AI / Data Scientist + Analyst:
https://roadmap.sh/ai-data-scientist

![Warning]! = iffy if i got the concepts to know correct
** = what even is this??? (unknown unknown)
* = not fully conceptualized or have actually used (known unknown)
? = i want to understand the higher/broader concepts better (deeper wisdom or rusty)

Mathematics:
Linear Algebra - Vectors, Matrices, Transformations
Differential Calculus - Derivatives, Integration

Statistics:
Understand all the types of distributions:
https://pbs.twimg.com/media/DIZ4zh6WsAAblfL.jpg
https://pbs.twimg.com/media/GCxJnJmWgAAO0jA?format=jpg&name=large

? Hypothesis Testing - Type 1/2 Errors, Random Vars, 1 vs. 2 sample tests.
? Probability Theory - Bayes, Probability Space, CLT
Sampling Methods - SRS, Stratified, Cluster
* Metrics - Feature Engineering, Measurement

![Warning]!
Econometrics:
SARIMA - AR, I, MA
? General Time Series - Autocorr, Seasonality
** More complex econometrics

Exploratory Data Analysis:
Understand all these types of graphs:
https://python-graph-gallery.com/

Pandas, Matplot, Seaborn - Advanced DF Manipulation, Plot Elements, Multiples
Scikit-Learn - Training, Model Creation, Train/Test

Machine Learning:
Understand all these types of algorithms:
https://images.squarespace-cdn.com/content/v1/58dc1a1ee4fcb51cbb80a096/24c2f0fb-e667-42d6-a6fb-b2c4ad79ed41/ML-CheatSheet-en.png
https://scikit-learn.org/1.3/tutorial/machine_learning_map/index.html

? Supervised - Linear, Logistic, NNs
? Unsupervised - Clustering, Dimensionality Reduction
? Model Evaluation - Confusion Matrix, ROC Curve, Model Metrics

Parts of the Roadmap Left (In-Order):
* Deep Learning
** MLOps

To be a better Data Scientist I need to focus on:
Formalizing Broader Concepts of ML
Learning Econometrics / Time-series ML
Using MLOps Platforms
Experimenting with Deep Learning

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END, *'s still need to be removed before roadmap check-off