Name: Preparing for Data Analysis
Price: -1 USD

Preparing for Data Analysis

Overview

Refine your approach to data preparation. Dive into reprocessing techniques, feature engineering strategies, and exploratory analysis with Pandas, matplotlib, and Plotly to uncover insights and enhance machine learning outcomes.

Syllabus

An Overview of Machine Learning Pipelines

We'll define the steps of the machine learning pipeline, from data ingestion to production. We'll emphasize preprocessing and feature engineering, essential steps to well-performing trading models.

Data Acquisition and Preprocessing

How do you get data from the Internet to your model? We'll talk about ingestion, transformation,and data wrangling using Pandas, the industry-standard time-series package for data manipulation.

Feature Engineering for Trading Models

Feature engineering significantly improves model performance, and in this lesson, we'll go over strategies you can use. Basic and more involved techniques will be presented, with use cases noted.

Exploratory Data Analysis

What's your data trying to tell you? Use EDA, exploratory data analysis, to find out! We'll use Python's two most popular plotting packages, matplotlib and Plotly, to find out your data's secrets.