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Training

Our Training on Data Analysis are split in Modules and Sub-modules.

To help you to make a meaningful training program we pre-compiled some themes with specific modules: you can start from that solutions and modify it to build the scheduling right for you.

Python for science and engineering
34 hours
Data Analysis and Signal Processing 12 hours
Python Practical Programming 13 hours
Math & HPC 9 hours
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Python for data analysis
26 hours
Data Analysis and Signal Processing 19 hours
Python Practical Programming 7 hours
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Machine learning (require python)
30 hours
Data Analysis and Signal Processing 4 hours
Math & HPC 3 hours
Machine Learning 23 hours
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Python advanced programming
24 hours
Python Practical Programming 27 hours
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The scheduling and calendar of the courses can be customized as well. Usually we suggest to have no more than one or two days per week dedicated to the training. In this way every participant can do exercises in the spare time to fix the concepts learned with the instructor.

The number of participants is unlimited: for a fixed price you can invite as many people as you want. Nevertheless we advice to not exceed 10 attendees per trainer to have a good level of interaction with the instructor. If you need to train bigger groups you can require additional trainer assistants to help the participants with exercises and questions.

We also give the opportunity to completely free to design their custom programs by selecting any module:

Data Analysis and Signal Processing

Basic Data Operativity

4 hours

– Organization and indexing of 2D and 3D data matrices
– Data query by index or conditional expression
– Duplicated and Missing data
– Advanced indexing, hierarchical indexing

Data Input/Output

4 hours

– File I/O: work with data in different formats
– CSV and text files
– MATLAB®
– Excel spreadsheets
– HTML
– JSON
– SQL and databases
– HDF5

Time Series

4 hours

– Time Series
– Regular sampled and Irregular sampled Time ranges
– Advanced indexing for Time Series
– Frequency conversions, Upsampling, Downsampling

Statistical Tools

1 hours

– Linear Models
– Nearest Neighbors
– Gaussian Processes
– Decision Trees

Data Organization

1 hours

– Merging and Joining different data sources
– Reshaping Data Structures – pivot tables

Advanced Data Management

1 hours

– Apply functions to bulk data
– Grouping Data

Open Data

1 hours

– Sources for Open Data

BIG Data

3 hours

– Working with Terabyte-sized Data with PyTables
– Memory-mapped data on disk
– Interface to SQL databases, NoSQL, Amazon S3

Python Practical Programming

Introduction

1 hours

– Introduction to the IPython Notebook environment.

Python Basics

6 hours

– Objects and Variables
– Scripts, Modules and Namespaces
– Strings
– Lists, Sets and Tuples
– Dictionaries
– Control Structures

Effective Programming

3 hours

– Functions, External functions and Private Methods
– Scoping Rules and Documentation
– Modules and Libraries
– File IO
– Working with the Operating System
– COM Extensions

Style Guides

3 hours

– PEP8
– How to manage data
– Coding styles examples

Object Oriented

4 hours

– OOP in Python
– Methods and attributes
– Inheritance and Memory Management
– Override
– Duck-typing and overloading

Speed-Up with LLVM & C

4 hours

– Cython and Weave
– LLVM compilers
– Using External C Libraries with ctypes
– Embedding Python in C code

Unicode

2 hours

– Unicode

Regular Expressions

2 hours

– Regular Expressions

Managing Exceptions

2 hours

– Handling and Raising Exceptions
– User-defined Exceptions

Math & HPC

Numpy Basics

3 hours

– Linear Algebra, vectors, n-dimensional matrices
– Basic Plotting
– IO of structured data

HPC

2 hours

– Parallelization on Multi-Core CPU
– Parallelization on GPU
– Cloud Computing – 1000’s cores in 2 lines of code

Signal Processing

2 hours

– Fast Fourier Transforms
– Spectral Analysis and Filtering

Advanced 2D Graphics

2 hours

– Plot 2D
– Subplots and fine Plotting optimization

Machine Learning

ML Overview

3 hours

– What is Machine Learning?
– Supervised and Unsupervised Learning
– Regression to Predict a numerical Value
– Dimentionality Reduction
– Classification
– Clustering
– How do I choose what to do?

Data Preparation

3 hours

– Preprocessing Data
– Testing with Datasets & Genarators
– Feature extraction
– Numerical Features
– Categorical Features
– Derived Features

Dealing with Overfitting

4 hours

– Dealing with Bias and Variance
– Overfitting and Regularization

Basic Supervised Methods

2 hours

– Linear Models
– Nearest Neighbors
– Gaussian Processes
– Decision Trees

Unsupervised Learning

3 hours

– Gaussian Mixture Models
– Principal Component Analysis
– Independent Component Analysis
– Manifold Learning
– Clustering
– Outliers Detectors
– Hidden Markov Models

Validation & Testing

4 hours

– Measuring Prediction Performances
– Validation and Testing
– Model Selection and Assesment

Advanced Algorithms

4 hours

– In Depth with Support Vector Machines (SVMs)
– In Depth with Random Forests
– Deep Neural Networks – An Overview
– Future Developments – An Overview