[Stock Markets Analytics Zoomcamp] Module2 "Working with Data in Pandas"

Links:
- Course Github: github.com/DataTalksClub/stoc...
- Registration Form: docs.google.com/forms/d/e/1FA...
- PythonInvest Website: pythoninvest.com/
- (support PythonInvest): buymeacoffee.com/pythoninvest
Time Codes:
[0:11] Speaker Intro
[0:40] Agenda
[3:12] System Design and Goals
[4:11] Today's Module Output: Large Pandas DataFrame
[4:31] Q&A: Module 1 and Homework 1
[17:49] Introduction to Processing Data from Web Pages
[28:08] Time Series Data Type and Transformations
[35:29] Macro Indicators Introduction
[40:42] New Dataset on European Union Data
[43:20] Fundamental Indicators Challenges
[47:18] Creating a Unified Dataset with Different Time Series
[1:00:03] Predicting Growth Rates and the Importance of Relative Rates
[1:03:22] Introduction to Technical Indicators
[1:10:10] Final Steps to Create a Unified Dataset
[1:19:08] Recap
Module 2 "Working with Data in Pandas":
The Core Libraries for Data Analysis in Python
- Explore the core libraries: Numpy, Pandas, and Matplotlib, Seaborn, Plotly Express.
Understanding Data Types and Manipulation
- Delve into various data types: numeric, string, and date categories
- Generating dummy variables
Enhancing Datasets with Feature Generation Techniques
- Derive additional features such as hour/day of the week, growth over different periods.
- Technical indicators using the TaLib library.
- Generating future growth (what we want to predict)
Effective Data Cleaning Strategies
- Learn strategies for cleaning and preparing data for analysis.
- Acquire skills in joining multiple datasets for a holistic view.
Thorough Descriptive Analysis
- Descriptive analysis of the dataset
- Explore correlations within the data

[@vincentmorara2102]

Insigtful presentation