Introduction to Data Science with Silicon Valley Engineers

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!

Uncover true value in the age of data! 📊 Designing applications focused on data has now become essential. Develop the insight and skills your company wants with the latest trends and practical case studies. Start now with efficient data processing and design secrets! Your next step, leap into the data-driven world!

Don't miss the opportunity to learn about Kubernetes, the hottest infrastructure in Silicon Valley, from a practitioner!

This is a practical, hands-on course designed so that even SQL beginners can start easily and apply it right away. ✨ Learn to pull data with SELECT, connect with JOIN, organize with GROUP BY, and more— master only the most frequently used features in real work, in an easy and fun way! 📊 We minimize complex explanations and focus on realistic examples that make you think, "Ah, this is why SQL is important!" 💡 Data analyst, developer, data engineer… Whatever path you choose, SQL is an essential skill. 🚀 With this one course, solidify your fundamentals and develop the data intuition to solve problems on your own in real-world situations! 🔥 Start right now. This is the easiest first step into the world of data! 🌍💙

You will learn Apache Airflow, the most widely used Orchestrator for creating software data pipelines.

Learn Everything About Python from a Silicon Valley Software Engineer. Currently, as a 15-year veteran software developer, I handle everything from web applications, big data, and even SRE & DevOps, all with Python. From the basics of Python to advanced techniques, don't miss this opportunity to learn all the skills and know-how for using Python in real-world Silicon Valley practice!

Learn how modern data teams leverage dbt. Build maintainable analytics models and validated data pipelines hands-on. Master core concepts of analytics engineering with a practical focus. This course is for those who want to take their data career to the next level 🚀

The essential technology of the cloud era, the complete guide to Amazon EKS! Manage Kubernetes clusters easily and quickly with EKS, from container orchestration to practical application.

Redis is a fast and scalable open-source database. This technology is used to maximize performance in real-time applications, games, social media platforms, and more. However, to truly understand the power of Redis and use it effectively, you need a deep understanding. Now is your chance to learn Redis in a professional way. "Redis with Silicon Valley Engineers" covers a wide range of topics, from the core concepts of Redis to advanced features. This course is designed for everyone, from beginners who are new to Redis to advanced users.

Real-time data processing is now essential, not optional! Learn to handle real-time streaming smartly with Apache Flink. Batch? Streaming? Even complex concepts can be understood easily and quickly. Hands-on practice configuration where you'll get the hang of it by directly working with Kafka and DB integration! In a world driven by data, start first with Flink.

"The technology that prepares you for the future, Docker" This course focuses on learning Docker quickly and easily. It explains the basic concepts and usage of Docker in an intuitive and easy-to-understand way, and through interesting practical exercises, you can learn solid skills that can be used immediately in practice, such as creating containers, building images, and setting up networks. This is the perfect course for those who want to quickly absorb and apply the latest technology to their work!

Become a pioneer in the latest large language model (LLM) technology through Langchain online lectures! This course provides practical skills and innovative knowledge that will upgrade your career. #LangChain #LangGraph #LangSmith #MCP

Master AWS networking completely! Learn VPC, subnet, routing, and security with a focus on practical application, and design secure and scalable cloud network architectures. Complete the core skills that every working engineer must know in a short period of time!

The Scala course provides essential skills and knowledge for professionals working in the fields of big data and data science, offering an excellent opportunity to learn efficient data processing and analysis methods through Scala's powerful programming language capabilities. Scala is widely used in big data processing tools like Apache Spark, and through this course, participants will be able to acquire advanced skills required in the big data ecosystem, thereby enhancing their competitiveness. For developers and data scientists who want to build a successful career in a data-centric world, this course will be a crucial learning opportunity not to be missed.

This is a practical Ansible course taught by Silicon Valley engineers. Ansible is a powerful tool for automated infrastructure management and provisioning, saving time and effort and building a consistent environment.

Gradle Masterclass for Efficient Build Management – Learn how to automate complex builds and increase project productivity. This course covers the core concepts and practical application of Gradle in detail. You will be able to build a professional-level build environment in no time.

Apache Superset is an open-source BI tool that lets you quickly create charts and dashboards by connecting multiple databases. You can visualize data without knowing code, and SQL Lab allows you to run queries directly, making it useful for everyone from beginners to professionals. The dashboards you create can be shared immediately with permission settings, making it perfect for collaboration. Learning Superset gives you the ability to make data-driven decisions quickly and build practical dashboards you can use right away at work. In short, it's perfect for anyone who wants to make data "speak." 🚀

🔥 Vibe Coding with Streamlit 🔥 Real-time coding experience where you can instantly create your own web service with just one line of code! Visual-focused, hands-on lectures tailored to MZ sensibilities - no complex backend knowledge required. Learn the basics of Streamlit and awaken your development instincts by directly implementing practical apps. Complete your own "personal service" with code in a vibe way right now 😎💻

“There are many ways to solve problems, but why do we feel lost during interviews?” This lecture offers coding interview survival strategies directly from a current engineer working in Silicon Valley. Focusing on problems frequently encountered in real interviews, we'll guide you not just through simple solutions, but also how to articulate your thought process and how you'll be perceived from the interviewer's perspective. Designed for mid-level developers and above, the curriculum is optimized for real-world interviews. If you are preparing for coding tests and technical interviews, gain practical skills and confidence through this lecture.

Are you still managing ML experiments manually? 🤔 Automate everything from experiment tracking to model deployment with MLflow, and dramatically boost your development productivity! An essential skill for data scientists and ML engineers 💡 Start right now! 🚀

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 structured 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 book covers basic usage and data preprocessing methods for Python beginners/non-developers. As an appendix, we also provide a preprocessing method using the ChatGPT code interpreter.

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.

With Python Quant Investment, you can create investment strategies based on data and invest according to the strategy. You can implement various asset allocation strategies and ultimately create your own investment strategy.

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.

The Intermediate Python course is designed to deepen your understanding of Python by covering advanced topics such as functional programming, metaprogramming, exception handling, and object-oriented programming.

Let's develop specialized skills in programming skills, algorithm skills, and statistical analysis skills!

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.

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

It improves logical thinking skills, develops an understanding of how to use programming tools, and develops the ability to design algorithms for problem solving.

A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.

* 100% Practical Curriculum: Focused on hands-on data handling rather than grammar-based learning * Essential Machine Learning Prep: A complete summary of data preprocessing techniques required before AI training * Data Analysis Tool Mastery: Conquer core functions of Numpy (numerical operations) and Pandas (data manipulation) * Deriving Visualization Insights: Transferring data visualization skills using Matplotlib and Seaborn * Building Statistical Foundations: Learning basic statistics and group analysis techniques to identify data characteristics

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems