Practical Data Science Part 2. Data Preprocessing

Contains only the essentials!
Essential data preprocessing for data analysis

Big data analytics, machine learning, deep learning, artificial intelligence, and digital transformation (DT) are among the most in-demand technology fields today. In nearly every industry, training data scientists to handle these technologies is crucial and urgent.

Data preprocessing is the task that requires the most time from data handlers in companies and has the greatest impact on data analysis (machine learning) performance.

📝 Core data preprocessing

This lecture covers effective data exploration (EDA) methods and the four key concepts of data preprocessing: data cleaning, scaling, outlier handling, and data transformation.

👩‍💻 Theory + Practice Lecture Structure

Predicting Titanic Survivors?

We help you immediately apply data analysis required in the field through theory-based exercises such as missing value handling, data transformation, and linear classification prediction.

🙋‍♂️ Topics needed on site

_{Handling tables}

_{Time series data processing}

In practice, combining table-structured data in various ways is often necessary. Understand the differences between the concat, append, join, merge, groupby, and pivot_table functions, and explain which functions are useful in which situations.

In practice, we often deal with unstructured time series data. We'll explain how to use datetime and the sequential walk-forward time series prediction method, and introduce binary classification and regression prediction models using linear models.

📕 Course Features

• All content is explained with practice code.

Go to the practice code 👉 https://github.com/data-labs/preprocessing

• The example code is structured so that you can use it right away in your work.
• The code is concise, yet contains the essentials and is written to be easy to use.

👩‍💻 Core Data Science

 

Practical Data Science Part 1: Introduction to Python

Python, the foundational language of data science.
This course is designed to provide basic knowledge of Python.
For those who do not have basic knowledge of the Python language,

Practical Data Science Part 1. Through an introductory Python lecture.
I recommend learning player knowledge.