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.

Live, instructor-led online and on-site training

We appreciate that you need flexibility to fit in with new working situations - whether you're an individual, part of a distributed team, or simply have projects and deadlines to meet.

Our remote training can take place online in a virtual classroom, with content split into modules to accommodate your scheduling challenges and meet your learning goals. Get in touch today to find out how we can help design a cost-effective, flexible training solution.

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 Time Series data

Databases:

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

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