Business Data Analysis Part 2 - Basic Statistics

Easy explanations and examples are provided so that you can apply them directly to your work.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

I created a lecture by selecting the Java parts that are absolutely necessary for Android app development. The total lecture time is 13 hours and 30 minutes. It will be very helpful if you organize it once before developing Android. There are more lectures on the YouTube Soft Campus channel. I hope it was helpful. Instructor Jaeseong Yoon

Introduction to C language, the foundation language for software development

"From environment setup to practical business projects, a data science journey to find revenue answers through data (39 lectures in total)." Go beyond simply learning how to use libraries and learn how to cook real-world e-commerce data through the data science process. Starting from development environment setup and Numpy/Pandas basics to data preprocessing, statistical analysis, visualization, and business scenario-based RFM segmentation and hypothesis testing! Systematically master the core competencies of a data scientist through practical projects.

: "From Mathematical Foundations to the Latest Models, Completing the Deep Learning Pipeline with PyTorch (44 Lectures in Total)" The era of simply learning how to call model.fit() is over. From the very basics of artificial neural networks—differentiation and gradient descent—to essential PyTorch techniques used in the industry, and CNN/RNN for handling image and time-series data! We will help you systematically master the entire process of deep learning. Go beyond analyzed data and step into the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

: "From environment setup to 7 practical projects, a deep learning full-stack journey across data domains (35 lectures in total)" Go beyond simply following example code and learn how to cook various types of real-world data using deep learning. Starting from building CPU/GPU development environments, you will cover waste sorting classification (Image), stock price prediction (Time-series), ECG anomaly detection (AE), animation stylization (GAN), medical image segmentation (U-Net), and sound classification (Audio)! Systematically master core deep learning algorithms through hands-on projects.

: "From Mathematical Foundations to the Latest Models: Completing the Deep Learning Pipeline with TensorFlow (44 Lectures Total)" The era of simply learning how to call model.fit() is over. From the very foundations of artificial neural networks—differentiation and gradient descent—to the essential use of TensorFlow and Keras in the industry, and even CNN/RNN for handling image and time-series data! We will help you systematically master the entire process of deep learning. Go beyond analyzed data and enter the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

"Beyond Data Analysis: Mastering Predictive Modeling with 5 Real-World Projects (45 Lectures Total)" Have you finished learning data analysis but feel stuck when it's time to actually build a model? Beyond simply learning how to call libraries, this course will help you fully master the inner workings of algorithms and optimal model validation strategies—ranging from Titanic survival prediction to spam text classification. Systematically conquer projects that wield the most power in the industry, from linear models to the latest ensemble algorithms and the basics of Natural Language Processing (NLP). Step into the world of AI modeling and start predicting the future based on analyzed data.

"Beyond Data Analysis: From Predictive Model Design to Optimization (29 Lectures in Total)" Have you finished your data analysis but feel stuck when it actually comes to building a model? We help you fully master not just how to call libraries, but the working principles of each algorithm and optimal model validation strategies.

* 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

Currently, Android development can be done using both Java and Kotlin. You can develop the same Android applications regardless of which language you choose. This course covers most aspects of Android application development, from basics to advanced topics, and all code is written using Kotlin starting from the fundamentals. Additionally, it utilizes Google's latest UI library, Material 3. This course involves completing three projects throughout the entire Android curriculum. By working on these projects, you can master advanced techniques actually used in the industry, such as Hilt, Retrofit, Room Database, and MVVM. Furthermore, you can increase productivity using AI by performing "vibe coding" with Gemini within the Android development tools. Soft Campus will always strive to provide the most up-to-date lectures.

Android can be developed using the Java and Kotlin languages. You can develop the same Android application regardless of which language you use. This course covers most aspects of Android application development, from basic to advanced levels, and all code is written using the Kotlin language starting from the basics. Additionally, it utilizes Google's latest UI library, Material 3. This course primarily focuses on the action bar, data management, resource management, sensors, and networking within the overall Android development process.

Currently, Android can be developed using the Java and Kotlin languages. You can develop the same Android applications regardless of which language you use. This course covers most aspects of Android application development, from basics to advanced levels, and all code is written using the Kotlin language starting from the fundamentals. Furthermore, it utilizes Google's latest UI library, Material 3. This course primarily focuses on menus, Android application components, threads, and fragments within the overall Android curriculum, and more in-depth topics will be covered in the next course. Soft Campus will always strive to provide the latest lectures. Thank you.

You can create hybrid casual games using Unity 6.

Learn C# in the Unity environment. Learning C# will help you naturally learn the Unity development environment, which will be very beneficial for beginner developers.

This is the Android 15 App Development Part 1: UI Programming course featuring the latest Material 3 library.

With the latest version in 2024 and a running time of 17 hours and 48 minutes, you can completely conquer Kotlin with this one course.

This course teaches you how to develop front-end web using React. You will learn the core JavaScript grammar for React, CDN-based React development, and NPM-based React development methods.

Using Unity based on the C# language, you will learn how to create a defense game step by step.

"From environment setup to practical business projects, a data science journey to find revenue answers through data (39 lectures in total)." Go beyond simply learning how to use libraries and learn how to cook real-world e-commerce data through the data science process. Starting from development environment setup and Numpy/Pandas basics to data preprocessing, statistical analysis, visualization, and business scenario-based RFM segmentation and hypothesis testing! Systematically master the core competencies of a data scientist through practical projects.

Although the majority of real-world data consists of '2-mode' relationships—such as buyer–product, user–content, and word–document relationships—have you encountered distorted metrics or limitations in data transformation from blindly applying conventional 1-mode analysis methods? We present clear solutions ranging from the core principles of 2-mode networks to data conversion and direct analysis (Direct/Bipartite) approaches, as well as hands-on practice in user profiling and association analysis using NetMiner. Spend less time struggling with data conversion and metric calculation, and take your research and professional insights to the next level with unparalleled 2-mode network analysis capabilities spanning text, bibliographic, and SNS data.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

This course is designed to learn how to collect data through Logpresso and normalize the collected data so that it can be queried. In this course, you will learn how to collect data by setting up a collector, how to parse data mixed in various forms, and how to create a collector by installing an agent on a remote server.

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.

How is processing big data different from processing data with Pandas? Let's learn about Spark, an essential framework for big data processing.

This course explains how to perform data engineering, including real-time processing and external integration, which are the strengths of Logpresso. It also covers in depth how to assign reference data in real time, the precedence of logically related events, and CEP (Complex Event Processing) for detecting various actions and performing follow-up tasks based on collection intervals.

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.

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.

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.

Based on the instructor’s experience of struggling when first learning machine learning, this course has been newly designed—unlike conventional courses—to help you understand machine learning easily and apply it to real-world problems.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

A DBA instructor with 30 years of experience teaches 45 essential SQL tuning techniques that can be immediately applied in practice through real-world examples. Master the practical know-how to make slow queries 10 times faster, from execution plan analysis to index optimization and join tuning!

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

This is a basic Python Excel programming course learned through an xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

In Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.

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!

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.