Fully Customizable Trainings - Unlimited Participants
  • Our Training on Data Analysis are split in Modules and Sub-modules.
  • We leave the customers completely free to design their custom programs by selecting any module.
  • 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.
  • 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.


Hours selected: 0
Python for science and engineering 34 hours
Python for data analysis 26 hours
Machine learning (require python) 30 hours
Python advanced programming 27 hours
Custom, you select the modules you want

Data Analysis and Signal Processing

Submodule Description
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
Excel spreadsheets
SQL and databases
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 hour
Time Series
Regular sampled and Irregular sampled Time ranges
Advanced indexing for Time Series
Frequency conversions, Upsampling, Downsampling
Data Organization
1 hour
Merging and Joining different data sources
Reshaping Data Structures - pivot tables
Advanced Data Management
1 hour
Apply functions to bulk data
Grouping Data
Open Data
1 hour
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

Submodule Description
1 hour
Introduction to the IPython Notebook environment.
Python Basics
6 hours
Objects and Variables
Scripts, Modules and Namespaces
Lists, Sets and Tuples
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
How to manage data
Coding styles examples
Object Oriented
4 hours
OOP in Python
Methods and attributes
Inheritance and Memory Management
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
2 hours
Regular Expressions
2 hours
Regular Expressions
Managing Exceptions
2 hours
Handling and Raising Exceptions
User-defined Exceptions

Math & HPC

Submodule Description
Numpy Basics
3 hours
Linear Algebra, vectors, n-dimensional matrices
Basic Plotting
IO of structured data
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

Submodule Description
ML Overview
3 hours
What is Machine Learning?
Supervised and Unsupervised Learning
Regression to Predict a numerical Value
Dimentionality Reduction
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
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
Hours selected: 0
Training Quote
Hours selected: 0

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