environment to register your kernel. But for automated testing, you should...

[nishtha135]

Which coding languages should I learn to boost my IT career opportunities?

A career in IT needs a mix of versatile programming languages. Here are some of the most essential ones:

Python – Easy to learn and widely used for data science, machine learning, web development, and automation.

JavaScript – Key for web development, allowing interactive websites and backend work with frameworks like Node.js.

Java – Known for stability, popular for Android apps, enterprise software, and backend development.

C++ – Great for systems programming, game development, and areas needing high performance.

SQL – Essential for managing and querying databases, crucial for data-driven roles.

C# – Common in enterprise environments and used in game development, especially with Unity.

[frank-olivier]

The story of BASIC’s development began in 1963, when Kemeny and Kurtz, both mathematics professors at Dartmouth, recognized the need for a programming language that could be used by non-technical students. At the time, most programming languages were complex and required a strong background in mathematics and computer science. Kemeny and Kurtz wanted to create a language that would allow students from all disciplines to use computers, regardless of their technical expertise.

The development of BASIC was a collaborative effort between Kemeny, Kurtz, and a team of students, including Mary Kenneth Keller, John McGeachie, and others. The team worked tirelessly to design a language that was easy to learn and use, with a syntax that was simple and intuitive. They drew inspiration from existing programming languages, such as ALGOL and FORTRAN, but also introduced many innovative features that would become hallmarks of the BASIC language.

One of the key innovations of BASIC was its use of simple, English-like commands. Unlike other programming languages, which required users to learn complex syntax and notation, BASIC used commands such as “PRINT” and “INPUT” that were easy to understand and remember. This made it possible for non-technical users to write programs and interact with the computer, without needing to have a deep understanding of computer science.

BASIC was first implemented on the Dartmouth Time-Sharing System, a pioneering computer system that allowed multiple users to interact with the computer simultaneously. The Time-Sharing System was a major innovation in itself, as it allowed users to share the computer’s resources and work on their own projects independently. With BASIC, users could write programs, run simulations, and analyze data, all from the comfort of their own terminals.

The impact of BASIC was immediate and profound. The language quickly gained popularity, not just at Dartmouth, but also at other universities and institutions around the world. It became the language of choice for many introductory programming courses, and its simplicity and ease of use made it an ideal language for beginners. As the personal computer revolution took hold in the 1970s and 1980s, BASIC became the language of choice for many hobbyists and enthusiasts, who used it to write games, utilities, and other applications.

Today, BASIC remains a popular language, with many variants and implementations available. While it may not be as widely used as it once was, its influence can still be seen in many modern programming languages, including Visual Basic, Python, and JavaScript. The development of BASIC was a major milestone in the history of computer science, as it democratized computing and made it accessible to a wider range of people.

The Birth of BASIC (Dartmouth College, August 2014)

Friday, April 25, 2025

[biopractify]

Free online courses for bioinformatics beginners

🔬 Free Online Courses for Bioinformatics Beginners 🚀

Are you interested in bioinformatics but don’t know where to start? Whether you're from a biotechnology, biology, or computer science background, learning bioinformatics can open doors to exciting opportunities in genomics, drug discovery, and data science. And the best part? You can start for free!

Here’s a list of the best free online bioinformatics courses to kickstart your journey.

📌 1. Introduction to Bioinformatics – Coursera (University of Toronto)

📍 Platform: Coursera 🖥️ What You’ll Learn:

Basic biological data analysis

Algorithms used in genomics

Hands-on exercises with biological datasets

🎓 Why Take It? Ideal for beginners with a biology background looking to explore computational approaches.

📌 2. Bioinformatics for Beginners – Udemy (Free Course)

📍 Platform: Udemy 🖥️ What You’ll Learn:

Introduction to sequence analysis

Using BLAST for genomic comparisons

Basics of Python for bioinformatics

🎓 Why Take It? Short, beginner-friendly course with practical applications.

📌 3. EMBL-EBI Bioinformatics Training

📍 Platform: EMBL-EBI 🖥️ What You’ll Learn:

Genomic data handling

Transcriptomics and proteomics

Data visualization tools

🎓 Why Take It? High-quality training from one of the most reputable bioinformatics institutes in Europe.

📌 4. Introduction to Computational Biology – MIT OpenCourseWare

📍 Platform: MIT OCW 🖥️ What You’ll Learn:

Algorithms for DNA sequencing

Structural bioinformatics

Systems biology

🎓 Why Take It? A solid foundation for students interested in research-level computational biology.

📌 5. Bioinformatics Specialization – Coursera (UC San Diego)

📍 Platform: Coursera 🖥️ What You’ll Learn:

How bioinformatics algorithms work

Hands-on exercises in Python and Biopython

Real-world applications in genomics

🎓 Why Take It? A deep dive into computational tools, ideal for those wanting an in-depth understanding.

📌 6. Genomic Data Science – Harvard Online (edX) 🖥️ What You’ll Learn:

RNA sequencing and genome assembly

Data handling using R

Machine learning applications in genomics

🎓 Why Take It? Best for those interested in AI & big data applications in genomics.

📌 7. Bioinformatics Courses on BioPractify (100% Free)

📍 Platform: BioPractify 🖥️ What You’ll Learn:

Hands-on experience with real datasets

Python & R for bioinformatics

Molecular docking and drug discovery techniques

🎓 Why Take It? Learn from domain experts with real-world projects to enhance your skills.

🚀 Final Thoughts: Start Learning Today!

Bioinformatics is a game-changer in modern research and healthcare. Whether you're a biology student looking to upskill or a tech enthusiast diving into genomics, these free courses will give you a strong start.

📢 Which course are you excited to take? Let me know in the comments! 👇💬

[spacetimewithstuartgary]

New data model paves way for seamless collaboration among US and international astronomy institutions

Software engineers have been hard at work to establish a common language for a global conversation. The topic—revealing the mysteries of the universe. The U.S. National Science Foundation National Radio Astronomy Observatory (NSF NRAO) has been collaborating with U.S. and international astronomy institutions to establish a new open-source, standardized format for processing radio astronomical data, enabling interoperability between scientific institutions worldwide.

When telescopes are observing the universe, they collect vast amounts of data—for hours, months, even years at a time, depending on what they are studying. Combining data from different telescopes is especially useful to astronomers, to see different parts of the sky, or to observe the targets they are studying in more detail, or at different wavelengths. Each instrument has its own strengths, based on its location and capabilities.

"By setting this international standard, NRAO is taking a leadership role in ensuring that our global partners can efficiently utilize and share astronomical data," said Jan-Willem Steeb, the technical lead of the new data processing program at the NSF NRAO. "This foundational work is crucial as we prepare for the immense data volumes anticipated from projects like the Wideband Sensitivity Upgrade to the Atacama Large Millimeter/submillimeter Array and the Square Kilometer Array Observatory in Australia and South Africa."

By addressing these key aspects, the new data model establishes a foundation for seamless data sharing and processing across various radio telescope platforms, both current and future.

International astronomy institutions collaborating with the NSF NRAO on this process include the Square Kilometer Array Observatory (SKAO), the South African Radio Astronomy Observatory (SARAO), the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), and Joint Institute for Very Long Baseline Interferometry European Research Infrastructure Consortium (JIVE).

The new data model was tested with example datasets from approximately 10 different instruments, including existing telescopes like the Australian Square Kilometer Array Pathfinder and simulated data from proposed future instruments like the NSF NRAO's Next Generation Very Large Array. This broader collaboration ensures the model meets diverse needs across the global astronomy community.

Extensive testing completed throughout this process ensures compatibility and functionality across a wide range of instruments. By addressing these aspects, the new data model establishes a more robust, flexible, and future-proof foundation for data sharing and processing in radio astronomy, significantly improving upon historical models.

"The new model is designed to address the limitations of aging models, in use for over 30 years, and created when computing capabilities were vastly different," adds Jeff Kern, who leads software development for the NSF NRAO.

"The new model updates the data architecture to align with current and future computing needs, and is built to handle the massive data volumes expected from next-generation instruments. It will be scalable, which ensures the model can cope with the exponential growth in data from future developments in radio telescopes."

As part of this initiative, the NSF NRAO plans to release additional materials, including guides for various instruments and example datasets from multiple international partners.

"The new data model is completely open-source and integrated into the Python ecosystem, making it easily accessible and usable by the broader scientific community," explains Steeb. "Our project promotes accessibility and ease of use, which we hope will encourage widespread adoption and ongoing development."

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