# Complete Roadmap For Data Scientist

## Complete Roadmap For Data Scientist

### Mathematics :

* Linear Algebra
* Analytics Geometry
* Matrix
* Vector Calculus
* Optimization
* Regression
Dimensionality Reduction
Density Estimation
Classification

### Probability :

* Discrete Distribution
- Binomial
Bernoulli
- Geometric etc.
* Continuous Distribution
- Uniform
- Exponential
- Gamma
* Normal Distribution

* Introduction to Probability
* 1D Random Variable
*  Function of One Random Variable
* Joint Probability Distribution

### Statistics :

* Introduction to Statistics
* Data Description
* Random Samples
* Sampling Distribution
* Parameter Estimation
* Hypotheses Testing
* ANOVA
* Reliability Engineering
* Stochastic Process
* Computer Simulation
* Design of Experiments
* Simple Linear Experiments
* Correlation
* Multiple Regression
* Nonparametric Statistics
* Statistical Quality Control
* Basic of Graphs

### * Python Basics :-

- List
- Set
- Tuples
- Dictionary
- Function
* Numpy
* Pandas
* Matplotlib/Seaborn

* DataBase :-
- SQL
- MongoDB

* R Basics :-
- List
- Data Frame
- Matrix
- Array
* dplyr
* ggplot2
* Tidyr
* Shiny

Other :-
- Data Structure
- Web Scraping
- Linux
- Git

### * Machine Learning :

#### - Classification :-

1. Support Vector Machine
2. Discriminant Analysis
3. Naive Bayes
4. Nearest Neighbor
5. Neural Networks

#### - Regression :-

1. Linear Regression, GLM
2. SVR, GPR
3. Ensemble Methods
4. Decision Trees
5. Neural Networks

#### * Unsupervised Learning :- Clustering :-

1. K-Means, K-Medoids, Fuzzy C - Means.
2. Hierarchical
3. Gaussian Mixture
4. Hidden Markov Model
5. Neural Networks

### Deep Learning :

- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Keras
- PyTorch
- TensorFlow

### Natural Language Processing :

- Text Classification
- Word Vectors

- Tableau
- Power BI

### Deployment :

- Microsoft Azure
- Heroku
- Django

### Description:

In the above topics you can see the complete road map to become Data Scientist. In each and every topic we have defined the sub - topics, according to that topics you can study from different sources available on the Internet. A part from mathematics part if we talk about technical part firstly you have to learn python with all the above mention libraries, then come to the machine learning topics and read each and every algorithm with theory concepts as well as practical, understand the working of each and every algorithm and implement it by practical. Then same process you have to follow for Deep Learning algorithms. Then come to the next part which is NLP, NLP is used for automatic manipulation of Natural Language, like speech and text. so after covering NLP we will move on to next point which is Data Visualization there are two important tools which is Power BI and Tableau.
you can learn any one of them. Then last one is Deployment phase in which you can select any one of
1. 