Online Learning Path

This markdown is a online learning record for myself and part of study notes for the following topics. I will continue updating the record from time to time.

My Background: M.S. Financial Mathematics, B.S. Statistics, M.Eng. Computer Science (ongoing, part time)
Resources: Coursera Udemy Udacity Medium Blooberg Terminal CFA FRM Stanford Online MIT Open Courseware

Table of contents

1. Statistics & Math:

Statistical Learning
Machine Learning Foundation - Math Foundation [Note]
Time Series Analysis [Note]

2. Data Analytic

2.1 MySQL (SQL):

Managing Big Data with MySQL
Create a Python Application using MySQL

2.2 MongoDB (NoSQL):

MongoDB and Python - Quick Start
Create a Python Application using PyMongo and MongoDB Database

2.3 Tableau:

Tableau: Certified Associate Certification

Toys:
Visualizing Citibike Trips with Tableau
Building Candlestick Charts with Tableau

2.4 Excel & VBA:

Unlock Excel VBA and Excel Macros

2.5 Python:

Introduction to Data Science in Python
Python for Everybody Specialization

1. Getting Started with Python
   
2. Python Data Structures
   
3. Using Python to Access Web Data
   
4. Using Database with Python
   
5. Capstone: Retrieving, Processing, and Visualizing Data with Python
   

Python for Time Series Data Analysis

Toys:
COVID19 Data Analysis using Python
Processing Data with Python
Mining Data to Extract and Visualize Insights in Python

2.6 SAS:

3. Data Science

3.1 Data Science Insight:

AI for Everybody [Note]

3.2 Machine Learning:

Machine Learning (Stanford Unv.)
Machine Learning Specialization (Unv. Washington)

1. Machine Learning Foundations: A Case Study Approach
   
2. Machine Learning: Regression
   
3. Machine Learning: Classification
   
4. Machine Learning: Clustering & Retrieval
   

Applied Machine Learning in Python
Python for Data Science and Machine Learning Bootcamp [Note]

Feature Engineering for Machine Learning
Feature Selection for Machine Learning
Deployment of Machine Learning Models

3.3 Deep Learning/Natural Language Processing/Computer Vision:

Deep Learning Specialization

1. Neural Networks and Deep Learning
   
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
   
3. Structuring Machine Learning Projects
   
4. Covolutional Neural Networks
   
5. Sequence Models
   

AI Tensorflow Developer Professional Certificate

1. Introduction to TensorFlow for Artifical Intelligence, Machine Learning, and Deep Learning
   
2. Convolutional Neural Networks in TensorFlow
   
3. Natural Language Processing in TensowFlow
   
4. Sequences, Time Series and Prediction
   

NLP - Natural Language Processing

3.4 Git:

The Data Scientist's Toolbox
Version Control Using Git

3.5 AWS (Cloud):

AWS Fundamentals Specialization

1. AWS Fundamentals: Going Cloud-Native
   
2. AWS Fundamentals: Addressing Security Risk
   
3. AWS Fundamentals: Migrating to the Cloud
   
4. AWS Fundamentals: Building Serverless Applications
   

Getting Started with AWS Machine Learning

3.6 Spark (Big Data):

Spark and Python for Big Data with Pyspark

4. Product

4.1 Product Insight

The Product Life Cycle: A guide from start to finish with google sheet
SWOT Analysis [Note]

4.2 A/B Testing:

A/B Testing
A/B Testing for Business Analysts

5. Computer Science

Computational Thinking for Problem Solving [Note]

5.1 Algorithms:

Algorithmic Toolbox
Algorithms and Data Structrues in Python

5.2 Web Applications

Python and Flask Bootcamp: Create Websites using Flask
Build a Machine Learning Web App with Streamlit and Python

6. Finance

Chartered Financial Analyst
CFA Level1 CFA level2 CFA level3
Financial Risk Manager
FRM level1 FRM level2

6.1 Trading and Financial Analysis:

Algorithms Trading: Backtest, Optimize & Automate in Python
Python and Statistics for Financial Analysis

6.2 Asset Management:

Investment Management with Python and Machine Learning Specialization

1. Introduction to Portfolio Construction and Analysis with Python
   
2. Advanced Portfolio Construction and Analysis with Python
   
3. Python and Machine Learning for Asset Management
   
4. Python and Machine Learning for Asset Management with Alternative Data Sets
   

6.3 Risk:

Credit Risk Modeling in Python
Logistic Regression using SAS - Indepth Predictive Modeling

6.4 C++:

C++ Programming for Financial Engineering (distinction)

6.5 Bloomberg:

Bloomberg Market Concepts (Economic Indicators, Currencies, Fixed Income and Equities)