Inflearn - Lifetime Career Platform

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.

There was a rumor that Ediya Coffee always opens its stores near Starbucks. How much of a difference is there really between the store locations of Ediya and Starbucks? Will the real estate price fluctuation trends from 2013 to 2019 be reflected in apartment pre-sale prices as well? What kind of parks are in my neighborhood? How can we best utilize the data available on the Public Data Portal? The goal is to become familiar with Python and various data analysis libraries by working with different types of data through public data.

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.

How to Read the World with Data: Explore Your Own Data Science! Dive deep and practically into the tools and techniques that form the core of modern data science. In particular, you will learn how to analyze data and implement algorithms using Anaconda, Numpy, Pandas, and Scikit-learn, essential components of data science.

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.

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.

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.

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 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.

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

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.

Do you want to create a tool that can help you with your real-world investments using Python? In this course, you will learn how to create an interactive web dashboard for financial data analysis for intermediate Python users and learn about pair trading strategies for in-depth quantitative work. We hope that you will be able to create your own useful analysis tools to help you analyze financial data more powerfully and make wise investment decisions.

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.

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

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.

I have packed this with everything I want to tell junior PMs/POs who are struggling due to a lack of good mentors. I will generously share various techniques and mental models used by high-performing PMs/POs.

By skimming through the content covered in the Tableau certification, you can grasp the overall features of Tableau in a short period of time.

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.

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 NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.