Data Science with Python Training Course

Who should attend

Analysts, Data Scientists, and software developers who want to get a practical introduction to Data Science and Machine Learning with Python.

Prerequisites

Delegates should ideally have some prior experience programming and / or of using statistical analysis techniques. Any experience with Python would be beneficial but we will give you "just enough" Python pointers to be able to create real-world workable solutions to data analysis, Machine Learning and visualisation challenges.

On-site

We would be happy to discuss custom / on-site Python Data Science training for any size of team. We can take into account your existing technical skills, project requirements and timeframes, and specific topics of interest to tailor the most relevant and focussed course for you.

This can be particularly useful if you need to learn just the new features and Python programming Best Practices, or need to include extra topics to help with pre-requisite skills. If you would like to dicuss your custom training requirements, please get in touch.

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running code in Jupyter notebook

Data Analysis with Python:

Python core concepts

• Core data types in Python
• Control flow statements
• Defining and using custom functions
• The Python standard library
• Working with data:
  • Iteration and list comprehensions
  • Accessing raw data on file (CSV, JSON, ...)
  • Working with dates and times
• Basics of Object-Oriented Programming in Python

Python Data Science libraries

Numpy:

• Working with NumPy arrays
• Essential operations with NumPy arrays
• Stats and linear algebra with NumPy

pandas:

• Working with table-like data in pandas
• Essential operations with Series and DataFrame object
• Loading data from file into DataFrame objects
• Summary statistics over DataFrame objects
• Data aggregation queries (groupby() method)
• Exploratory analysis of new datasets
• Data visualisation over DataFrames
• Join/merge operations with DataFrames
• Working with text data in DataFrames

• Working with relational databases in Python
• Overview on SQLAlchemy for database interaction
• Integration of pandas and SQL

Miscellanea

• Python packaging: using and creating custom libraries
• Unit testing: tools to perform unit testing in Python
• Interaction with web services

Machine Learning with Python

Overview of Machine Learning

• What is Artificial Intelligence? What's up with the hype?
• Data Science vs. Data Mining vs. Machine Learning
• Machine Learning Problems and Applications
• Python Environment Set-up
  • The Anaconda Python distribution
  • Jupyter Notebooks
  • Python Ecosystem for Data Science and Machine Learning

Machine Learning Overview

• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting
• Supervised Learning Problems
  • Regression: predicting a quantity
    • Algorithm in depth: Linear Regression and Polynomial Regression
      
      
  • Classification: predicting a label
    • Algorithm in depth: k-Nearest Neighbours
    • Algorithm in depth: Support Vector Machine
    • Algorithm in depth: Naive Bayes
• Unsupervised Learning Problems
  • Clustering: grouping similar items
    • Algorithm in depth: k-Means
    • Algorithm in depth: Hierarchical Agglomerative Clustering
    • Algorithm in depth: DBSCAN
  • Dimensionality Reduction
    • Algorithm in depth: Principal Component Analysis
• Evaluation of Machine Learning algorithms

Deep Learning & Neural Network Overview

• Intro to Artificial Neural Networks
• Mathematical Concepts required by Deep Learning
• Neural Network concepts
  • Neural Network Types
  • Gradient Descend
  • Back-propagation
  • Activation Functions
  • Loss Functions
  • Hyper-parameters
• Deep Network Architectures
• Deep Learning Libraries