Data Science Roadmap

This contains a roadmap for students to learn Data Science
# Data Science Roadmap

## Mentors
> Prakhar Srivastava
> Saloni Mehra
> Vishesh Tripathi
> Devansh Singh

The roadmap is divided into 4 chapters that span across the whole tenure of the course. Chapters are required to be completed by the mentor and the student.

Downlaod Anaconda : https://www.anaconda.com/distribution/#download-section
or you may also use any other python IDE

## Chapter 1 `Basic Python Data Structures`
> `Unit 1` Numpy & Scipy
> Useful Links: [Numpy Doc](https://docs.scipy.org/doc/numpy/user/), [Scipy Doc](https://docs.scipy.org/doc/scipy/reference/)
> `Unit 2` Pandas
> Useful Link: [Pandas Video Tutorial](https://www.youtube.com/watch?v=Iqjy9UqKKuo&list=PLQVvvaa0QuDc-3szzjeP6N6b0aDrrKyL-)

## Chapter 2 `Plotting with different libraries`
> `Unit 1` Matplotlib
> Useful Links: [Video Tutorial](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF), [Matplot Doc](https://matplotlib.org/3.0.3/users/index.html)
> `Unit 2` Seaborn
> Useful Link: [Seaborn Doc](https://seaborn.pydata.org/), [Video Tutorial](https://www.youtube.com/watch?v=KvZ2KSxlWBY)

## Chapter 3 `Machine Learning using sklearn`
> `Unit 1` Probabilistic and Statistical Models
> `Unit 2` Machine Learning Models and Algorithms
>Useful Link: [Scikit Learn Doc](https://scikit-learn.org/stable/user_guide.html)

## Chapter 4 `Making Predictions using Keras`
> `Unit 1` Feature Engineering
> `Unit 2` Neural Networks and Deep Learning
> Useful Link: [Kears Doc](https://keras.io/)

## References `Projects/Code samples`
Regression
> `Linear regression` - https://medium.com/@contactsunny/linear-regression-in-python-using-scikit-learn-f0f7b125a204
> `Support Vector Regressor` - https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff
> `also try` - Support Vector Regressor, Extra trees regressor, Decision tree regressor, Random forest regressor, Adaboost regressor

https://scikit-learn.org/stable/modules/clustering.html

Classification
> `Logistic regression` - https://towardsdatascience.com/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a
>`Random forest classifier` - https://chrisalbon.com/machine_learning/trees_and_forests/random_forest_classifier_example/
>`Support Vector Classifier` - https://scikit-learn.org/stable/tutorial/basic/tutorial.html
>`Also try` DecisionTreeClassifier, ExtraTreeClassifier, GaussianProcessClassifier, GradientBoostingClassifier

Clustering
>`K-Means` - https://towardsdatascience.com/k-means-clustering-with-scikit-learn-6b47a369a83c
>`DBSCAN` - https://www.dummies.com/programming/big-data/data-science/how-to-create-an-unsupervised-learning-model-with-dbscan/
>`Also try` - https://scikit-learn.org/stable/modules/clustering.html

Sklearn API reference : https://scikit-learn.org/stable/modules/classes.html