Main topics

What is data science? Linux intro.


We’ll know data science and the road map of these courses.
We’ll know more about useful commands in Linux.


Linux Bash Shell Cheat Sheets


Python (I): basics

We need to learn a programming language. Python is our recommendation.
We’ll begin to work with python.

Any python tutorial is fine!

Python (II): IDEs, package managers, Anaconda, pandas, simple

Python interactive development environments.
We’ll know about Anaconda and pandas.
We’ll learn about simple statistical analysis on some real data.

Probability and statistics (I)

Statistical inference, significant difference, CL estimation, developing and evaluating hypotheses, probability, best fitting, correlations

Probability and statistics (II), Symbolic programming

preliminary statistical analysis to mine a data set, How programming languages are able to deal with analytical mathematics

Intro. to signal/image processing

Bayesian inference (from session 5), how we are able to analyze sound/image/video

Machine learning intro. (I)

OOP, ML concepts, features, classification, regression, overfitting problem, regulators

Machine learning intro. (II), Project development

Cross validation, hyper parameter optimization, how we are able to how to manage and track progress of a project. What will happen after project delivery?

The art of data visualization and presentation

You’ll learn about data visualization and presentation importance and related techniques.

Search about matplotlib, seaborn, bokeh, plotly and dash.

Neural networks

Perceptron, CNN, GD, SGD



Neural networks intro.

Basics; train, save, restore, tune up a model



TensorFlow (I)

RNNs, autoencoders, variational autoencoders, GANs