You want to learn data science. How do you find the right resources?

Several opportunities to learn. Here are a few: - SuperDataScience team's courses in ML and AI on Udemy - Jose Portilla's courses on Udemy - freedCodeCamp - YouTube resources like those from Jon Krohn, James Briggs, Andrej Karpathy, etc. - edX - Coursera - Andrew Yang's courses - FastAI (and Jeremy Howard's videos) As you build your theoretical knowledge in these fields, you will layer that with practical implementations from the same instructors. Also search for relevant opportunities to practice what you learn through platforms like Kaggle, Zindi, and others. After that - you have another world waiting for you: - Cloud Computing for ML (AWS, Azure, Google) - Software Development (mid-level) - CI / CD - MLOps (if you're ambitious enough)

To effectively learn data science, it's crucial to define SMART (Specific, Measurable, Achievable, Relevant, Time-bound) objectives.

To start with Data Science, you just need to get comfortable with a few basic things. First, you should understand Probability & Statistics because they're super important. Then, you'll need to know about data literacy. After that, you can start learning programming skills like Python or R. Depending on how well you understand these skills, you can figure out your path in Data Science from there.

You need to know that data science is not a single skill but a collection of: 1. Statistics 2. Data visualisation 3. Machine Learning 4. Domain expertise 5. Brainstorming Do little by little in each of these and that would make growth really fast.

Identifying your preferred learning style is essential for maximizing the effectiveness of your data science education. Let's dive into how understanding your learning preferences can guide you in selecting the right resources. As a budding data scientist, you may be eager to specialize in machine learning for healthcare applications. To develop expertise in healthcare data analysis and predictive modeling, it is important to set clear goals. By doing so, you can focus your search on resources that are specifically tailored to healthcare data and machine learning techniques, ensuring a targeted and efficient learning process.

Back in college, I came across a promotion for the book “Data Science from Scratch” by O’Reilly. At first, I was pretty skeptical about learning coding and data science from a book and even after reading it I still didn’t think it was time well spent ! But as time went on, I realized that I remembered a lot more from this book than from any article or notebook. Why ? a couple thoughts: 1. Reading seems to make things stick in your brain better than just watching a video or skimming through a blog post, even if it doesn’t feel as exciting in the moment. 2. A book covers a bunch of different topics and shows you how they all fit together. When you learn about these topics from different places, you might miss out on the big picture.

Finding the right resources for learning data science depends on your preferred learning style: Visual learners: Benefit from resources such as video tutorials, interactive visualizations, and infographics that explain concepts visually. Auditory learners: Thrive with audio-based resources like podcasts, audiobooks, and lectures, as they learn best by listening. Kinesthetic learners: Learn effectively through hands-on activities like coding exercises, real-world projects, and workshops that involve applying their knowledge.

If you want things to go by your pace then better to read articles, concept summaries, documentations etc. If you want guided but uncontrolled tutoring, then video lectures would be better. If you are someone who likes to read in-depth concepts then go for books. But in all methods, consider it mandatory to do projects. Nothing bests them.

Ensuring the quality and credibility of resources is crucial in data science. When evaluating the reliability of learning materials, consider key factors. If you are a hands-on learner who learns better through practical application, consider using resources that provide interactive coding exercises and real-world datasets for analysis. This way, you can improve your understanding and retention of data science concepts by aligning your learning style with resources that emphasize hands-on experience.

Everyone has a different style, pace, and preferences of learning, with some preferring reading, others videos, and some podcasts. Recognizing and embracing your unique learning style in data science enhances understanding and retention, keeps you engaged, accelerates progress, improves problem-solving, allows for a customized learning path, and minimizes frustration, making your educational journey more effective and enjoyable.

Finding reliable resources is crucial for successful data science learning. Here's how to evaluate quality and credibility: Author credentials: Check the instructor's expertise and experience in data science. Look for industry professionals or academics with a strong track record. Content accuracy: Ensure the content is up-to-date and aligns with current data science practices. Look for resources that reference reputable sources and best practices. Community feedback: Read reviews and ratings from previous learners to gauge their experience and the resource's effectiveness. Learning outcomes: Understand the learning objectives and assess if the resource aligns with your specific goals and learning style.

Quality and credibility in learning resources are crucial for effective data science education: 1. Accurate Knowledge: Ensures correct, current data science insights. 2. Effective Learning: Quality instructors enhance concept understanding. 3. Reputation: Reputable sources enhance career credibility 4. Time Efficiency: Reduces re-learning from outdated or incorrect content. 5. Practical Application: Offers real-world context for better comprehension. 6. Network Access: Provides professional networking opportunities. 7. Critical Thinking: Promotes in-depth understanding and analysis. Selecting reputable, high-quality resources is key to a robust data science education.

In the vast landscape of data science resources, asking for feedback and recommendations can provide valuable guidance. By leveraging the insights of others, you can streamline your search for the best learning materials. When searching for data science tutorials and courses online, you will come across many options that claim to be authoritative sources. However, it is important to evaluate each resource carefully. Check the credentials of the authors, assess the comprehensiveness of the content, and verify the timeliness of the information. By conducting thorough evaluations, you can ensure that the resources you choose meet the highest standards of quality and credibility.

Go to platforms like LinkedIn, Kaggle and talk to professionals or anyone you look up to. I have done this and still do this, and this helps a lot. And make sure to not rely on a single opinion, take a few and then do what suits you best.

Here's how you can seek feedback and recommendations: Online communities: Join data science forums and discussion groups on platforms like Reddit, LinkedIn, and Stack Overflow to connect with other learners and experts. Ask for recommendations and share your experiences. Data science mentors: Seek guidance from experienced data scientists through mentorship programs or personal connections. Industry events: Attend conferences, meetups, and workshops to network with data science professionals and learn about the latest trends and resources. Social media: Follow data science influencers and organizations on Twitter, LinkedIn, and YouTube for regular updates on new resources and learning opportunities.

Embracing experimentation and adaptation is crucial for continuous improvement on your data science journey. Let's explore how flexibility and open-mindedness can optimize your learning experience. Imagine joining online forums and communities dedicated to data science enthusiasts. Engage with experienced practitioners who offer recommendations based on their learning journeys. Their insights can lead you to discover hidden gems, such as lesser-known blogs or forums, enriching your learning experience with diverse perspectives and resources.

Actively participate in online forums, attend meetups, and join relevant social media groups where data scientists share insights, resources, and advice. Additionally, seek out mentors or experts in your desired domain, such as computer vision research, and solicit their feedback on recommended learning materials and approaches. For instance, in my previous experience in computer vision research, I regularly sought feedback from senior researchers and practitioners in the field. By leveraging their expertise and insights, I gained valuable recommendations for textbooks, online courses, research papers, and software tools tailored to my learning goals and interests in computer vision.

In my experience, it always helps to participate in data science communities on platforms like Stack Overflow, Reddit (r/datascience), and LinkedIn groups. Engaging with the community allows you to seek advice and share experiences. Explore tutorials and documentation of relevant tools and libraries such as Python, R, Pandas, NumPy, Scikit-Learn, and TensorFlow. Official documentation is often comprehensive.

Data science is a hands-on field that requires practical experience. Here's how: Personal projects: Work on personal projects that interest you, such as analyzing a dataset related to your hobby or building a machine learning model to solve a personal problem. Kaggle competitions: Participate in data science competitions on Kaggle to test your skills, learn from other participants, and gain valuable experience. Open-source projects: Contribute to open-source data science projects on GitHub to collaborate with other developers, learn new techniques, and build your portfolio. Data science hackathons: Participate in hackathons to work on real-world data challenges, learn from mentors, and network with other data scientists.

Experimentation and adaptation are key in data science, especially when working with real-world data. Engaging with real-world data through hands-on practice exposes you to unique challenges and complexities that theoretical studies can't fully prepare you for. This is where the value of personal projects becomes evident. For example, in my projects on detecting fake news using advanced machine learning techniques and generating images with GANs, I learned crucial lessons in model selection, data preprocessing, and iterative refinement. These experiences not only sharpened my technical skills but also improved my problem-solving abilities, teaching me to approach data science projects with flexibility and creativity.

In your pursuit of mastering data science, you come across various learning resources. These resources include textbooks, online courses, and coding challenges. To gauge their effectiveness, you experiment with different resources instead of sticking to a single method. Through this iterative process of experimentation and adaptation, you refine your learning strategy and discover the most efficient pathways to enhance your data science skills.

Apply (LAS) principle everyday! 1.Learn: Learn from structured courses covering topics like machine learning, statistics, and data visualization. 2.Apply: Practice coding and data analysis with real-world datasets through projects and competitions on platforms like Kaggle. 3.Share: Write blog posts or create videos to share your insights, experiences, and learnings with others.

1. **Master of Information and Data Science (MIDS) at UC Berkeley School of Information**: Offers a comprehensive program for advanced learning. 2. **Codementor and Topcoder**: Platforms that provide opportunities for hands-on learning and mentorship. 3. **Analytics Vidhya and KDnuggets**: Websites offering valuable insights, articles, and resources for data science enthusiasts. 4. **"The Drunkard's Walk" by Leonard Mlodinow**: A book recommended for those interested in data science. 5. **Machine Learning Course by Stanford University**: A course that can provide a solid foundation in machine learning. 6. **Intellipaat, edX, Google Cloud, and Khan Academy** 7. **edX, Coursera, DataCamp, Udacity, and Udemy**