[Renewal] Python Data Analysis for Beginners (Easy! Master the entire process of preprocessing, pandas, and visualization) [Data Analysis/Science Part 1]
This is a course for beginners to master the fundamental techniques of the entire Python data analysis process. As an e-commerce planner and developer currently applying data analysis techniques in the field, I have designed this course to help you easily learn and immediately apply the entire Python data analysis workflow.
What sets this lecture apart from others is...
You often say things like:
"You don't need to understand 100%."
"Make good use of AI."
"The important thing isn't creating results by utilizing 100% of the code yourself."
Thanks to those words, even though I can't write code from A to Z from scratch, I've become able to start projects by utilizing AI and thinking, "Ah, so this part is used with this meaning," "Oh, I could try changing this part like this," or "I think there were functions like that back then; should I look them up?"
Of course, it would be wonderful to be able to do everything from A to Z alone, but when it's difficult to invest that much time while balancing a career, I believe it's more important to at least have the fundamentals and background knowledge to utilize code written by others.
In that sense, this course not only allows you to build a solid foundation through repeated listening, but it also felt comfortable—like receiving private tutoring without the pressure.
At first, I just listened and nodded along. After class, I took the time to do some clone coding on at least a few of the final files (if not all of them), adding my own comments and editing them in my own style.
Thank you for the great lecture.
5.0
데싸데분
31% enrolled
I'm continuing this course after taking the Web Scraping Basics Bootcamp!
I'm currently taking other bootcamps in parallel with the goal of becoming a data scientist, and from a beginner's perspective, Fun Coding's lecture quality seems truly overwhelmingly excellent!
Going forward, I plan to actively use Fun Coding's lectures for preview purposes, while focusing on review and project work in other bootcamps!
Thank you sincerely for the great lectures and passionate feedback every time! I'll see you again in the next lecture 😊
5.0
gyunhwank
100% enrolled
This course was structured around practical exercises covering data preprocessing and EDA using Pandas, and visualization through Plotly, which was a great help in learning the flow and feel of data analysis.
By applying the various features of Pandas to actual datasets, I became familiar with the analysis process, and through Plotly, I could create intuitive visualization results, allowing me to develop my data interpretation skills as well.
As someone learning data analysis for the first time, the practical-oriented structure was particularly useful, and because the instructor's explanations were kind, it was a course that even non-majors could follow without difficulty.
What you will gain after the course
How to use pandas
Data Analysis Basics
Python Data Preprocessing
Latest Data Visualization
Plotly visualization library
Various data formats and data collection
The official course chosen by top tech companies like Naver, Kakao, Line, Coupang, and Baemin! A high-quality course that will solidify your Python data analysis fundamentals.
This course is a lecture for seriously mastering professional Python data analysis skills. It is designed to help you master everything from data preprocessing and data manipulation using the pandas library to the most useful modern visualization library (plotly). Based on 8 years of experience teaching 80,000 students while working in the field, this is a special course created by considering the learner's perspective as much as possible compared to typical IT lectures, and detailed additional materials are provided along with the lessons.
This course is currently being used as the official in-house Python data analysis training for one of the "Na-Ka-Ra-Ku-Bae" companies.
This course has been renewed by reflecting previous feedback
How should I build the fundamentals of data science and data analysis?
Experience the entire process of data collection, preprocessing, and analysis (SQL/NoSQL + Python). If you want professional analysis skills, master the Python-based techniques in this course. If you are aiming to become a data analyst or scientist, we provide a roadmap for step-by-step learning from the introductory level. (Refer to the Data Analysis/Science Roadmap at the bottom)
I have created a video that explains data-related careers and the entire analysis/science process in detail. Through this video, you can efficiently learn the data curriculum through self-study according to your goals.
I want to try actual data analysis myself as quickly as possible!
Everyone already possesses the basic knowledge required for data analysis. Knowing how to calculate an average is enough. The key is to quickly acquire the skills to perform the entire data analysis process using Python.
From various data preprocessing to data analysis with real-world data Summarizing all core skills for professional data analysis
Python data analysis is not a skill that can be mastered all at once. To build proficiency, you need to become 'familiar' with it, and it is effective to encounter similar concepts and application examples from various angles. To this end, I would like to introduce a book I wrote that will be helpful to refer to alongside this lecture. By utilizing both media, you can become more accustomed to data analysis techniques.
Python data analysis can feel awkward at first. After learning how to use the tools and watching the real-time code execution through the online lectures, you can build a solid foundation by deleting only the code from the provided notebooks, writing the key code yourself, and comparing it with the video.
After building a solid foundation, you can effectively enhance your skills by encountering different explanations of similar syntax and additional examples through books.
There are so many data analysis courses, but even after taking various ones, I still don't get it!
The data field is a combination of various theories and technologies. Therefore, it is important to learn them systematically. Rather than lectures that assume you already know all the relevant theories and jump straight into analyzing data and applying fancy machine learning or AI techniques, lectures that allow you to learn the theories and technologies required for beginners one by one, practice them, and build them up as your own are more helpful.
Based on actual data analysis and domain experience from top-tier tech companies (Naver, Kakao, Line, Coupang, Baemin) A lecture that explains foundational technologies step-by-step and systematically from a beginner's perspective
I even want to participate in Kaggle competitions.
Kaggle competitions primarily use machine learning and AI to predict data. To master these technologies, you must first become familiar with data analysis techniques such as pandas. This course covers pandas and visualization techniques, and is structured to help you learn machine learning and deep learning (AI) technologies step-by-step through a systematic roadmap. (Refer to the roadmap below)
What are the skills required for data analysis?
In the field, data is primarily analyzed using SQL and pandas. Professional data analysis requires skills in data preprocessing, analysis, and visualization. To achieve this, you need to master pandas and plotly. This course covers all the core skills required for professional data analysis using Python in a real-world business environment.
How can you effectively learn data analysis skills?
pandas has a barrier to entry due to its unintuitive syntax and vast range of features, requiring a lot of practice. This course has been structured with this in mind:
First half: Learning basic pandas and preprocessing functions by converting massive daily data into monthly data
Second half: EDA analysis with actual e-commerce data, applying data analysis and visualization (plotly) techniques
Through this, it is designed to help you become familiar with pandas and plotly in a short period of time and master the entire process of Python data analysis.
I heard that for data analysis, you need to have a good understanding of the actual business domain?
However, it is difficult to take a lecture that requires you to first understand various fields you are not even interested in. Try analyzing using the e-commerce data covered in this lecture. Even without using the term "untact era," all businesses have been shifting online for the past several years. To understand online business, try understanding e-commerce data, which is at its core. Understanding the most helpful domain along with the related technology will both be of great help.
Grasp core e-commerce data and real-world business domain experience to get a feel for both data analysis and the business domain!
Even if I watch the lectures, there are no materials, so should I also purchase the book?
We provide summarized explanations that go beyond the limitations of a book, along with actual code in a format that can be executed immediately. By watching the lectures and running the materials together, reviewing becomes easy! You can also refer back to them whenever you need them in the future. (I am very attached to the materials. I create materials that are better than books, so that the materials alone make the tuition fee well worth it.)
Now verified by 90,000 people online and offline over 9 years With well-organized materials and clear explanations, we provide better online IT lectures! When you learn well, things change!
Don't I need to learn matplotlib for Python visualization?
matplotlib is a traditional but limited data visualization technology that primarily focuses on creating static graphs. In contrast, the modern technology plotly focuses on creating interactive graphs that allow for user interaction. Furthermore, it offers advantages such as superior visual quality, suitability for web environments, and support for a wider variety of graphs. As a result, plotly has recently become the mainstream choice in the industry. Therefore, this course explains plotly technology, which is becoming the dominant visualization skill.
plotly (supports dynamic graphs) VSmatplotlib (focused on static graphs)
A helpful course even for those who have already taken data analysis classes!
To make Python data analysis skills your own, various practical exercises are necessary. This course conducts data analysis from start to finish using various real-world examples (COVID-19 data preprocessing, e-commerce data analysis). Through this, you can improve your proficiency in related technologies and organize the knowledge you might have missed.
시간을 낭비하지 마세요! 우리는 정보가 없어서 못하는 것이 아닙니다! 검증된 강의로 익히세요!
This course has been refined through countless pieces of feedback over several years and was created after deep deliberation driven by a passion for online education.
This is a course created through constant reflection and improvement so that you can feel, 'Ah! This really is different!' I ask that only those who are serious about learning take this course!
A data preprocessing example created by processing actual raw data in a data lecture COVID-19 data is the most helpful example for practicing actual pandas basic functions and preprocessing. Therefore, we have structured the course to firmly master the relevant skills by creating graphs for the entire period when COVID-19 was most active, as follows:
Daily Trend of COVID-19 Confirmed Cases by Country (Including the entire tracking period of confirmed cases during the pandemic)
Created at a professional report level for actual business data analysis, including industry know-how! Just drawing graphs is not enough. In the professional world, details are crucial.
Various graphs and multi-faceted analysis
Systematically Learn Dave Lee's Fun Coding Roadmap 🔑
Developer, Data Analyst, and Data Scientist Career Roadmap!
We provide an A to Z roadmap that allows you to build a solid foundation in a short period, covering everything from web/app development to data analysis and AI. Since IT technologies are closely interconnected, integrating them is essential for web/app services or data science. By mastering core technologies through a step-by-step increase in difficulty, you can learn efficiently and grow into a competitive developer or data expert with a comprehensive understanding of systems and data. To support this, we have prepared a roadmap that systematically organizes the core technologies of each field.
1. The Fastest End-to-End Data Roadmap
We have created a video that explains this roadmap, data-related careers, and the entire process of data analysis/science in detail. By referring to this video, you can easily master the data process in a short time on your own without any trial and error!
Wait! ✋ Click the roadmap below to see more details. If you purchase the roadmap as a bundle, it is offered at a discounted price! (The discount rate is scheduled to be reduced soon.)
add_shortcode('roadmap','66','roadmap','1')
2. The Fastest Full-Stack Roadmap
We have created a video that explains this roadmap in detail, along with the fastest way to learn and implement web/app development on your own. By referring to this video, you will be able to implement web/apps in a short period of time without trial and error. web/ứng dụng trong thời gian ngắn mà không gặp phải sai sót nào.
Wait! ✋ Click the roadmap below to see more details. If you purchase the roadmap as a bundle, it is offered at a discounted price! (The discount rate is scheduled to be reduced soon.)
add_shortcode('roadmap','49','roadmap','2')
3. Essential Computer Science (CS) Core Knowledge for Development and Data Fields
This roadmap is a course that systematically organizes essential Computer Science (CS) knowledge, which serves as the foundational IT theory for the development and data fields. Among these, we are opening lectures that allow you to systematically learn the most important core subjects, such as computer architecture, operating systems, and networks.
add_shortcode('course','337332','list')
Recommended for these people
Who is this course right for?
Those who want to master Python data analysis techniques
Those who want to master pandas and visualization techniques
Those who want to grow as data analysts in the long term
Those who want to master data analysis skills in the long term
Those who want to build a solid foundation in basic data analysis skills.
Key Experience: Coupang Senior Development Manager/Principal Product Manager, Samsung Electronics Development Manager (Approx. 15 years of experience)
Education: BA in Japanese Language and Literature, Korea University / MS in Computer Science, Yonsei University (A complete mix)
Key Development Experience: Samsung Pay, E-commerce Search Service, RTOS Compiler, Linux Kernel Patch for NAS
Books: Linux Kernel Programming, Understanding and Developing the Linux Operating System, IT Core Technologies That Anyone Can Easily Read and Understand, Python Programming Primer for Absolute Beginners
I am a student who aims for graduate school and research in deep learning, machine learning, and mechatronics.
So I took Python lectures from other instructors to build up my basics, and I took this lecture to learn the data processing and analysis process. At first, unlike other instructors, he didn't write the source code while filming videos, but prepared class materials and lectured on the content in detail. Most of the lectures I took were from the former, so it took me a while to get used to the latter. However, the materials related to the class content were really solid. I really liked this part.
Also, as the class progressed, what impressed me the most was that even though the class was just continuing, it was repetitive learning. For me, the most difficult thing about listening to lectures is repetitive learning. In the case of academies, they make students repetitive learning through assignments, but on average, many students, including me, find repetitive learning difficult or boring through lectures. However, this lecture was a very helpful lecture for me because it allowed me to learn new content while repetitive learning. Of course, I plan to take other classes again and challenge myself with repeated learning, lol...
When I take this class, I first watch the video all at once. If there is a part that I don't understand, I watch it over and over again. Then, I put down the video, put the materials that the teacher gave me on one monitor window, and at first, I wrote down the source code as I remembered it, and when I couldn't remember it/when I thought I had finished writing the source code, I checked the materials.
In addition, if you post a question on the Q&A board or the video, you will receive a reply in a day or less at the earliest. This is where you can feel the teacher's enthusiasm. Also, one of the things I felt when I took the Python class was that when you ask a question, they give you a link to the relevant content. And I saw some people who lectured by saying that it would be helpful if you referred to it. Personally, I didn't like it, but the instructor of that class put a lot of effort into leaving comments.
And, I plan to take a class on MongoDB for the next class!
The class was really good ^_^!
Thank you so much for leaving such a great review. It must have taken you a long time to write such a review, but I was actually a little touched that you wrote it in such detail. Reviews like this are a great force that can create good lectures.
I tried both the method you mentioned, writing code while doing it, and the method of explaining it with materials and a kind of scenario, but when I did the former, the content I wanted to convey was not conveyed in a substantial way, and since I was worried about both the code and the content I wanted to convey, the learning effect actually decreased. So I decided to use the latter.
Actually, answering questions every day is not easy for me either... I'm worried that I'll have to make an announcement when I go on vacation, haha.
After all, since I've never met you before, if my answer is not conveyed properly, it's easy for there to be misunderstandings, so I'm paying more attention.
When I meet many people online without meeting them in person, there are many cases... Luckily, many people gave me positive reviews, which is a great help.
Thank you so much.
Thank you very much. It was a great help.
I am very satisfied with the lecture content and lecture materials.
I am also looking forward to the machine learning lecture. ^_^
I am so glad that it was helpful. I will also use the course reviews you wrote to encourage me, always think about them, and try to make better lectures. I really work hard on the lecture materials. I also enjoy making materials. I am so glad and happy that you are satisfied. Please do not open it to the outside, and use it only for personal use. Thank you.
The materials in the provided Jupyter notebook are neat and easy to read, and the practice of graphing the trend of confirmed COVID-19 cases by country is good. If you ask a question you don't know, they will answer quickly and sincerely, and there are no points to deduct. 5 out of 5. The explanations are also good and not difficult.
As a coding beginner, I started with nothing, starting with the Python bootcamp lecture, then the crawling lecture, and now I have finished the Python data analysis lecture.
Although these lectures may seem like separate lectures on the outside, they have a single flow and purpose as they always emphasize during the lectures, and most importantly, they explain in detail and in an easy-to-understand manner from the perspective of a non-major, so I was able to take the classes comfortably.
I am currently working in the real estate business, and after taking these lectures, I gained the ability to process and utilize data provided by sites such as Naver Real Estate and public data portals as I want.
It may seem lacking to experts, but I think that having this ability as a real estate business owner who is not an IT expert is a really great weapon.
Also, there is a huge difference between passively looking at processed data provided by others and looking at data that you have processed yourself.
So, if you are just starting out like me, don't worry too much and follow Janjaemi Coding's lectures one by one, you will find yourself growing before you know it.
And if there's one thing I wish for, it would be great if there was a lecture that completed a project from start to finish (even if the lecture length is relatively short) based on the lectures I've taken so far (Python Bootcamp, Database, Crawling, Data Analysis, etc.).
I'm now going to listen to SQL and NOSQL that I missed in the middle!!!! (My goal is to take all of Janjaemi Coding's lectures this yearㅎㅎ)
Thank you for the great lecture and I will continue to trust and follow you in the future.
Ah... such a good course review... you must have spent some time on it... thank you. I'm a little touched again. In my opinion, developers only know IT, but people in other fields have expertise in their own fields. Since there are not many people in each field who know IT well, I think that if you have your own expertise and can utilize IT, you can have a huge impact. However, it is very difficult to create such a lecture or absorb such a lecture. Nevertheless, through this lecture, I really like that you actually analyzed real estate data with Python. I think it's because the students are that smart. Thank you.
Thanks to the A-Z approach with detailed explanations for each topic, I feel like I'm building knowledge from the ground up. No matter what subject you're studying, finding the right course and instructor that fits you is the most important thing, and I think I've found mine.
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