Important Topics to become a data scientist[Advanced Level]
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1. MathematicsLinear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification
2. Probability Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution
3. Statistics Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression
4. Programming Python:Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn
R Programming:R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny
DataBase:
SQL
MongoDB
Data Structures
Web scraping
Linux
Git
5. Machine Learning How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage
6. Deep Learning Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification
7. Feature EngineeringBaseline Model
Categorical Encodings
Feature Generation
Feature Selection
8. Natural Language Processing Text Classification
Word Vectors
9. Data Visualization Tools BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense
10. Deployment Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django
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