### Main topics

### What is data science? Linux intro.

### Headlines

We’ll know data science and the road map of these courses.

We’ll know more about useful commands in Linux.

### References

Linux Bash Shell Cheat Sheets

### Details

### 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

TBA

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### Neural networks intro.

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

TBA

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### TensorFlow (I)

RNNs, autoencoders, variational autoencoders, GANs

TBA

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