Data Science and AI

Data Analysis, Machine Learning, Mathematics, Statistics
- Data Analysis:
  - Data Processing
  - Data Cleaning
  - Data Scraping
  - Data Modeling
  - Data Visualization
  - Finding Insights
  - Making Conclusions + Recommendations
- Machine Learning and Deep Learning with (un)structured data
  - Computer Vision
  - Natural Language Processing
  - Reinforcement Learning
  - Transfer Learning
  - Multiple class Classification
- Statistics
  - Linear Regression
  - Logistic Regression
  - Probability
  - Distributions
  - Maximum Likelihood Estimation
  - Transformations
  - Bootstrapping
  - Data Generation Processes
  - Bayesian Methods and Inference
  - Time Series Analysis
Programming
- Python, R, SAS, Stata, Java, etc.
  - Python Libraries:
    - TensorFlow, Keras, PyTorch, sklearn, tkinter, numpy, pandas, matplotlib, BeautifulSoup, seaborn, etc.
  - R libraries:
    - tidyverse, ggplot2, dplyr, shiny, knitr, lubridate
Economics
- Game Theory, Experimental Design, Price Discrimination.