Data Analyst Career with Generative AI [Datalian Seminar Replay | May 2024]

Did you know that there are different types of data analysts? If you want to know everything about in-house data analysts and consulting data analysts, come to this seminar!

If you are preparing for a data analyst job or career change and were curious about hearing from someone in the field, ask a data analyst anything!

Through this lecture, students are expected to be able to check actual materials such as big data contests and self-introduction essays and determine the direction of their studies and job preparation.

Data analyst job prep: Lost on where to begin? Get hints at this September seminar!

Portfolios are an essential element for landing a job as a data analyst! If you've been feeling overwhelmed about how to prepare, get some tips from this seminar.

The real story of an AI-native company run by GolbinHacker, the 1st place winner of Kakao AI TOP 100 and author of the bestseller "The Best Prompt Engineering Lecture." It explores how just two developers build and operate a service, along with the new competencies developers and organizations need in the AI era through real-world cases.

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

Linear Algebra in One Go. Learn linear algebra concepts in detail—concepts that, once learned, will serve as the building blocks for your thinking for a lifetime. Going beyond simple explanations, we kindly guide you through practical exercises to show you how to apply them in real-world situations.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

This course is part of the 'Wrtn AI Literacy Voucher Program', a collaboration between Inflearn and Wrtn, and is available completely free for job seekers who are vulnerable to AI. Career expert Career D will teach you step-by-step how to apply AI to job searching and career transitions. From basics to hands-on practice, it's designed so that even AI beginners can follow along easily without any burden!

SQLD Yellow Book 176 Problem-Solving Course for those who studied but can't solve problems. After completion, you'll go beyond passing to become an expert. SQLD All-in-One Pass!

An easy-to-understand introduction to data lecture by Korea's first data humanist, Shin Do-yong! Let's learn data-based thinking to properly read and utilize massive amounts of data!

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

"AI will not replace people. People who master AI will replace those who rely on it." In the era of AI, where creating results has become easier than ever, human thinking and problem-solving skills are becoming even more critical. Jae-sung Kim, a former McKinsey consultant, will guide you step-by-step through advanced problem-solving skills using AI, powerful research strategies, and persuasion techniques to turn your results into reality.

Contains the growth history and know-how of portal company N, which achieved rapid growth through cloud services.

SQLD, where should I start? For those of you feeling completely overwhelmed, this is the lecture for you. You will meet a transformed version of yourself after completing the course. SQLD All-in-One Pass!

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

After using it for 14 days starting immediately upon release, here are the reasons why I recommend Claude Code (AI Coding) to everyone—from job seekers to industry professionals—as something they can apply right away.

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.