What are the best ways to learn statistics on your own?

To learn statistics independently, leverage online courses (Coursera, Khan Academy), recommended books (like "Naked Statistics" by Charles Wheelan), and solve practice problems. Use statistical software (R, Python) for hands-on learning, engage in online communities for discussion, and apply concepts to real-world scenarios. YouTube channels (StatQuest) and podcasts ("Data Skeptic") offer accessible explanations. Consistent practice and diverse resources are key to mastering statistics on your own.

Firstly, start by grasping the fundamental concepts such as probability theory, descriptive statistics, and inferential statistics. Additionally, practice solving problems regularly to reinforce your understanding and gain proficiency in applying statistical methods. Secondly, immerse yourself in practical applications by analyzing real-world datasets. Platforms like Kaggle provide access to various datasets and competitions where you can apply statistical techniques to solve problems and learn from others' approaches. Furthermore, consider using statistical software such as R or Python with libraries like NumPy, pandas, or SciPy to perform data analysis and visualization, enhancing your practical skills.

1. Set clear goals: Determine why you want to learn statistics and what concepts and techniques you need to focus on. 2. Choose resources wisely: Select high-quality and relevant resources that suit your learning style and goals. 3. Learn by doing: Practice applying statistics to real-world problems and data sets using tools like Excel, R, Python, or online calculators. 4. Review and revise: Regularly refresh your memory and consolidate your understanding of statistics through methods like flashcards, quizzes, and summaries. 5. Seek feedback and support: Engage with online communities, find mentors or peers, and attend workshops or conferences to get advice and assistance.

Your goals should not only be well-defined, but they should ideally be in response to some task or project you want to complete. Learning statistics cold-turkey with no intention of applying it will lead you to forget it within a few weeks.

To effectively learn statistics on your own: 1. Online Courses: Utilize platforms like Coursera or Khan Academy for structured lessons. 2. Textbooks and E-books: Start with basic texts, then progress to advanced material. 3. Software Practice: Learn and practice with statistical software like R or Python. 4. Kaggle Competitions: Apply skills on real data through Kaggle. 5. Study Groups: Join forums or groups to discuss and clarify concepts. 6. Personal Projects: Implement statistics in personal or open-source projects for hands-on experience. 7. Blogs and Podcasts: Follow experts for updates and insights. 8. Regular Review: Consistently review concepts to reinforce understanding.

Learn by doing… Read, do the exercises, use online resources, engage with others ?? There are many spheres of statistics: Frequentist, Bayesian, and my favourite Probability Theory ??♂? I recommend starting with the basics I.e. some beginner course you can find online or a textbook recommendation by someone highly familiar with the topic… During this process ask yourself at each topic “where is this used?” and follow up with that, go out and seek these and list them out (don’t go into the details just yet)… Second step would be diving into the new listed content and repeating

Learn by doing! Dive into statistics by actively engaging in exercises, leveraging online resources, and participating in discussions with fellow enthusiasts. Explore various statistical spheres such as Frequentist, Bayesian, and Probability Theory to broaden your understanding and find your niche. Begin with foundational concepts through beginner courses available online or textbooks recommended by seasoned experts. Embrace a hands-on approach, applying statistical methods to real-world scenarios, and seeking feedback to reinforce learning. Remember, curiosity and persistence are key on your journey to mastering statistics!

Some resources: Mathematical Statistics - John Rice Truth About Linear Regression - Cosma Shalizi Regression Analysis - Seber and Lee (very hard) A First Course in Bayesian Statistics - Peter Hoff

If you're diving into statistics, a cool starting point is hitting up some introductory courses. ?? You can check out platforms like Coursera, Udemy, or edX; they've got loads of free or budget-friendly courses. They'll walk you through the basics of Statistics such as – measures of central tendency, variability, probability, and even some hypothesis testing. Personally, I love soaking up knowledge from YouTube, blogs, and Stack Overflow as they are well-reviewed, updated, and engaging. ???? Many people find statistics challenging, but trust me, it's the cornerstone if you learn by doing. ????

Consider interactive learning: While traditional resources like textbooks are valuable, consider incorporating interactive platforms like Khan Academy or StatQuest. These offer engaging exercises and visualizations that can solidify your understanding.

It involves actively engaging with real datasets, utilizing tools like R, Python, undertaking projects, participating in competitions like those on Kaggle, simulating experiments, leveraging online courses with practical assignments, joining study groups or forums, keeping a learning journal, & applying statistics to personal interests. This approach emphasizes the practical application of statistical concepts through hands-on experience, enhancing understanding & skills in real-world scenarios. By integrating statistics into various aspects of learning & applying it to areas of personal interest, learners can make the subject more engaging & relevant, thereby solidifying their grasp of statistical methods & their applications.

To deepen understanding and enhance skills, it is crucial to apply concepts to real-world data. To gain insight and test your knowledge, use tools like Excel, R, Python, or online calculators, and engage with datasets from Kaggle or UCI.

Hands-On Experience: Gain practical experience by working on real-world problems and datasets. Reinforce Understanding: Applying knowledge helps solidify concepts and techniques in your mind. Skill Development: Practice enhances your statistical skills and proficiency. Discover Insights: Working with data allows you to uncover patterns and draw meaningful conclusions. Tool Utilization: Utilize software tools like Excel, R, Python, or online calculators to implement statistical analyses. Data Sources: Find or create datasets to practice with, utilizing platforms like Kaggle, UCI, or Google Dataset Search. Experimentation: Explore different approaches and techniques through hands-on experimentation to deepen your understanding.

Learning statistics by doing is crucial for practical mastery. Applying statistical concepts in real-world scenarios enhances understanding and problem-solving skills. Hands-on experience fosters a deeper connection to theoretical knowledge, promoting retention and practical application. Through projects and exercises, learners develop the ability to interpret and communicate statistical findings effectively. Ultimately, learning statistics by doing not only solidifies theoretical concepts but also prepares individuals for data-driven decision-making in diverse fields.

Apply theoretical concepts to practical problems by working on hands-on projects or case studies. Use statistical software like R or Python with libraries such as Pandas or NumPy to analyze data and implement statistical techniques. Experiment with different datasets and scenarios to deepen your understanding and build confidence in applying statistical methods.

It's crucial to ensure that each revision session is not just a mere repetition but an opportunity to add new layers of understanding - This process is similar to learning a language. In this spirit, I facilitate a reading group that examines new research topics within the quantitative industry. It embodies the essence of effective learning in statistics: a perpetual cycle of review and enhancement. We reinforce our core statistical knowledge and, more importantly, build upon it, making every learning moment count. This way, the process of revisiting and revising becomes a powerful tool for growth, pushing us to constantly evolve and adapt in our ever-expanding field of study.

Cumulative Nature: Recognize that statistics requires ongoing review because it builds upon prior knowledge. Consolidate Concepts: Go over important ideas and methods again to make sure you understand them. Review Methods: To effectively review, use a variety of strategies such as mind maps, summaries, quizzes, and flashcards. Self-Testing: Test your understanding and memory by posing tough questions to yourself. Teach Others: To solidify your knowledge and pinpoint areas in which you still need to improve, explain statistical concepts to another person. Frequent Practice: To ensure that the information stays fresh in your memory, plan frequent review sessions.

?? Work with real data! Grab datasets from Kaggle, Google Dataset Search, or use your own. Practicing with actual data provides invaluable hands-on experience. Use libraries like NumPy, Pandas, or Scikit-learn to apply statistical methods. Hands-on practice enhances understanding and retention. Regularly review and reinforce your knowledge. Create summary notes, flashcards, or mind maps to revisit key concepts. Consistent revision helps solidify your understanding and prepares you for more advanced topics. Many find statistics challenging, but trust me, it's the cornerstone if you learn by doing. ????

In order to master statistics, it is important to review and revise previous knowledge. Reinforce concepts with flashcards, quizzes, summaries, or mind maps. To solidify your understanding, ask yourself tough questions or teach others.

Regularly review and reinforce your knowledge through practice quizzes, self-assessments, or peer feedback. Identify areas of weakness or misunderstanding and revisit relevant resources to clarify concepts. Stay updated with new developments and advancements in statistics through continued learning and engagement with the community.

Online Communities: Engage with platforms like Reddit, Stack Exchange, or Discord to connect with others learning or teaching statistics. Ask Questions: Don't hesitate to ask questions when you're unsure or need clarification on statistical concepts. Share Ideas: Contribute to discussions by sharing your insights and experiences with fellow learners. Advice and Guidance: Seek advice and guidance from mentors, tutors, or peers to overcome challenges and achieve your learning goals. Peer Support: Connect with peers who are also learning statistics to exchange knowledge, provide mutual support, and collaborate on projects. Feedback Loop: Utilize feedback from others to identify areas for improvement and enhance the understanding of statistics.

Once you've built a strong base in descriptive statistics, dive into inferential statistics. Master probability, hypothesis testing, and regression analysis for a comprehensive understanding. Keep the practice going! The field evolves, so staying current is key. Attend workshops, conferences, and online seminars to sharpen your skills. While many find statistics challenging, trust me, it's the cornerstone if you learn by doing. ????

Learning statistics doesn't mean going at it alone. Get feedback, questions, and advice from online communities like Reddit or Discord. For guidance through challenges and achieving goals, seek out mentors or peers.

If you understand it logically then you can do it magically!!! One of the best ways to learn statistics is to apply the concepts in day-to-day life. Once you start applying you will enjoy it and learning will be fun. You can apply it to compare any two products of the same type. You can apply the concepts of mean, variance and spread while reading online reviews of any product on Amazon or a service on Google.

If you wake up everyday thinking “ugh I HAVE to do statistics today” it will be 10x more miserable than saying “Nice, I have time to review some stats today” Shift your perspective.

??Motivation > Method The best course in the world is useless if you don't engage. Picking projects that genuinely excite you helps statistics feel less like homework and more like exploration. ?Data Storytelling's Fuel Those who love finding trends in data will enjoy statistics the most. It's about the thrill of going past raw numbers and seeing the patterns and possibilities they hold. This investigative skill drives excellent data scientists. ?The "Aha!" Moment There's nothing like cracking a statistical concept and "realizing" how it applies to the messy world. Maybe hypothesis tests finally clicked while comparing website changes. These small wins solidify your knowledge way better than rote memorization.

Lasst euch nicht entmutigen, wenn etwas nicht auf Anhieb klappt oder verst?ndlich ist. Neue Synapsen zu bilden braucht Zeit, aber der Weg zum Ziel bietet viele kleine Erfolgserlebnisse. Und ihr werdet sehen, je gr??er das Wissen über Statistik und Zusammenh?nge wird, umso mehr Sinn ergeben viele Dinge egal ob beruflich oder privat. Statistik Stück für Stück zu verstehen verleiht einen geweiteten Blick auf die Welt.

Apply - Ask - Re-Apply. What's better than learning a skill while applying it on something you are interested in? Like football? Try Poissonian modelling on the goals. Like fintech? Try Laplacian modelling on return of stock prices. The key really is to find something you are interested to get a head start. Once you have realised that what you have learnt might not be good enough, imagine what you could potentially add on. If we can model change in stock price using laplacian distribution, can we do something similar on volatility? how can that feed back to our laplacian model? Before you know, you will start venturing deeper into the statistics.

Celebrate your progress: Don't forget to acknowledge your achievements, no matter how small. Learning statistics takes time and effort, so celebrate each milestone to stay motivated and engaged.

The advantage of learning statistics on your own is you can break the stigma of p-value dominance. Most stats courses will be taught from the perspective of "significance this, significance that". In reality, you are talking about relative rarity. The significance threshold is just that: a threshold. It can be played with, just not post hoc. Apart from that, focus on the why of statistics, not necessarily on the what of statistics. It is more important to know how tests pair to scenarios than it is to know how to calculate something. When you look at how to calculate something, use that as a way to eavesdrop in to why the decision of the test itself is made.