인프런 - 라이프타임 커리어 플랫폼

Financial data such as stocks, exchange rates, coins, real estate, and bonds are easily accessible and provide us with opportunities to make money, but most people miss valuable opportunities because they do not know their characteristics and how to handle them. Through this lecture, you can learn how to understand the meaning of various financial data and how to analyze them using Python.

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.

Author's direct lecture for the SQL200제 book published by Jeongbomunhwasa! So far, numerous data analysts have gained wings in their data retrieval skills with Instructor Yooyeonsu's SQL lectures.

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

A theory-practice QGIS course covering analysis basics, advanced functions, and visualization introduction for QGIS utilization. Start QGIS by taking this unprecedented all-in-one course!!

Master advanced SQL, solve 101 practical data analysis problems at a professional level together, and build a solid SQL data analysis skillset.

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. This course covers the entire process of utilizing data in the PM role, including the flow of work from day 1 of a job change through the start and end of a project. It is an introductory data course that covers data-driven thinking, logical thinking, metric definition, log design, experiment design, creating a data culture, and more, to help you develop critical thinking skills. Although created as a course for PMs, it is structured with content that data analysts would also find beneficial (in fact, many analysts have taken the course). In addition, many people in marketing, design, and business planning roles are also taking the course.

This lecture is a follow-up lecture to 'Python Data-Based Stock Quant Investment Part 1'. If Part 1 was more of an introduction, Part 2 is an in-depth lecture that focuses on the entire flow of practical strategy implementation and quantitative investment development. This class focuses on advanced Pandas techniques for handling time series data, and how to implement signal-based strategies and static/dynamic asset allocation strategies that require adjusting asset weights at various intervals based on this. Furthermore, it goes beyond strategy implementation and learns about 'code framework' that directly verifies and backtests various investment strategies with minimal code modifications, how to extend this to improve it so that it can lead to actual investment, and what to watch out for in this process. In addition to the programming component, you can experience the best Python quant investment flow that you cannot find in investment books, blogs, YouTube, etc. by deeply covering theoretical contents such as the two types of return concepts (simple return, log return) and evaluation indicators related to backtesting.

For those looking to learn Data Science, Big Data, and Crawling, (1) we will summarize the most essential Python core syntax in a short time, and (2) you will become familiar with Python and crawling through practical crawling programming.

This course is designed for beginners, students, and those who want to use Polars, aiming to acquire the necessary skills for data analysis, from basic concepts of the Python-based data analysis library Polars to simple data processing and exploratory data analysis (EDA).

If you work with data, SQL is a fundamental skill you must know. In this course, we will learn SQL from a data analysis perspective, focusing on hands-on practice. The practice will be conducted in Google Colab using DuckDB.

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.

We will teach you the key points of SQL data analysis methods in an easy-to-understand manner through analysis projects and theories.

I want to create an algorithm trading bot that makes unlimited money and run it automatically. Where should I start?

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.

Based on the key indicators defined based on data, learning through experiments and rapidly iterating to grow the service. This is a 101 lecture covering the basics of growth hacking.

We will reveal the actual data analysis portfolio of successful job seekers and the portfolio-related questions they received during interviews!

As you work through more difficult Leetcode SQL problems, you'll learn how to apply multiple grammar rules to a single problem.

How can data be leveraged in side projects? Hear the stories of 'Geumim' and 'Skull' at this seminar!