You want to learn data science remotely. What are the best resources available?

In my personal opinion, there are multiple options for learning fundamental data science concepts, terms, glossaries for tools like Microsoft Excel, SQL, Python and others for understanding statistics, probability, machine learning, data visualization by familiarizing any learner with essential data science basics. Platforms like Coursera, Udemy & YouTube offer course material for data science learning resources for Python Programming; Data Science Fundamentals; MySQL Basics for Data Analysts; Data Science projects among others. For learners interested in Google’s Data Analytics Professional Certification on Coursera, you can access courses for free & one can pay a small fee for the completion certificate to demonstrate proficiency.

There are several highly-rated online courses available for data science, offered by reputable platforms and universities. Here are some of the best ones: 1. Coursera 2. edX 3. DataCamp 4. LinkedIn Learning 5. Kaggle 6. Udemy 7. Udacity These courses cover a wide range of topics in data science, including programming languages like Python and R, data analysis, machine learning, data visualization, and more. Depending on your interests and skill level, you can choose the course that best suits your needs and goals. Additionally, many of these platforms offer certificates upon completion, which can be valuable for showcasing your skills to potential employers.

Learning Data Science involves two main aspects. First, understanding the technical side from basic to advanced levels. Second, staying updated on the latest developments and impactful projects in the field. To grasp the technical aspects, online courses are crucial, with Google's specialized programs being particularly beneficial. These courses offer well-structured content covering a wide range of topics. Additionally, keeping up with digital media is important to stay current in Data Science. Following industry podcasts and YouTube channels can be very effective for this purpose. Ultimately, mastering Data Science involves a dual journey of learning and applying skills to meaningful projects that create value.

Online learning democratizes data science education, offering vast resources tailored to diverse learning styles. The key is to curate your curriculum, focusing on courses that offer practical projects and peer interaction. This approach mitigates drawbacks like content overload and fosters a disciplined, community-centric learning experience. Embrace the flexibility, but also seek out mentorship and real-world application to truly master data science. There many popular platforms are available in the market paid and free as well My personal favourite free resources are YouTube , Coursera(deeplearning . ai),kaggle,stackoverflow and github and for paid one i think 365datascience is best platform to learn Data science

Projects! After you have learned the basics of Python, pandas, numpy, and model building, pick a dataset from Kaggle, UCI ML repository, etc.. and start performing EDA & Model Building by reading already submitted notebooks, youtube, etc. This will stop you from getting into the tutorial trap and give you a better understanding of the challenges faced during solving real-world projects. Meanwhile, you can keep learning by : 1. Reading only articles (Medium, Analytics Vidhya, Towards Data Science) 2. Podcasts (Data Stories, Data Framed, Data Skeptic) 3. Coursera, Udemy, Udacity, EDX provides free courses on ML, DL, NLP, Math

I bought and read the Python book. However, I think you need to buy about 5 books to understand it. The reason is that the books are divided into beginner, intermediate, and advanced levels. You should buy one book each for beginner, intermediate, and advanced. You may also want to read PDF files or make use of audiobooks. Subscribe to newsletters to stay up-to-date. You may want to join and discuss with the community.

I have tried both the things 1. Books 2. Courses+Articles A book takes a longer time to cover, I would suggest that you take up the 2nd approach. Take some Statistics and Basic ML course and then move to Online articles for further learning.

Books and blogs are invaluable for self-paced learning in data science. They offer depth and breadth, from foundational concepts to advanced techniques. While they may lack interactivity, they compensate with rich narratives that can clarify complex ideas. To stay current, complement them with interactive platforms and community discussions. This multifaceted approach ensures a robust and up-to-date understanding of the field. I believe books is invaluable asset in our life Making notes while watching data science lectures is also helps as notes (you can call this your own handmade book??) There are many books in market but O'Reilly publication have done amazing job in this domain

For books, "Data Science for Business" by Foster Provost and Tom Fawcett provides practical insights into applying data science techniques in real-world business scenarios. It offers a comprehensive overview of data science concepts, methodologies, and case studies, making it an invaluable resource for both beginners and experienced practitioners. As for blogs, "Towards Data Science" on Medium offers a wealth of articles, tutorials, and insights from industry professionals and researchers in the field of data science. It covers a wide range of topics, including machine learning, data visualization, and deep learning, providing valuable learning resources and practical advice for aspiring data scientists.

Books and blogs are excellent resources for learning data science remotely. Titles like "Data Science for Dummies" and "Python for Data Analysis" provide clear explanations and practical examples to help you grasp key concepts. Meanwhile, blogs like "Towards Data Science" offer insights from industry experts and cover the latest trends and techniques in data science. While these resources lack interactivity, they offer valuable knowledge and inspiration. Just be mindful of outdated information and the need for some prior understanding of the subject matter.

Exploring podcasts and videos provides a third avenue for remote learning in data science. Platforms like DataFramed, Data Skeptic, Linear Digressions, Data Stories, and TED Talks feature insights, experiences, and tips from data science experts. Engaging with podcasts and videos exposes learners to diverse perspectives, ideas, and challenges in the field. Despite these advantages, drawbacks include the potential lack of depth, the demand for focused attention, and challenges in finding reliable and relevant sources.

I would recommend podcasts. A cybersecurity expert I know who is an instructor at Udemy also uses podcasts to teach his students. In my experience, podcasts feature the same functionality as listening to audio from an audiobook in a web browser.

Positive Aspects: ? Accessibility: Many podcasts and videos are free and easily accessible online. ? Flexibility: You can listen to podcasts or watch videos while multitasking, making it a convenient way to learn. ? Engaging: Visual and auditory content can be more engaging and easier to digest for some learners. Negative Aspects: ? Lack of Depth: These formats may not always dive deep into technical details, offering a more general overview instead. ? Passive Learning: Similar to books, these formats are generally more passive and may not be ideal for mastering complex concepts. The best approach often involves combining multiple resources to benefit from their strengths while mitigating their weaknesses.

Podcasts and videos are a dynamic way to absorb data science knowledge. They bring expert voices to your ears and visual learning to your screen, offering a personal touch to complex topics. While they may not dive deep, they're perfect for on-the-go learning and sparking new ideas. To enhance depth, pair them with hands-on projects or discussions in data science forums. This multimedia approach enriches learning, making it both accessible and practical. But until you do practice on your own after watching video it will not help you,just watching video tutorials hours after hours without any practice are not considered as good practice at all .

Podcasts and videos are fantastic resources for learning data science remotely. Shows like DataFramed and Data Skeptic feature industry experts sharing valuable insights and tips. Similarly, TED Talks offer thought-provoking discussions on data science topics. While these resources lack depth, they provide diverse perspectives and real-world examples. Just be mindful of the need for attention and the importance of verifying the credibility of the sources you choose.

Projects and competitions is the best choice to learn Data science remotely. It helps you learn faster and practice directly.I think practice is more insightful than courses .

Projects and competitions are the battlegrounds of data science learning. They push you beyond theory into the realm of practical problem-solving. Yes, they demand motivation and resilience, but the rewards are tangible: hands-on experience, peer feedback, and a portfolio that speaks volumes. Embrace the challenges; they're not just obstacles but stepping stones to mastery and recognition in the data science community. Dive in, learn, iterate, and emerge victorious with newfound skills and confidence.But only relevant projects will help you

Engaging in data science projects & competitions is an excellent way to apply theoretical knowledge to real-world scenarios & enhance practical skills. For example, participating in Kaggle competitions allows you to work on challenging data science problems & collaborate with a global community of data scientists. Additionally, undertaking independent projects, such as analyzing public datasets to uncover insights or building predictive models for specific domains like healthcare or finance, provides valuable hands-on experience & demonstrates your capabilities to potential employers or collaborators. These projects not only showcase your skills but also offer opportunities for continuous learning & growth in the field of data science.

Another excellent way to learn data science remotely is by engaging in projects and competitions available on platforms like Kaggle, DrivenData, and DataQuest. These platforms offer a wide range of tasks, from simple exercises to complex challenges, allowing you to apply your skills in real-world scenarios. Working on projects and competitions provides valuable hands-on experience, feedback, and recognition in the field of data science. However, it's important to stay motivated and patient, as some tasks may be challenging and dependent on data quality and availability.

Hands-on projects and competitions are crucial for developing practical skills and gaining real-world experience in data science. Platforms like Kaggle, DrivenData, and TensorFlow Playground offer various datasets and challenges to work on, ranging from beginner to advanced levels. Additionally, you can find open-source projects on GitHub, Kaggle or contribute to collaborative data science projects on platforms like Codefree.

For networking and community involvement, Linkedin is the way to go. Linkedin is a business-focused social media platform. An online search will turn up results with 400 million users or even 900 million users. In my experience, networking is a certificate of completion. Data Science, Data Analyst, Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, and Generative AI. It has allowed me to expand my network of contacts. As your networking grows, you can get invitations to join communities and participate in them.

Another valuable resource for learning data science remotely is to engage with communities and networks dedicated to the field. Platforms like Data Science Central, Data Science Network, and Women in Data Science offer opportunities to connect with fellow learners, mentors, experts, and potential employers. These communities host discussions, events, webinars, mentoring programs, and job postings, providing support, guidance, and networking opportunities in the field of data science. However, active participation is key, and it's important to be mindful of potential spam and to find communities that align with your interests and goals.

O aprendizado em comunidade é uma das grandes maneiras de desenvolver conhecimentos. O próprio LinkedIn possui várias comunidades para você conhecer e desenvolver habilidades de ciências de dados. Sugiro também seguir pessoas que produzem conteúdo sobre o assunto aqui no LinkedIn

Unirse a comunidades en línea como Data Science Central y Reddit's r/datascience permite interactuar con otros profesionales, hacer preguntas y compartir recursos. Estas comunidades son útiles para establecer conexiones profesionales, obtener orientación y colaborar en proyectos. Además, participar en grupos de LinkedIn relacionados con la ciencia de datos permite ampliar tu red profesional y acceder a oportunidades laborales y recursos de aprendizaje adicionales. Estas redes sociales son útiles para mantenerse conectado con la comunidad y recibir apoyo y orientación de otros profesionales.

Connect with other aspiring and professional data scientists online or through local meetups. Share knowledge, ask questions, and get valuable insights from others in the field.

Data science requires statistics. Besides that, you need to be able to use tools, hone your Python skills, and compile reports. The main goal is to be able to produce statistics.

Do not undervalue the importance of practical experience, even when online courses are beneficial. Take on real-world initiatives, regardless of their size. Nothing helps ideas stick like practical practice.

Consider your learning style: Do you prefer structured courses, hands-on projects, or a mix of everything? Tailor your approach to what works best for you. Start with the fundamentals: Before diving into complex algorithms, make sure you have a solid grasp of statistics, programming, and the core data science concepts. Be persistent: Data science takes time and dedication to master. Don't get discouraged by setbacks, and keep practicing!

DataCamp y Kaggle Learn obtendras una experiencia práctica mientras aprendes. Estas plataformas ofrecen cursos interactivos que incluyen videos, ejercicios prácticos y proyectos que te permiten aprender haciendo. Además, sigue a expertos en redes sociales como Medium, C, LinkedIn para recibir consejos y trucos útiles sobre herramientas y técnicas. Muchos expertos comparten recursos, tutoriales y consejos prácticos en sus perfiles sociales, lo que puede ser útil para mejorar tus habilidades y mantenerte actualizado en el campo de la ciencia de datos y sobresalir en cualquier área de tu organización.

Supplement your learning with online articles and tutorials offering specific data science hacks and shortcuts. This can help you streamline your workflow and become more efficient.

You might need to have a multi pronged approach: 1. Read blogs and listen to YouTube for updates and news and how-to hands-on capabilities for the latest technologies 2. Start an online course or sign up for a online college/ masters program 3. Get hands on yourself with projects and applying and testing what you learn on the latest technologies like notebooks from the YouTube videos, and the more formal and complete education programs 4. Get with peers and mentors to understand and unpack trends controversies etc

One crucial aspect to consider in the journey of learning data science is the importance of building a strong foundation in mathematics and statistics. While proficiency in programming languages like Python or R and familiarity with machine learning algorithms are essential, a solid understanding of mathematical concepts such as linear algebra, calculus, and probability theory forms the backbone of data science. Therefore, aspiring data scientists should devote time to strengthening their mathematical skills to effectively grasp the underlying principles and concepts in data analysis and modeling.

Online courses are available for a fee and free of charge. First, beginners are recommended to take the free course. For example, graded learning, edx Coursera and Udemy. Otherwise, LinkedIn Learning. Subscribe to our newsletter to stay up-to-date. LinkedIn’s AI’s community is Artificial Intelligence, Machine Learning, Data Science & Robotics and AIAI(Artificial Intelligence, Deep Learning, Machine Learning)

In addition to the above resources, you can employ GPTs (openai chatgpt, google bard, microsoft copilot) as mentors along your learning trip. How? Simply instruct GPT with the appropriate prompt (in this case, the persona pattern prompt) and allow it to assist you and supply you with all you need.

Given the inherently practical nature of data science, it's essential that Learning & Development be dynamic and continuously adaptive. Embracing remote learning is pivotal in this context. My strategy for staying current with technological advancements, industry benchmarks, and practical case studies includes: 1. Regularly engaging with blog posts and podcasts. 2. Participating in webinars and workshops. 3. Participate in online communities. 4. Seeking mentorship opportunities from domain experts for guidance and shared learning. 5. Actively ask for feedback and reflecting. Crucially, applying acquired knowledge through personal projects, alongside mentorship and reflective practices, is the most effective method to solidify learning.