This is a data science career roadmap lecture that tells you what I have learned through trial and error as a non-major. You can optimize your career preparation time and process without wasting time or effort.
My unique selling point as a non-major
A clear understanding of data science and careers
Skillsets to Achieve Your Data Science Career Goals
Study methods to fill your skill set
Are you a non-major or have no background in data science, but still want to enter a data science career? This course is for you. This course is a data science roadmap that provides study methods and direction for non-majors to become data scientists.
As a non-major with a liberal arts background, I successfully transitioned to a career in data science and currently work as a data scientist in the UK. Recently, I've been building my data science career by running a YouTube channel and working on data science projects. While data science can be a daunting field, I'll share practical tips on how to effectively embark on a data science career.
My lectures are an investment that will eliminate wasted time and effort. By following the lectures closely, you'll learn what I've learned through trial and error, allowing you to enter data science more effectively and quickly.
As someone who didn't major in data science, I understand better than anyone the frustrations, difficulties, worries, and concerns that come with preparing for a career in data science. Based on my own experiences and lessons learned through trial and error, I'll share practical methods to help you shorten your career preparation time and optimize the process.
Are you wondering if you can do it even though you're not a data science major? Regardless of your major or background, anyone can create a roadmap for a data science career that's right for them.
At the end of each chapter, we provide practice problems that allow you to apply the lecture content to your own situations. Solving these problems will give you the opportunity to reflect on what you've learned and find the right answers for you. These practice problems also provide specific examples based on my own experiences, allowing you to apply what you've learned.
• I can make my non-major background my “Unique Selling Point” that will make me stand out from other competitors.
• You can understand exactly what data science is and set your own data science career goals considering your circumstances and preferences.
• Identify the skill sets you need to achieve your career goals and learn in detail how to study to fill those skill sets.
• We help you reduce trial and error and optimize your preparation time and process.
Learn why your major is advantageous for a data science career and use it to create a unique selling point that can be leveraged to your advantage during the job search process.
Understand data science and its career paths, and set goals for a data science career that fits your background and circumstances.
Identify the skills needed to achieve your set goals.
Learn how to study and develop a plan to develop the skills you need.
Course review one week after opening
🙋♂️ Can non-majors also take the course?
A-1. Yes. My lecture is for those who want to enter a data science career without any prior data science background. Even those with prior data science knowledge who are unsure of how to begin preparing for a data science career can take my lecture and effectively prepare for their career.
🙋♂️ Is there anything I need to prepare before attending the lecture?
A-2. While there's no need to prepare in advance, a proactive approach is essential to effectively utilize the lectures. By solving the practice problems I provide for each chapter, you'll not just passively listen to the lectures, but also reflect on what you've learned. Apply the practice problems to your own situation, find the right answers, and you'll develop a roadmap tailored to your needs.
🙋♂️ What are the benefits of taking this course?
A-3. Based on my own experience as a non-major, I've realized that those who take this course can optimize their career preparation time and process.
Note
I currently work as a data scientist in the UK. Before coming here, I completed my undergraduate and master's studies at Korea University. Personally, I run the data science project "Visualizing Korea (https://visualisingkorea.com)." In 2020, Visualizing Korea won first place in Deacon's "COVID-19 Data Visualization AI Competition," often called the Korean Kaggle. In 2019, an article published by Visualizing Korea was also a finalist for the Data Journalism Awards' Data Visualization of the Year award.
Although I am currently actively working in the field of data science and even run a YouTube channel about data science (https://www.youtube.com/c/visualisingkorea), five years ago I was a non-major with a liberal arts degree preparing for a career in data science.
Having been a non-major myself, I understand the frustrations and concerns that come with preparing for a career as a non-major. To help you avoid wasting time like I did and maximize your preparation time, I've shared my insights from trial and error as a non-major, the things I wish I'd known earlier in my career in data science, and, most importantly, the things I wish I'd known when preparing. I'll share all of these insights with those who are at the same crossroads.
Even if you're a complete beginner or non-major in data science, I hope you'll follow my lectures carefully and learn how to effectively prepare for a data science career without wasting time or effort, unlike me who wasted time and missed many opportunities.
Who is this course right for?
Beginners who are new to data science
Non-majors who have never studied data science or related fields
A liberal arts major who is afraid of programming languages or math
People who know what data science is but are unsure of how to prepare for it
Need to know before starting?
No prior knowledge is required, but an active attitude to apply the lecture content to my situation is required.
All
31 lectures ∙ (2hr 25min)
All
40 reviews
4.4
40 reviews
Reviews 3
∙
Average Rating 5.0
5
I took a weekend and finished it all. It was so concise and organized information about data science careers that I was able to save time searching here and there. The most impressive thing was that the major or job I have been doing is not necessarily disadvantageous to becoming a data scientist. I think the key is to find your own unique selling point and how to integrate it into the field of data science. Thank you for the great lecture :)
That's right! You understood the content of the first chapter exactly :) I was also able to get my first job by using my background. I hope you will make good use of johnnyljh's unique selling points and get good results. Thank you for your positive review :)
Reviews 6
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Average Rating 4.5
5
I listened to the lecture well. The instructor organized it neatly and clearly, so it was organized in my head and absorbed well. They say that the job of data scientist is popular and in high demand, but I didn't know what specific skill sets I should build. After listening to the instructor's explanation, a roadmap was drawn up. ㅎㅎ However, if I had to choose something that was a bit disappointing in the lecture, I think it would have needed a little more specific explanation... For example, in order to understand a data science career, you said that you need to set a data science career goal and study efficiently to achieve the goal, but I felt like there were some specific examples or explanations missing throughout the lecture. Other than that, it was really well organized and good! Thank you!!!
Hello Yankee! I am glad that you have achieved the purpose of the lecture since you have drawn a roadmap :) If there was a part that was difficult to understand due to a lack of specific explanation, please leave a comment in the Q&A section! I will try to supplement the explanation. Thank you for your positive review :)
Reviews 3
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Average Rating 5.0
Reviews 1
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Average Rating 5.0
5
As a non-major, I wanted to see someone who had succeeded in a similar environment, and thanks to this, all the things I was curious about and wanted to know were resolved. The content was organized easily, so it was easy to understand, and the practice problems were especially helpful. As a non-major, I was at a loss, but this lecture was perfect for me! I will prepare diligently as I have learned and succeed like the instructor! Thank you for creating a great lecture.
I think one of the difficulties of preparing as a non-major is that it is difficult to find people who have succeeded in similar environments. I am very proud that the parts you were curious about have been resolved! I hope you will prepare diligently as you have learned and have good results :)
Reviews 1
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Average Rating 5.0
5
Whether you are a major or not, I think this is a must-have lecture to save your time. Because it fits the reason why the lecture was created. (Purpose of the lecture: To shorten the preparation time and provide an efficient preparation process for students to obtain a job in data science by providing organized information on the parts that the instructor was confused about while preparing for data science.) Personally, in the last stage, I think it would be better if the instructor shared the links that he recommends or is already subscribed to, although each student has a different desired career in relation to specialized books and newsletters/blogs. Thank you for the great lecture.. I listened to it all at once today and haven't been able to answer all the practice problems yet, but I don't think I will study anxiously without a clear roadmap. Thank you again for providing a great lecture!! ㅎㅎ If I have questions while sailing.. I will ask..ㅎㅎ Thank you!!
Hello, Arumnim, I am so happy to hear that the lecture was helpful! :D The list you mentioned is different for each individual depending on their curriculum goals, so I did not recommend it, but I think sharing links can be helpful. I will try to find links that can be helpful from a general perspective :) Thank you for your positive lecture review and comments :)
Hello, Arum, I have posted the list of questions you were curious about in the Q&A section (https://inf.run/wkJk). I hope it helps. Thank you :)
$34.10
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