インフラン - ライフタイムキャリアプラットフォーム

This is not an investment lecture. It covers data collection, analysis, and visualization using stock market data. You will work with various data formats and learn various text preprocessing techniques. It covers visualization techniques for time-series data, methods for representing scales, and several techniques for interpreting stock price data. This course is about learning how to gain insights through data analysis. It is designed so that the concepts learned through stock price data can be applied to collecting, analyzing, and visualizing other time-series data such as demand, inventory, sales, and traffic volume.

This course is for both beginners who have never been exposed to programming before and those who have some theoretical knowledge about Python. The main purpose of the course is to create interest and enjoyment in Python based on real projects. I hope that through this course, you will gain more ideas and have more fun with Python.

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

Inflearn cumulative students over 30,000, Datarian's SQL basics course with rich online/offline teaching experience. Learn SQL basic theory and solve 10 SQL problems together.

When using Pandas for data analysis, preprocessing, machine learning, and deep learning, we often find ourselves relying on the same core functions. There is a Pandas Cheat Sheet that compiles only these essential features. For those who have grown tired of trying to learn Pandas through thick books, this two-page document will teach you Python Pandas. Master the core functions of Pandas using the cheat sheet provided by the official Pandas tutorial.

Gain insights from DataOven CEO Ha Yongho, a first-generation data analyst who has demonstrated numerous insights and successes. This is a meetup video for data professionals, held on July 18, 2024.

This course is designed for beginners and consists of easy explanations and various hands-on projects. It covers data collection using web crawling and LangChain tools, and summarization, extraction, sentiment analysis, and recommendation systems using LLM.

Let's learn together about Columnar Storage, which allows you to conveniently handle both structured and unstructured data provided by GCP and allows for a free table format, and BigQuery, a universal database.

This is a detailed and practical course for Google Analytics beginners. Rather than just covering fragmented features, you can learn the actual analysis process using GA4 through scenario-based analysis. It is continuously being updated to reflect the latest GA4 changes!

Basic knowledge is not enough for real-world investing; build your own profit model using the machine learning know-how of a 10-year veteran quant.

Learn Pandas' core features, comparing with SQL!

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

Through this course, you can learn SQL and database fundamentals, SQL syntax, database design for practical applications, database utilization through programming, and data analysis use cases.

Learn about the basics of Python required for data analysis, the data analysis framework (pandas), and the data analysis process.

This is a lecture on how to use NumPy and practice its application in real-world scenarios.

This is a basic/introductory course on digital marketing. It covers how to design digital marketing based on data and various practical cases. It contains stories and content that I want to share from my 14 years of experience in various fields of marketing, from digital marketing agencies to in-house marketers at large corporations. It is recommended for those who want to understand digital marketing in general.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

The fact that you can analyze data more easily and effectively with Python Pandas instead of Excel! Learn the basic functions of the Pandas library and how to use it in practice through financial data analysis.

We conduct app log analysis using data from Google Analytics 4 and Firebase. It includes useful content for practical use, such as arrays, window functions, funnels, retention, Google Sheets, and more.

This is a course for PMs who want to utilize data. It covers the entire process of using data in a PM role, capturing the full workflow from your first day at a new job to the beginning and end of a project. It is an introductory data course designed to build your thinking power, covering everything from data-driven and logical thinking to metric definition, log design, experiment design, and building a data culture. Although it was created for PMs, the content is also highly beneficial for data analysts (in fact, many analysts have already taken this course). Additionally, many professionals in marketing, design, and business planning roles are also taking the course.