빅데이터 분석기사 시험 실기(Python)

In the [Beginner's] Data Visualization with QGIS course, you will learn about basic spatial analysis methods using the QGIS program and spatial data.

Let's use the Folium library and Google Cloud API provided by default in Python. You can perform spatial data analysis based on coding without a separate tool.

Analyze in an environment like Excel and automatically create Python code. This lecture will help you understand data analysis by comparing it to Pandas and Seaborn.

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

In the [Beginner's] Data Visualization with Power BI course, you will learn how to edit data using the query editor, visualize data using various tools, and check and share the analyzed results.

This is a basic course on machine learning and deep learning analysis for data analysis beginners.

We will help those preparing for the Big Data Analysis Engineer Practical Exam with R to prepare for the exam. We will select and inform you of only the essential parts based on the questions that were on the Big Data Analysis Engineer Practical Exam. If you complete the course, you will be one step closer to passing the Big Data Analysis Engineer Practical Exam. We have reviewed the recent exam questions and uploaded related scripts and data. Also check out the latest exam information.

The Certified Big Data Analyst exam is a nationally recognized certification administered twice a year. This course provides detailed lectures covering everything from the basics to the core concepts to help you prepare for the practical exam. The exam difficulty increases with each iteration. To address this, we provide a range of essential content, starting with the fundamentals, to improve your chances of passing.

Many applicants want to solve past questions. Why is that? Because the question setter refers to past questions. Solving the latest past questions is the shortcut to success.

The Big Data Analyst Engineer exam is a national certification held twice yearly. For those preparing, we extensively covered core topics from the basics. With each session, exam difficulty rises. To this end, we provided essential content from basics to boost passing rates.

I will guide non-majors and beginners to quickly pass the Big Data Analysis Engineer practical exam! Theory lightly, practice thoroughly. Even without complex background knowledge, focusing on past questions, we intensively study only the points sure to appear on the exam.

This lecture aims to systematically learn the essential core concepts and functions absolutely necessary for the Business Information Visualization Practical Exam (Tableau), based on the exam guidelines of the organizing institution. It is structured around practical exercises so that you can freely use the functions frequently tested in the actual exam, and through solving mock exam problems of types A and B, you can improve your understanding of the exam types and your problem-solving skills together. Lecture plan and materials can be checked at https://github.com/hansikyung/Tableau_OneTime.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

Through this lecture, you can learn about understanding SQL and databases, SQL syntax, database design for practical application, database utilization through programming, and even data analysis application cases.

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

Those who have learned the basics of Python will learn how to utilize Python after watching this lecture.

This is a basic course on Python Excel programming that you can learn with the xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

Learn NoSQL technology, which can handle Big Data, one of the fundamental technologies in full-stack and data science utilized by modern startups. MongoDB is the easiest and quickest technology to utilize among NoSQL databases. In this lecture, you will learn the basics of MongoDB in a short time, and also acquire the skills to manipulate and utilize MongoDB with Python.

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.

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.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

Students who want to learn Hadoop and Big Data will celebrate the amazing advancements in experiencing the world of Big Data through this course!

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.

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>.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

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!