About the class

💻 This course is Practical know-how on big data development and analysis from current data scientist expertsIt is a systematic curriculum that includes You can understand the overall flow of big data system construction and analysis from A to Z. You can also acquire relevant know-how by learning about projects from big data collection to loading, processing, and analysis.

❶ Big data collection and loading

You can design a storage structure for collected and transformed data to suit business purposes, taking into account data types and analysis purposes.

❷ Inferential statistics

When it comes to data analysis, if you learn the basic concepts of statistics, you can perform more in-depth analysis. You can understand the concepts of population and samples, sampling methods, and types of scales. You can then understand random variables and the hypothesis testing process. You can also learn about correlation analysis and regression analysis.

❸ visualizing

Data mining-based data analysis can be implemented and the entire process can be managed according to business goals, strategies, and policies. You can also understand the differences between statistics-based data analysis and machine learning-based data analysis and the purpose of use, and determine the need to apply machine learning techniques according to the purpose of use. Depending on the problem to be solved, machine learning techniques suitable for explaining and patterning data structures can be selected and application procedures can be planned. Based on the purpose to be analyzed and the characteristics of the data set, it is possible to determine the criteria for dividing the training data set and the test data set for applying machine learning techniques.

Appropriate machine learning techniques can actually be applied for accurate classification or predictive modeling according to the established analysis plan. When a continuous objective variable (or response variable) is given, various numerical prediction models can be compared and the optimal numerical prediction model can be selected and applied to solve the problem. Various clustering techniques can be applied, and the optimal clustering technique can be selected and applied.

❹ data analysis

Explain the concepts and features of machine learning and the importance of space and data. It also explains the basics of image processing and the main challenges of machine learning, and practices machine learning classification and prediction algorithms, and deep learning algorithms for image and text processing.

Course effect

• Get insights into technologies and products related to big data collection.
• You can derive data requirements and identify classifications and characteristics by type.
• As a working data scientist, you can learn how to collect and load data.
• You can learn various machine learning theories and practices at the same time.

Recommended target

• Those who want to work in the field of data analysis and related fields
• Those who aim to obtain ADP qualifications and big data analysts
• Those who want to change jobs to big data development and analysis work within the company