How do you measure your progress when following data science tutorials?

I measure progress by understanding the concepts, applying them in practical scenarios, and completing exercises. I also track the time it takes to grasp each concept and set goals for mastering techniques or finishing projects within a specific timeframe.

While studying from data science tutorials, progress can be measured by setting specific, measurable goals. Before starting, define what you want to achieve, such as mastering a new algorithm or understanding statistical concepts. Periodically review these goals to track your advancement. This structured approach keeps learning focused and ensures each tutorial aligns with your objectives.

Setting clear goals is essential to measure progress ... Define learning objectives: Before you begin a tutorial, clearly define what you want to learn or accomplish. Identify specific skills, concepts or techniques you want to understand. Track milestones: Break the learning program into smaller milestones or checkpoints. Monitor your progress as you complete each section or module to ensure you understand and can apply the concepts covered. Apply knowledge: Apply the knowledge gained in the learning program to real-world projects or scenarios. Practice coding, analyze data sets, and create AI models to reinforce your understanding and skills.

From what I've seen, setting continuous goals work for only a few highly driven people. Most others have been demotivated if they couldn't keep up with it and leave it entirely. Instead of giving yourself a deadline for many milestones within a single course, it might be better to have a blurry goal and you promise yourself to spend at least 10 minutes every day on it. This is often times better as one would have to progress everyday and this piles up over time.

Set SMART goals for your data science journey: Skill Acquisition: Learn ML algorithms, visualization libraries, and data preprocessing. Project Completion: Finish X projects in Y months, tackling various problems. Portfolio Development: Create a diverse portfolio showcasing skills and solutions. Certification: Complete courses and earn relevant certifications. Community Engagement: Engage in forums, meetups, and hackathons. Learning Hours: Dedicate Z hours weekly for study and practice.

Practice! Practice!! Practice!!! It’s the way to develop proficiency. You also develop muscle memory when you practice. It’s also a way to develop your portfolio as a beginner. Don’t get discouraged if you’re not getting it; you get better with each practice. You can also join Data Challenges.

Practicing coding is essential for reinforcing what we've learned. Track your progress by using version control systems like Git, which can help you monitor the evolution of your code over time. Engage in coding challenges on platforms such as LeetCode, Kaggle, or HackerRank to sharpen your skills. Maintain a portfolio of projects that demonstrate your abilities and the complexity of tasks you have handled. Additionally, regularly review and refactor your code to improve its quality and efficiency, ensuring you continually develop your coding skills.

Dedicate regular time slots to coding practice: Frequency: Aim for daily or regular practice sessions. Topics: Focus on data structures, algorithms, and libraries relevant to your goals. Challenges: Solve coding challenges on platforms like LeetCode or HackerRank. Projects: Work on personal or tutorial-based projects to apply coding skills. Review: Analyze and refactor past code to improve efficiency and readability. Feedback: Seek feedback from peers or mentors to refine your coding skills.

To keep track of my progress, I always try to keep practicing with the codes. Moreso, it helps me improve my knowledge by doing different datasets

Translating your thoughts into code and gathering insights from data is a must for Data Scientist. You should try these steps to make the most out of it: - Modify the code: Can you change the code in the tutorial to handle different data or scenarios? This demonstrates your ability to adapt and apply the concepts. - Build your own mini-project: Once you've grasped a concept, try creating a small program that uses that concept. This reinforces your learning and expands your skillset.

In data science, it is important to understand the theory ... Comprehension check: Regularly check your understanding of the theoretical concepts presented in the tutorials. Make sure you can explain the key principles, algorithms and methods in your own words. Application skills: Test your ability to apply theoretical knowledge to practical problems or data sets. Try your hand at exercises, challenges or mini-projects that require you to apply the concepts you have learned.

One thing while understanding theory is the extent to which you try to grasp things. For all practical purposes, unless one is stats or ML major, it is okay to get a good functional theory understanding. For example, for 95% of the people knowing p value of beta coefficient in regression is enough as that serves practical purposes. Knowing what test the p value is for might not add practical value as most have an idea of less is good/bad and that helps them. While obviously it's better to know everything, since your time is limited, first focus on practical learning of 20% theory that helps you with 80% of your work. If one has time, they can venture forth into theory in depth once this stage is crossed.

Master theory by: Structured Learning: Follow a curriculum or textbook to cover foundational concepts. Active Engagement: Take notes, summarize topics, and teach them to others for deeper understanding. Practice Problems: Solve theoretical problems to apply concepts and solidify understanding. Visual Aids: Use diagrams, charts, or online resources to visualize complex theories. Discussion Groups: Join study groups or forums to discuss and clarify concepts with peers. Real-World Examples: Relate theoretical concepts to practical applications for better retention.

I strive to understand the theoretical concepts and underpinnings of whatever subject I am dealing with. I believe that understanding the theories and structure of the subject gives a foundation of how to deal with data and achieving the set objectives

For me , understand ing the the concept, which enhance the code , trying to link the code with i learned before to convert to real world application

Whenever I learn something new I always try it out. I think that's why people get into data science. After learning a new technique or skill, I give it a go until I get it to work in a real use case. Document the process then repeat the process until it's committed to memory. Develop the process and become an expert.

Applying knowledge through hands-on projects with real datasets solidifies learning and understanding data science concepts deeply. Following tutorials alone isn't enough; creatively applying concepts to your own projects and data allows you to see methods in action, face unanticipated challenges, and develop an intuitive grasp. Building mini-projects reinforces concepts and provides valuable experience in data cleaning, analysis, and handling real-world complexities. Take on personal projects or online challenges to test skills in novel scenarios - hands-on experience trumps passive following for true understanding. Applying knowledge is key to making significant strides in your data science journey.

Applying the knowledge you've gained is a vital step in solidifying your learning. Work on mini-projects that implement the concepts you have learned, allowing you to see how they function in practice. Use real-world datasets to practice data cleaning, analysis, and visualization, providing you with practical experience. Participate in data science competitions to apply your skills in a competitive and dynamic environment. Additionally, consider writing blog posts where you explain how you applied various data science techniques to solve problems, which can help deepen your understanding and showcase your expertise.

Try solving real-world problems or datasets outside the tutorial. Your ability to tackle these challenges reflects your understanding and skill level.

Regular reviews are important to ... Consolidate material: Regular review of course material helps to reinforce new concepts and techniques, deepen understanding and consolidate learning outcomes. Recognizing gaps: Frequent review sessions allow learners to identify areas of weakness or misunderstanding, enabling targeted learning and practice to effectively close these gaps. Retention and mastery: Regular repetition and reinforcement improves knowledge retention and promotes mastery of fundamental data science principles and techniques.

Regular reviews are important for tracking your learning progress. Schedule weekly reviews to evaluate what you have learned and to identify any gaps in your understanding. Create progress reports that summarize your achievements and pinpoint areas that need improvement. Based on these reviews, adapt your learning plan to better suit your needs and overcome any challenges you encounter. Reflect on the methods and resources that were most effective, ensuring you continue to use the best approaches for your learning.

doing reviews on tutorials not only helps you to understand the subject better but can also play a role in identifying areas that one might have missed to grasp during the learning process

I revisit previous tutorials and code snippets. Repetition reinforces learning and helps identify areas where my knowledge might be shaky.

Look for mentors who can provide guidance and constructive criticism. Engage in peer review sessions where you and your peers can give and receive feedback on each other’s work. Participate in online communities, such as Reddit or Stack Overflow, where you can ask questions and get feedback from a broader audience. If you’re applying data science skills in the workplace, seek feedback from colleagues or supervisors to gain insights on practical applications and areas for improvement.

Other aspects to consider ... Community: Active participation in data science forums, discussion groups or online communities allows you to share experiences, seek advice and learn from others. Participating in discussions and supporting your fellow students often is a sign of deeper understanding. Efficiency: Evaluating the amount of time and resources spent on exercises and projects can be an indication of the efficiency and effectiveness of learning. Tracking milestones: Setting milestones or checkpoints within learning programs helps to break down learning objectives into manageable tasks.

When following data science tutorials, measure progress by completing tasks such as understanding the concepts explained, replicating the code, and achieving similar results. Track your ability to apply the techniques to new datasets or problems, and assess your comprehension by explaining the concepts to someone else. Additionally, set milestones for completing sections or projects within the tutorial and evaluate your understanding and proficiency at each stage. Finally, measure progress by comparing your skills before and after completing the tutorial, noting any improvements in your ability to solve real-world data science problems.

Personalization. That the key, can you change a tutorial project to suit something local to you ? Do you understnad the code to add or remove features ? Do you see a part of the tutorial where you can have cleaner code ? This is an important benchmark because it means your not just following blindly but beginning to understand and think in the way you need too.

Understand Concepts: Ensure comprehension of key concepts before moving forward. Hands-On Practice: Complete exercises and implement code examples to reinforce learning. Evaluate Solutions: Compare your solutions to provided answers or benchmark performance metrics. Problem-Solving Skills: Assess your ability to tackle new problems independently using acquired knowledge. Portfolio Building: Document completed projects in a portfolio to showcase skills and track improvement over time.

Some tutorials offer quizzes or assessments to test your understanding. Regularly taking these can help pinpoint areas where you may need further study.