How to Learn Data Analysis Faster.
Enhancing Your Data Analysis Learning Journey: Strategies for Success
Data analysis has become a vital skill in today’s fast-paced world where data is being generated every minute, and companies rely on their data in other to make informed decisions. Aspiring data analysts and self-learners often embark on online courses to acquire the knowledge and tools necessary to navigate the complexities of data analysis, and this is why couple of months ago, I also took an online course to become a data analyst, and the experience completely changed how I learn from these courses. Since then, I’ve gotten more out of not just data analyst courses, but all online courses. In this post, I’ll explain my learning process and how you can do the same.
You know, some people think that just buying an online course means you automatically understand everything without any effort. That’s not true. Sometimes, after finishing a course, you might feel unsure if you actually learned anything. I remember when I struggled with this. I spent a good amount of money on a course but didn’t take it seriously. I treated it like watching a movie. To make it worse, the course had a time limit. I managed to finish it, but I felt bad for not trying hard enough. I wasn’t sure if I really understood what I learned, so I had to study it all over again. That’s when I started using something called “active recall,” which I’ll explain in a bit.
Checklist Before Enrolling for A Course:
When you’re thinking about enrolling in an online course, there are a few important things you should consider to make sure you’re setting up a strong foundation for learning. Some of these things include:
1. Genuine Interest: Ask yourself why you’re taking this course. Is it because someone told you to, or do you genuinely want to develop yourself? Will the course answer your important questions? Make sure you’re excited about learning something new. Often, you can figure out the answers by carefully looking at the course outline. By examining what the course covers, you can decide if it’s worth your time and effort.
2. Setting Clear Goals: Once you’ve checked out the course content, you can create your learning goals. I usually keep it simple, focusing on just one or maybe two goals. For example, if you’re taking the IBM Data Analyst Professional Certificate course (and if you haven’t seen my review, you can find the link in the description below), you might want to ask yourself: What’s the most important skill you want to learn from this course? Imagine if you could only take away one thing from this course — what would it be? This kind of honest self-reflection is crucial. Without clear goals, it’s easy to get lost in the overwhelming amount of information in an online course.
3. Applying Active Recall: Once you’ve defined your goal and started with the course, it’s time to use active recall in your learning. This technique involves actively trying to remember information rather than just passively reading or watching. This helps you understand and remember better.
Remember, taking a moment to think about why you’re taking the course, setting achievable goals, and actively engaging with the material can make a huge difference in how much you get out of your online learning experience.
Active Recall:
This learning technique involves continuous self-testing during the learning process. Instead of passively consuming content, I began actively engaging with the material by constantly testing myself. This shift in approach made a profound difference. Not only did my understanding improve, but I also felt more confident in applying the concepts I had learned.
Active recall, the cornerstone of effective learning, entails consistently testing yourself while absorbing information. It’s a technique that promotes understanding, retention, and application. Using active recall is a smart way to study that’s backed by research. It means you keep testing yourself while you’re learning. This helps you understand and remember what you’re learning. Here’s how you can use active recall for a data analyst course:
1. Data Substitution: Instead of just using the data the course provides, find your own data sets. You can use things like your bank transactions or academic records. Replace the original data with this new data and see if you can reach the same conclusions. This makes you more engaged with the material and sparks your curiosity. You can even use this method for your final projects, changing the data sets to fit your own questions.
2. Task Comparison: Take a task or assignment and do it using different programs. For instance, if you’re asked to analyze data with Python, do it. Then do the same task using Excel. This way, you’re not only repeating the task, but you’re also learning how to solve problems in both programs. It builds your skills in both areas.
3. Task Modification: This is a bit more advanced. You change the steps of a task a bit to see how it affects the outcome. For example, if you’re asked to make a scatter plot, change it to a waterfall chart and see if you still get the same results. If it’s about coding, add or remove lines of code to see how it changes the outcome. This method helps you understand the material more deeply.
Mastering data analysis and making the most of your online learning involves practicing a lot. You personalize practice questions and exercises, and even change solutions to test yourself. This way, you keep getting better as you go along. Remember, the key is to keep practicing and testing your knowledge.
There’s one more important step you should take for every online course you complete. Create a dedicated folder on your computer for each course. Within that folder, make subfolders for each module or chapter. Save your end-of-module summaries, exercise answers, or any useful materials you come across into these folders. This will safeguard your course materials in case you lose access to them after finishing the course. Having everything organized in one place will make it easy to refer back to the course content or review your notes in the future.
To sum it all up, as a self-learner aiming to become a data analyst through online courses, active involvement in your learning process is key. Using techniques like data substitution, task comparison, and task modification while you learn is crucial. Embrace creativity and find new ways to solve both familiar and fresh challenges. When you eventually land a job, your learning won’t stop. You’ll actively seek innovative solutions for existing and novel problems alike. My hope is that I’ve provided you with a clearer understanding of how to make the most out of your online courses. I genuinely hope you find these insights valuable for your learning journey.