Fundamentals of Data Science + Introduction to Python

Course objectives

To get familiarized with Python as a tool for automated data processing, which involves data analysis and visualization, model building, assessment and validation, prediction, as well as reporting results of the analysis.

Training scope

• Introduction to statistical data analysis
  • Supervised and unsupervised learning
  • Classes and examples of models
  • Methods of model estimation/calibration
  • Principles of statistical hypothesis testing
  • Model validation and selection
  • Overfitting (bias-variance trade-off)
• Data Science techniques that will be implemented:
  • Linear and nonlinear regression models
  • Artificial neural networks
  • Decision trees and random forests
  • Bayesian classifiers
  • Ensemble modelling
  • Confusion matrix, ROC curve
  • Model quality statistics
  • Cross-validation
• Model development with Python:
  • Python as a programming language
  • PyCharm – an IDE for Python
  • Jupyter Notebooks – documents with live code
• Pandas for:
  • Import and export of data
  • Data structures  and data manipulation
• NumPy for linear algebra and numerical computation
• Scikit-learn for:
  • Regression
  • Classification
  • Clustering
  • Model validation and selection

Audience

Data Scientists or anyone who wants to use Python as a tool for automated data analysis. This is an introductory level training. Utilized ML methods and programming techniques will be explained,  but it is assumed that participants have a basic experience with Python.